Greenhouse gas emissions and the technical efficiency of dairy farmers

Agriculture in Scotland is facing immense challenges for the future. Scottish policy now promotes sustainable economic growth, which to many implies both a growth in resource use and the requirement to meet targets for reducing greenhouse gas emissions. Attempts to increase economic gains can be in conflict with environmental aims and the question arises how both objectives can be met simultaneously.This paper examines the relationship between the technical and environmental efficiency of dairy farms in Scotland with respect to greenhouse gas emissions. It estimates the technical efficiency of a sample of dairy farms based on survey data, applying a number of approaches based on the non-parametric Data Envelopment Analysis (DEA) method. The farms’ greenhouse gas emissions are calculated using a variety of sources and were used to estimate environmental efficiency. This paper finds that, within the study sample, farms which are more technically efficient, are bigger or have higher yields are also more efficient in their emissions of GHG emissions.These results suggest that the potential exists for, Scottish dairying to improve its competitiveness and lower greenhouse gas emissions by increasing efficiency, though the extent of the environmental benefits achieved largely depends on how efficiency is increased.     

Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a ‘Big Data’ approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence‐only or presence–absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi‐source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter‐ or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi‐source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA‐based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals.     

Multimedia information retrieval and environmental monitoring: Shared perspectives on data fusion

Computer-based remote monitoring of our environment is increasingly based on combining data derived from in-situ-sensors with data derived from remote sources, such as satellite images or CCTV. In such deployments it is necessary to continuously monitor the accuracy of each of the sensor data streams so that we can account for sudden failures of sensors, or errors due to calibration drive or biofouling. In multimedia information retrieval (MMIR), we search through archives of multimedia artefacts like video programs, by implementing several independent retrieval systems or agents, and we combine the outputs of each retrieval agent in order to generate an overall ranking. In this paper we draw parallels between these seemingly very different applications and show how they share several similarities. In the case of environmental monitoring we also need some mechanism by which we can establish the trust and reputation of each contributing sensor, though this is something we do not need in MMIR. In this paper we present an outline of a trust and reputation framework we have developed and deployed for monitoring the performance of sensors in a heterogeneous sensor network.     

A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales

This paper presents a novel spatio-temporal LSTM (SPATIAL) architecture for time series forecasting applied to environmental datasets. The framework was applied for three different ocean datasets: current speed, temperature, and dissolved oxygen. Network implementation proceeded in two directions that are nominally separated but connected as part of a natural environmental system – across the spatial (between individual sensors) and temporal dimensions of the sensor data. Data from twenty ocean sensors were used to train the model. Results were compared against four baseline models: two machine learning algorithms generated by robust autoML frameworks, and two deep neural networks based on CNN and LSTM, respectively. Results demonstrated ability to accurately replicate complex signals and provide comparable performance to state-of-the-art benchmarks. Learning from multiple sensors simultaneously increased robustness to missing data. This paper addresses two fundamental challenges related to environmental applications of machine learning: 1) data sparsity, particularly in a challenging ocean environment, and 2) environmental datasets are inherently connected in the spatial and temporal directions while classical ML approaches only consider one of these at a time. Furthermore, sharing of parameters across all input steps makes SPATIAL a fast, scalable, and easily-parameterized forecasting framework.     

Sparse and Compositionally Robust Inference of Microbial Ecological Networks

16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.     

New ecological insights through the Global Lake Ecological Observatory Network (GLEON)

Sensor networks are playing an increasingly important role in ecology. Continual advances in affordable sensors and wireless communication are making the development of automated sensing systems with remote communication (i.e., sensor networks) affordable for many ecological research programs (Porter et al. 2005)[1].     

ModuleBlast: identifying activated sub-networks within and across species

Identifying conserved and divergent response patterns in gene networks is becoming increasingly important. A common approach is integrating expression information with gene association networks in order to find groups of connected genes that are activated or repressed. In many cases, researchers are also interested in comparisons across species (or conditions). Finding an active sub-network is a hard problem and applying it across species requires further considerations (e.g. orthology information, expression data and networks from different sources). To address these challenges we devised ModuleBlast, which uses both expression and network topology to search for highly relevant sub-networks. We have applied ModuleBlast to expression and interaction data from mouse, macaque and human to study immune response and aging. The immune response analysis identified several relevant modules, consistent with recent findings on apoptosis and NFκB activation following infection. Temporal analysis of these data revealed cascades of modules that are dynamically activated within and across species. We have experimentally validated some of the novel hypotheses resulting from the analysis of the ModuleBlast results leading to new insights into the mechanisms used by a key mammalian aging protein.     

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the cortical networks involved in dynamical perceptual and cognitive tasks, such as attending to different sounds in a cocktail party. Many past studies have employed data recorded at the sensor level only, i.e., the magnetic fields or the electric potentials recorded outside and on the scalp, and have usually focused on activity that is time-locked to the stimulus presentation. This type of event-related field / potential analysis is particularly useful when there are only a small number of distinct dipolar patterns that can be isolated and identified in space and time. Alternatively, by utilizing anatomical information, these distinct field patterns can be localized as current sources on the cortex. However, for a more sustained response that may not be time-locked to a specific stimulus (e.g., in preparation for listening to one of the two simultaneously presented spoken digits based on the cued auditory feature) or may be distributed across multiple spatial locations unknown a priori, the recruitment of a distributed cortical network may not be adequately captured by using a limited number of focal sources.Here, we describe a procedure that employs individual anatomical MRI data to establish a relationship between the sensor information and the dipole activation on the cortex through the use of minimum-norm estimates (MNE). This inverse imaging approach provides us a tool for distributed source analysis. For illustrative purposes, we will describe all procedures using FreeSurfer and MNE software, both freely available. We will summarize the MRI sequences and analysis steps required to produce a forward model that enables us to relate the expected field pattern caused by the dipoles distributed on the cortex onto the M/EEG sensors. Next, we will step through the necessary processes that facilitate us in denoising the sensor data from environmental and physiological contaminants. We will then outline the procedure for combining and mapping MEG/EEG sensor data onto the cortical space, thereby producing a family of time-series of cortical dipole activation on the brain surface (or "brain movies") related to each experimental condition. Finally, we will highlight a few statistical techniques that enable us to make scientific inference across a subject population (i.e., perform group-level analysis) based on a common cortical coordinate space.     

The River Network Toolkit – RivTool

Freshwater ecosystems are some of the most endangered environments in the world, being affected at multiple scales by the surrounding landscape and human activities therein. Effective research, conservation and management of these ecosystems requires integrating environmental and landscape data with hierarchic river networks by means of summarisation and synthesis of information for large and comprehensive areas at different scales (e.g. basin, sub‐basin, upstream drainage area). The dendritic nature of river networks, the need to tackle multiple scales and the ever‐growing sources of digital information (e.g. temperature or land use data grids) have increasingly led to hardly manageable processing time and stringent hardware requirements when integrating and working with this information. Here we present the River Network Toolkit (RivTool), a software that uses only tabular data to derive and calculate new information at multiple scales for riverine landscapes. It uses data from linear hierarchical river networks and the environmental/landscape data from their respective drainage areas. The software allows the acquisition of: 1) information that characterises river networks based on its topographic nature; 2) data obtained via mathematical calculations that account for the hierarchical and network nature of these systems; and 3) output information using different spatial data sources (e.g. climatic, land use, topologic) that result from up and downstream summarisations. This user‐friendly software considers two units of analysis (segment and sub‐basin) and is time effective even with large datasets. RivTool facilitates and reduces the time required for extracting information for freshwater ecosystems, and may thus contribute to increase scientific productivity, efficiency and accurateness when generating new or improving existing knowledge on large‐scale patterns and processes in river networks.     

国家大型煤电基地生态环境监测技术体系研究——以内蒙古锡林郭勒盟煤电基地为例

随着我国煤电基地建设进程的不断加快,煤电基地建设与开发活动引起的环境问题也日趋严重。了解生态环境质量现状,评估其对生态系统与人民健康水平的影响,制定合理的保护、治理、恢复策略是煤电基地环境保护工作的重中之重,而生态环境监测是解决上述问题的基础。然而,现有的监测技术体系普遍存在自动化水平较低、成本较高、时空覆盖面较低等问题。鉴于物联网技术在提高信息采集效率和改善信息获取方式方面的作用日益显著,所以将物联网技术应用于煤电基地生态环境监测,从感知层、传输层、支撑层、应用层、用户层的角度明确生态环境监测技术体系,为解决上述问题提供有效途径。     

Environmental stratifications as the basis for national,European and global ecological monitoring

There is growing urgency for integration and coordination of global environmental and ecological data and indicators required to respond to the ‘grand challenges’ the planet is facing, including climate change and biodiversity decline. A consistent stratification of land into relatively homogenous strata provides a valuable spatial framework for comparison and analysis of ecological and environmental data across large heterogeneous areas. We discuss how statistical stratification can be used to design national, European and global biodiversity observation networks. The value of strategic ecological survey based on stratified samples is first illustrated using the United Kingdom (UK) Countryside Survey, a national monitoring programme that has measured ecological change in the UK countryside for the last 35 years. We then present a design for a European-wide sampling design for monitoring common habitats, and discuss ways of extending these approaches globally, supported by the recently developed Global Environmental Stratification. The latter provides a robust spatial analytical framework for the identification of gaps in current monitoring efforts, and systematic design of new complementary monitoring and research. Examples from Portugal and the transboundary Kailash Sacred Landscape in the Himalayas illustrate the potential use of this stratification, which has been identified as a focal geospatial dataset within the Group on Earth Observation Biodiversity Observation Network (GEO BON).     

Applications of forensic chemistry to environmental work

Recent trends in environmental remediation have increasingly employed the use of environmental chemistry techniques to decipher the source(s) and fate of the contaminants and, in some cases, to determine their age or apportion them to sources. An extensive database of pyrogenic and petrogenic 'chemical fingerprints' has been constructed by the Gas Technology Institute (GTI) and META Environmental, Inc. using gas chromatography coupled with a flame ionization detector (GC/FID) or with a mass spectrometer (GC/MS). The use of these chemical fingerprinting techniques have been highly successful in discerning wastes from wholly different sources as well as among Manufactured Gas Plant (MGP)-type wastes from different plant operations. However, these techniques have been limited when low-level polycyclic aromatic hydrocarbon (PAH) discernment is required. Specifically, these techniques often do not provide data with sufficient conclusive discriminating power between the 'urban background'PAH sources and those from MGP-operations, which is pertinent for meeting low-level, stringent site-cleanup standards. GTI has been developing a new analytical method for the measurement of 'urban background' PAH contamination. This method measures the compound-specific isotope ratio (CSIR) carbon with a GC/IRMS (isotope ratio mass spectrometer). The GC/IRMS technique is a relatively new analytical tool that has great potential as an environmental forensic method at former MGP sites. This paper focuses on the applications of both chemical and isotopic analysis of samples to discern PAH contamination in the environment.     

Healthcare Worker Contact Networks and the Prevention of Hospital-Acquired Infections

We present a comprehensive approach to using electronic medical records (EMR) for constructing contact networks of healthcare workers in a hospital. This approach is applied at the University of Iowa Hospitals and Clinics (UIHC) – a 3.2 million square foot facility with 700 beds and about 8,000 healthcare workers – by obtaining 19.8 million EMR data points, spread over more than 21 months. We use these data to construct 9,000 different healthcare worker contact networks, which serve as proxies for patterns of actual healthcare worker contacts. Unlike earlier approaches, our methods are based on large-scale data and do not make any a priori assumptions about edges (contacts) between healthcare workers, degree distributions of healthcare workers, their assignment to wards, etc. Preliminary validation using data gathered from a 10-day long deployment of a wireless sensor network in the Medical Intensive Care Unit suggests that EMR logins can serve as realistic proxies for hospital-wide healthcare worker movement and contact patterns. Despite spatial and job-related constraints on healthcare worker movement and interactions, analysis reveals a strong structural similarity between the healthcare worker contact networks we generate and social networks that arise in other (e.g., online) settings. Furthermore, our analysis shows that disease can spread much more rapidly within the constructed contact networks as compared to random networks of similar size and density. Using the generated contact networks, we evaluate several alternate vaccination policies and conclude that a simple policy that vaccinates the most mobile healthcare workers first, is robust and quite effective relative to a random vaccination policy.     

Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments

Analysis of protein interaction networks and protein complexes using affinity purification and mass spectrometry (AP/MS) is among most commonly used and successful applications of proteomics technologies. One of the foremost challenges of AP/MS data is a large number of false-positive protein interactions present in unfiltered data sets. Here we review computational and informatics strategies for detecting specific protein interaction partners in AP/MS experiments, with a focus on incomplete (as opposite to genome wide) interactome mapping studies. These strategies range from standard statistical approaches, to empirical scoring schemes optimized for a particular type of data, to advanced computational frameworks. The common denominator among these methods is the use of label-free quantitative information such as spectral counts or integrated peptide intensities that can be extracted from AP/MS data. We also discuss related issues such as combining multiple biological or technical replicates, and dealing with data generated using different tagging strategies. Computational approaches for benchmarking of scoring methods are discussed, and the need for generation of reference AP/MS data sets is highlighted. Finally, we discuss the possibility of more extended modeling of experimental AP/MS data, including integration with external information such as protein interaction predictions based on functional genomics data.     

Applications of forensic chemistry to environmental work

Recent trends in environmental remediation have increasingly employed the use of environmental chemistry techniques to decipher the source(s) and fate of the contaminants and, in some cases, to determine their age or apportion them to sources. An extensive database of pyrogenic and petrogenic ‘chemical fingerprints’ has been constructed by the Gas Technology Institute (GTI) and META Environmental, Inc. using gas chromatography coupled with a flame ionization detector (GC/FID) or with a mass spectrometer (GC/MS). The use of these chemical fingerprinting techniques have been highly successful in discerning wastes from wholly different sources as well as among Manufactured Gas Plant (MGP)-type wastes from different plant operations. However, these techniques have been limited when low-level polycyclic aromatic hydrocarbon (PAH) discernment is required. Specifically, these techniques often do not provide data with sufficient conclusive discriminating power between the ‘urban background’PAH sources and those from MGP-operations, which is pertinent for meeting low-level, stringent site-cleanup standards. GTI has been developing a new analytical method for the measurement of ‘urban background’ PAH contamination. This method measures the compound-specific isotope ratio (CSIR) carbon with a GC/IRMS (isotope ratio mass spectrometer). The GC/IRMS technique is a relatively new analytical tool that has great potential as an environmental forensic method at former MGP sites. This paper focuses on the applications of both chemical and isotopic analysis of samples to discern PAH contamination in the environment.

     

Functional analysis of protein kinase networks in living cells: beyond "knock-outs" and "knock-downs"

The identification of over 500 protein kinases encoded by the human genome sequence offers one measure of the importance of protein kinase networks in cell biology. High throughput technologies for inactivating genes are producing an awe-inspiring amount of data on the cellular and organismal effects of reducing the levels of individual protein kinases. Despite these technical advances, our understanding of kinase networks remains imprecise. Major challenges include correctly assigning kinases to particular networks, understanding how they are regulated, and identifying the relevant in vivo substrates. Genetic methods provide a way of addressing these questions, but their application requires understanding the nuances of how different types of mutations can affect protein kinases. The goal of this article is to provide a brief introductory primer into these issues using examples from yeast MAPK cascades and to motivate future systematic genetic analysis focusing on individual residues of protein kinases.