Transmission of antibiotic resistance genes in agroecosystems : an overview

The use of antibiotics in human medicine and animal husbandry has resulted in the continuous release of antibiotics into the environment, which imposes high selection pressure on bacteria to develop antibiotic resistance. The spread and aggregation of antibiotic resistance genes (ARGs) in multidrug-resistant pathogens is one of the most intractable clinical challenges. Numerous studies have been conducted to profile the patterns of ARGs in agricultural ecosystems, as this is closely related to human health and wellbeing. This paper provides an overview of the transmission of ARGs in agricultural ecosystems resulting from the application of animal manures and other organic amendments. The future need to control and mitigate the spread of antibiotic resistance in agricultural ecosystems is also discussed, particularly from a holistic perspective, and requires multiple sector efforts to translate fundamental knowledge into effective strategies

Effect of Soil Moisture Status and Animal Treading on N\u3csub\u3e2\u3c/sub\u3eO Emissions and the Effectiveness of a Nitrification Inhibitor Mitigation Technology

Nitrous oxide (N2O) is a potent greenhouse gas with a long-term global warming potential about 298 times that of carbon dioxide (CO2). In grazed grassland, most of the N2O is emitted from nitrogen (N) excreted by the grazing animal, particularly in the animal urine. When the soil is wet, such as that in winter grazing conditions, animal grazing can cause soil structural damage, leading to soil compaction. The combination of a wet soil plus soil compaction is particularly conducive for N2O production. A nitrification inhibitor technology using dicyandiamide (DCD) has been developed to reduce N2O emissions from grazed grassland (Di and Cameron 2002; 2003). However, the efficacy of this technology under wet and compact soil conditions has not been well studied. The objectives of this study were to determine: (1) The impact of soil moisture content on the abundance of ammonia oxidizers and N2O emissions; (2) the impact of animal treading on N2O emissions; and (3) The effectiveness of the nitrification inhibitor DCD in reducing N2O emissions, as affected by soil moisture status and animal treading

Geothermometry and geobarometry of overpressured lower Paleozoic gas shales in the Jiaoshiba field, Central China: insight from fluid inclusions in fracture cements

The Wufeng-Longmaxi organic-rich shales host the largest shale gas fields of China. This study examines sealed fractures within core samples of the Wufeng-Longmaxi shales in the Jiaoshiba shale gas field in order to understand the development of overpressures (in terms of magnitude, timing and burial) in Wufeng-Longmaxi shales and thus the causes of present-day overpressure in these Paleozoic shale formations as well as in all gas shales. Quartz and calcite fracture cements from the Wufeng-Longmaxi shale intervals in four wells at depth intervals between 2253.89 m and 3046.60 m were investigated, and the fluid composition, temperature, and pressure during natural fracture cementation determined using an integrated approach consisting of petrography, Raman spectroscopy and microthermometry. Many crystals in fracture cements were found to contain methane inclusions only, and aqueous two-phase inclusions were consistently observed alongside methane inclusions in all cement samples, indicating that fluid inclusions trapped during fracture cementation are saturated with a methane hydrocarbon fluid. Homogenization temperatures of methane-saturated aqueous inclusions provide trends in trapping temperatures that Th values concentrate in the range of 198.5 °C–229.9 °C, 196.2 °C-221.7 °C for quartz and calcite, respectively. Pore-fluid pressures of 91.8–139.4 MPa for methane inclusions, calculated using the Raman shift of C-H symmetric stretching (v1) band of methane and equations of state for supercritical methane, indicate fluid inclusions trapped at near-lithostatic pressures. High trapping temperature and overpressure conditions in fluid inclusions represent a state of temperature and overpressure of Wufeng-Longmaxi shales at maximum burial and the early stage of the Yanshanian uplift, which can provide a key evidence for understanding the formation and evolution of overpressure. Our results demonstrate that the main cause of present-day overpressure in shale gas deposits is actually the preservation of moderate-high overpressure developed as a result of gas generation at maximum burial depths

Machine learning for feedback in massive open online courses

© 2016 Dr. Jiazhen HeMassive Open Online Courses (MOOCs) have received widespread attention for their potential to scale higher education, with multiple platforms such as Coursera, edX and Udacity recently appearing. Online courses from elite universities around the world are offered for free, so that anyone with internet access can learn anywhere. Enormous enrolments and diversity of students have been widely observed in MOOCs. Despite their popularity, MOOCs are limited in reaching their full potential by a number of issues. One of the major problems is the notoriously low completion rates. A number of studies have focused on identifying the factors leading to this problem. One of the factors is the lack of interactivity and support. There is broad agreement in the literature that interaction and communication play an important role in improving student learning. It has been indicated that interaction in MOOCs helps students ease their feelings of isolation and frustration, develop their own knowledge, and improve learning experience. A natural way of improving interactivity is providing feedback to students on their progress and problems. MOOCs give rise to vast amounts of student engagement data, bringing opportunities to gain insights into student learning and provide feedback. This thesis focuses on applying and designing new machine learning algorithms to assist instructors in providing student feedback. In particular, we investigate three main themes: i) identifying at-risk students not completing courses as a step towards timely intervention; ii) exploring the suitability of using automatically discovered forum topics as instruments for modelling students' ability; iii) similarity search in heterogeneous information networks. The first theme can be helpful for assisting instructors to design interventions for at-risk students to improve retention. The second theme is inspired by recent research on measurement of student learning in education research communities. Educators explore the suitability of using latent complex patterns of engagement instead of traditional visible assessment tools (e.g. quizzes and assignments), to measure a hypothesised distinctive and complex learning skill of promoting learning in MOOCs. This process is often human-intensive and time-consuming. Inspired by this research, together with the importance of MOOC discussion forums for understanding student learning and providing feedback, we investigate whether students' participation across forum discussion topics can indicate their academic ability. The third theme is a generic study of utilising the rich semantic information in heterogeneous information networks to help find similar objects. MOOCs contain diverse and complex student engagement data, which is a typical example of heterogeneous information networks, and so could benefit from this study. We make the following contributions for solving the above problems. Firstly, we propose transfer learning algorithms based on regularised logistic regression, to identify students who are at risk of not completing courses weekly. Predicted probabilities with well-calibrated and smoothed properties can not only be used for the identification of at-risk students but also for subsequent interventions. We envision an intervention that presents probability of success/failure to borderline students with the hypothesis that they can be motivated by being classified as "nearly there". Secondly, we combine topic models with measurement models to discover topics from students' online forum postings. The topics are enforced to fit measurement models as statistical evidence of instruments for measuring student ability. In particular, we focus on two measurement models, the Guttman scale and the Rasch model. To the best our knowledge, this is the first study to explore the suitability of using discovered topics from MOOC forum content as instruments for measuring student ability, by combining topic models with psychometric measurement models in this way. Furthermore, these scaled topics imply a range of difficulty levels, which can be useful for monitoring the health of a course and refining curricula, student assessment, and providing personalised feedback based on student ability levels and topic difficulty levels. Thirdly, we extend an existing meta path-based similarity measure by incorporating transitive similarity and temporal dynamics in heterogeneous information networks, evaluated using the DBLP bibliographic network. The proposed similarity measure might apply to MOOC settings to find similar students or threads, or thread recommendation in MOOC forums, by modelling student interactions in MOOC forums as a heterogeneous information network

Harnessing microbiome-based biotechnologies for sustainable mitigation of nitrous oxide emissions

Achieving the Sustainable Development Goal of climate change mitigation within this century will require adoption of new innovative technologies to control emissions of nitrous oxide (N2O), an important greenhouse gas leading to global warming. This is particularly important in the face of growing fertilizer consumption and continuous land degradation. Currently used tools to mitigate N2O emissions are based on agrochemical inputs and agronomic practices. Emerging technologies include plant breeding approaches to manipulate microbiome activities in agro-ecosystems, and microbial biotechnology approaches for in situ microbiome manipulation and engineering via use of biochemical, cellular and genome-editing methods. This article assessed the likely contribution of microbial biotechnology to the mitigation of N2O emissions and discussed how to facilitate the development of environmental-friendly microbiome-based biotechnology for sustainable climate change mitigation

Harnessing microbiome‐based biotechnologies for sustainable mitigation of nitrous oxide emissions

Achieving the Sustainable Development Goal of climate change mitigation within this century will require adoption of new innovative technologies to control emissions of nitrous oxide (N2O), an important greenhouse gas leading to global warming. This is particularly important in the face of growing fertilizer consumption and continuous land degradation. Currently used tools to mitigate N2O emissions are based on agrochemical inputs and agronomic practices. Emerging technologies include plant breeding approaches to manipulate microbiome activities in agro-ecosystems, and microbial biotechnology approaches for in situ microbiome manipulation and engineering via use of biochemical, cellular and genome-editing methods. This article assessed the likely contribution of microbial biotechnology to the mitigation of N2O emissions and discussed how to facilitate the development of environmental-friendly microbiome-based biotechnology for sustainable climate change mitigation