Legacy Datasets Research Articles

Mapping topsoil organic carbon concentrations and stocks for Tanzania

Tanzania is one of the countries that has embarked on a national programme under the United Nations collaborative initiative on Reducing Emissions from Deforestation and forest Degradation (REDD). Tanzania is currently developing the capacity to enter into a carbon monitoring REDD+ regime. In this context spatially representative soil carbon datasets and accurate predictive maps are important for determining the soil organic carbon pool. The main objective of this study was to model and map the SOC stock for the 0–30-cm soil layer to provide baseline information for REDD+ purposes. Topsoil data of over 1400 locations spread throughout Tanzania from the National Forest Monitoring and Assessment (NAFORMA), were used, supplemented by two legacy datasets, to calibrate simple kriging with varying local means models. Maps of SOC concentrations (g kg−1) were generated for the 0–10-cm, 10–20-cm, 20–30-cm, 0–30-cm layers, and maps of bulk density and SOC stock (kg m−2) for the 0–30-cm layer. Two approaches for modelling SOC stocks were considered here: the calculate-then-model (CTM) approach and the model-then-calculate approach (MTC). The spatial predictions were validated by means of 10-fold cross-validation. Uncertainty associated to the estimated SOC stocks was quantified through conditional Gaussian simulation. Estimates of SOC stocks for the main land cover classes are provided. Environmental covariates related to soil and terrain proved to be the strongest predictors for all properties modelled. The mean predicted SOC stock for the 0–30-cm layer was 4.1 kg m−2 (CTM approach) translating to a total national stock of 3.6 Pg. The MTC approach gave similar results. The largest stocks are found in forest and grassland ecosystems, while woodlands and bushlands contain two thirds of the total SOC stock. The root mean squared error for the 0–30-cm layer was 1.8 kg m−2, and the R2-value was 0.51. The R2-value of SOC concentration for the 0–30-cm layer was 0.60 and that of bulk density 0.56. The R2-values of the predicted SOC concentrations for the 10-cm layers vary between 0.46 and 0.54. The 95% confidence interval of the predicted average SOC stock is 4.01–4.15 kg m−2, and that of the national total SOC stock 3.54–3.65 Pg. Uncertainty associated with SOC concentration had the largest contribution to SOC stock uncertainty. These findings have relevance for the ongoing REDD+ readiness process in Tanzania by supplementing the previous knowledge of significant carbon pools. The soil organic carbon pool makes up a relatively large proportion of carbon in Tanzania and is therefore an important carbon pool to consider alongside the ones related to the woody biomass. Going forward, the soil organic carbon data can potentially be used in the determination of reference emission levels and the future monitoring, reporting and verification of organic carbon pools.

Genetic and genomic monitoring with minimally invasive sampling methods.

The decreasing cost and increasing scope and power of emerging genomic technologies are reshaping the field of molecular ecology. However, many modern genomic approaches (e.g., RAD‐seq) require large amounts of high‐quality template DNA. This poses a problem for an active branch of conservation biology: genetic monitoring using minimally invasive sampling (MIS) methods. Without handling or even observing an animal, MIS methods (e.g., collection of hair, skin, faeces) can provide genetic information on individuals or populations. Such samples typically yield low‐quality and/or quantities of DNA, restricting the type of molecular methods that can be used. Despite this limitation, genetic monitoring using MIS is an effective tool for estimating population demographic parameters and monitoring genetic diversity in natural populations. Genetic monitoring is likely to become more important in the future as many natural populations are undergoing anthropogenically driven declines, which are unlikely to abate without intensive adaptive management efforts that often include MIS approaches. Here, we profile the expanding suite of genomic methods and platforms compatible with producing genotypes from MIS, considering factors such as development costs and error rates. We evaluate how powerful new approaches will enhance our ability to investigate questions typically answered using genetic monitoring, such as estimating abundance, genetic structure and relatedness. As the field is in a period of unusually rapid transition, we also highlight the importance of legacy data sets and recommend how to address the challenges of moving between traditional and next‐generation genetic monitoring platforms. Finally, we consider how genetic monitoring could move beyond genotypes in the future. For example, assessing microbiomes or epigenetic markers could provide a greater understanding of the relationship between individuals and their environment.

30 Years in the Life of an Active Submarine Volcano: A Time‐Lapse Bathymetry Study of the Kick‐'em‐Jenny Volcano, Lesser Antilles

AbstractEffective monitoring is an essential part of identifying and mitigating volcanic hazards. In the submarine environment this is more difficult than onshore because observations are typically limited to land‐based seismic networks and infrequent shipboard surveys. Since the first recorded eruption in 1939, the Kick‐'em‐Jenny (KeJ) volcano, located 8 km off northern Grenada, has been the source of 13 episodes of T‐phase signals. These distinctive seismic signals, often coincident with heightened body‐wave seismicity, are interpreted as extrusive eruptions. They have occurred with a recurrence interval of around a decade, yet direct confirmation of volcanism has been rare. By conducting new bathymetric surveys in 2016 and 2017 and reprocessing 4 legacy data sets spanning 30 years we present a clearer picture of the development of KeJ through time. Processed grids with a cell size of 5 m and vertical precision on the order of 1–4 m allow us to correlate T‐phase episodes with morphological changes at the volcano's edifice. In the time‐period of observation 7.09 × 106 m3 of material has been added through constructive volcanism – yet 5 times this amount has been lost through landslides. Limited recent magma production suggests that KeJ may be susceptible to larger eruptions with longer repeat times than have occurred during the study interval, behavior more similar to sub‐aerial volcanism in the arc than previously thought. T‐phase signals at KeJ have a varied origin and are unlikely to be solely the result of extrusive submarine eruptions. Our results confirm the value of repeat swath bathymetry surveys in assessing submarine volcanic hazards.

CSmetaPred: a consensus method for prediction of catalytic residues

BackgroundKnowledge of catalytic residues can play an essential role in elucidating mechanistic details of an enzyme. However, experimental identification of catalytic residues is a tedious and time-consuming task, which can be expedited by computational predictions. Despite significant development in active-site prediction methods, one of the remaining issues is ranked positions of putative catalytic residues among all ranked residues. In order to improve ranking of catalytic residues and their prediction accuracy, we have developed a meta-approach based method CSmetaPred. In this approach, residues are ranked based on the mean of normalized residue scores derived from four well-known catalytic residue predictors. The mean residue score of CSmetaPred is combined with predicted pocket information to improve prediction performance in meta-predictor, CSmetaPred_poc.ResultsBoth meta-predictors are evaluated on two comprehensive benchmark datasets and three legacy datasets using Receiver Operating Characteristic (ROC) and Precision Recall (PR) curves. The visual and quantitative analysis of ROC and PR curves shows that meta-predictors outperform their constituent methods and CSmetaPred_poc is the best of evaluated methods. For instance, on CSAMAC dataset CSmetaPred_poc (CSmetaPred) achieves highest Mean Average Specificity (MAS), a scalar measure for ROC curve, of 0.97 (0.96). Importantly, median predicted rank of catalytic residues is the lowest (best) for CSmetaPred_poc. Considering residues ranked ≤20 classified as true positive in binary classification, CSmetaPred_poc achieves prediction accuracy of 0.94 on CSAMAC dataset. Moreover, on the same dataset CSmetaPred_poc predicts all catalytic residues within top 20 ranks for ~73% of enzymes. Furthermore, benchmarking of prediction on comparative modelled structures showed that models result in better prediction than only sequence based predictions. These analyses suggest that CSmetaPred_poc is able to rank putative catalytic residues at lower (better) ranked positions, which can facilitate and expedite their experimental characterization.ConclusionsThe benchmarking studies showed that employing meta-approach in combining residue-level scores derived from well-known catalytic residue predictors can improve prediction accuracy as well as provide improved ranked positions of known catalytic residues. Hence, such predictions can assist experimentalist to prioritize residues for mutational studies in their efforts to characterize catalytic residues. Both meta-predictors are available as webserver at: http://14.139.227.206/csmetapred/.

Solving the conundrum of intervening strong Mg II absorbers towards gamma-ray bursts and quasars

Previous studies have shown that the incidence rate of intervening strong Mg ii absorbers towards gamma-ray bursts (GRBs) were a factor of 2–4 higher than towards quasars. Exploring the similar sized and uniformly selected legacy data sets XQ-100 and XSGRB, each consisting of 100 quasar and 81 GRB afterglow spectra obtained with a single instrument (VLT/X-shooter), we demonstrate that there is no disagreement in the number density of strong Mg ii absorbers with rest-frame equivalent widths \hbox{$W_{\rm r}^{\lambda2796}>1$} Å towardsGRBs and quasars in the redshift range 0.1 ≲ z ≲ 5. With large and similar sample sizes, and path length coverages of Δz = 57.8 and 254.4 for GRBs and quasars, respectively, the incidences of intervening absorbers are consistent within 1σ uncertainty levels at all redshifts. For absorbers at z < 2.3, the incidence towards GRBs is a factor of 1.5 ± 0.4 higher than the expected number of strong Mg ii absorbers in Sloan Digital Sky Survey (SDSS) quasar spectra, while for quasar absorbers observed with X-shooter we find an excess factor of 1.4 ± 0.2 relative to SDSS quasars. Conversely, the incidence rates agree at all redshifts with reported high-spectral-resolution quasar data, and no excess is found. The only remaining discrepancy in incidences is between SDSS Mg ii catalogues and high-spectral-resolution studies. The rest-frame equivalent-width distribution also agrees to within 1σ uncertainty levels between the GRB and quasar samples. Intervening strong Mg ii absorbers towards GRBs are therefore neither unusually frequent, nor unusually strong.

Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector

The water utility sector is subject to stringent legislation, seeking to address both the evolution of practices within the chemical/pharmaceutical industry, and the safeguarding of environmental protection, and which is informed by stakeholder views. Growing public environmental awareness is balanced by fair apportionment of liability within-sector. This highly complex and dynamic context poses challenges for water utilities seeking to manage the diverse chemicals arising from disparate sources reaching Wastewater Treatment Plants, including residential, commercial, and industrial points of origin, and diffuse sources including agricultural and hard surface water run-off. Effluents contain broad ranges of organic and inorganic compounds, herbicides, pesticides, phosphorus, pharmaceuticals, and chemicals of emerging concern. These potential pollutants can be in dissolved form, or arise in association with organic matter, the associated risks posing significant environmental challenges. This paper examines how the adoption of new Big Data tools and computational technologies can offer great advantage to the water utility sector in addressing this challenge. Big Data approaches facilitate improved understanding and insight of these challenges, by industry, regulator, and public alike. We discuss how Big Data approaches can be used to improve the outputs of tools currently in use by the water industry, such as SAGIS (Source Apportionment GIS system), helping to reveal new relationships between chemicals, the environment, and human health, and in turn provide better understanding of contaminants in wastewater (origin, pathways, and persistence). We highlight how the sector can draw upon Big Data tools to add value to legacy datasets, such as the Chemicals Investigation Programme in the UK, combined with contemporary data sources, extending the lifespan of data, focusing monitoring strategies, and helping users adapt and plan more efficiently. Despite the relative maturity of the Big Data technology and adoption in many wider sectors, uptake within the water utility sector remains limited to date. By contrast with the extensive range of applications of Big Data in in other sectors, highlight is drawn to how improvements are required to achieve the full potential of this technology in the water utility industry.

Simulating and quantifying legacy topographic data uncertainty: an initial step to advancing topographic change analyses

Rapid technological advances, sustained funding, and a greater recognition of the value of topographic data have helped develop an increasing archive of topographic data sources. Advances in basic and applied research related to Earth surface changes require researchers to integrate recent high-resolution topography (HRT) data with the legacy datasets. Several technical challenges and data uncertainty issues persist to date when integrating legacy datasets with more recent HRT data. The disparate data sources required to extend the topographic record back in time are often stored in formats that are not readily compatible with more recent HRT data. Legacy data may also contain unknown error or unreported error that make accounting for data uncertainty difficult. There are also cases of known deficiencies in legacy datasets, which can significantly bias results. Finally, scientists are faced with the daunting challenge of definitively deriving the extent to which a landform or landscape has or will continue to change in response natural and/or anthropogenic processes. Here, we examine the question: how do we evaluate and portray data uncertainty from the varied topographic legacy sources and combine this uncertainty with current spatial data collection techniques to detect meaningful topographic changes? We view topographic uncertainty as a stochastic process that takes into consideration spatial and temporal variations from a numerical simulation and physical modeling experiment. The numerical simulation incorporates numerous topographic data sources typically found across a range of legacy data to present high-resolution data, while the physical model focuses on more recent HRT data acquisition techniques. Elevation uncertainties observed from anchor points in the digital terrain models are modeled using “states” in a stochastic estimator. Stochastic estimators trace the temporal evolution of the uncertainties and are natively capable of incorporating sensor measurements observed at various times in history. The geometric relationship between the anchor point and the sensor measurement can be approximated via spatial correlation even when a sensor does not directly observe an anchor point. Findings from a numerical simulation indicate the estimated error coincides with the actual error using certain sensors (Kinematic GNSS, ALS, TLS, and SfM-MVS). Data from 2D imagery and static GNSS did not perform as well at the time the sensor is integrated into estimator largely as a result of the low density of data added from these sources. The estimator provides a history of DEM estimation as well as the uncertainties and cross correlations observed on anchor points. Our work provides preliminary evidence that our approach is valid for integrating legacy data with HRT and warrants further exploration and field validation.

Full-waveform seismic tomography of the Vrancea, Romania, subduction region

The Vrancea region is one of the few locations of deep seismicity in Europe. Seismic tomography has been able to map lithospheric downwelling, but has not been able yet to clearly discriminate between competing geodynamic interpretations of the geological and geophysical evidence available. We study the seismic structure of the Vrancea subduction zone, using adjoint-based, full-waveform tomography to map the 3D vP and vS structure in detail. We use the database that was built during the CALIXTO (Carpathian Arc Lithosphere X-Tomography) temporary experiment, restricted to the broadband sensors and local intermediate-depth events. We fit waveforms with a cross-correlation misfit criterion in separate time windows around the expected P and S arrivals, and perform 17 iterations of vP and vS model updates (altogether, requiring about 16 million CPU hours) before reaching stable convergence. Among other features, our resulting model shows a nearly vertical, high-velocity body, that overlaps with the distribution of seismicity in its northeastern part. In its southwestern part, a slab appears to dip less steeply to the NW, and is suggestive of ongoing – or recently concluded – subduction geodynamic processes. Joint inversion for vP and vS allow us to address the vP/vS ratio distribution, that marks high vP/vS in the crust beneath the Focsani sedimentary basin – possibly due to high fluid pressure – and a low vP/vS edge along the lower plane of the subducting lithosphere, that in other similar environment has been attributed to dehydration of serpentine in the slab. In spite of the restricted amount of data available, and limitations on the usable frequency pass-band, full-waveform inversion reveals its potential to improve the general quality of imaging with respect to other tomographic techniques – although at a sensible cost in terms of computing resources. Our study also shows that re-analysis of legacy data sets with up-to-date techniques may bring new, useful, information.

Development and Evaluation of a Barley 50k iSelect SNP Array.

High-throughput genotyping arrays continue to be an attractive, cost-effective alternative to sequencing based approaches. We have developed a new 50k Illumina Infinium iSelect genotyping array for barley, a cereal crop species of major international importance. The majority of SNPs on the array have been extracted from variants called in exome capture data of a wide range of European barley germplasm. We used the recently published barley pseudomolecule assembly to map the exome capture data, which allowed us to generate markers with accurate physical positions and detailed gene annotation. Markers from an existing and widely used barley 9k Infinium iSelect array were carried over onto the 50k chip for backward compatibility. The array design featured 49,267 SNP markers that converted into 44,040 working assays, of which 43,461 were scorable in GenomeStudio. Of the working assays, 6,251 are from the 9k iSelect platform. We validated the SNPs by comparing the genotype calls from the new array to legacy datasets. Rates of agreement averaged 98.1 and 93.9% respectively for the legacy 9k iSelect SNP set (Comadran et al., 2012) and the exome capture SNPs. To test the utility of the 50k chip for genetic mapping, we genotyped a segregating population derived from a Golden Promise × Morex cross (Liu et al., 2014) and mapped over 14,000 SNPs to genetic positions which showed a near exact correspondence to their known physical positions. Manual adjustment of the cluster files used by the interpreting software for genotype scoring improved results substantially, but migration of cluster files between sites led to a deterioration of results, suggesting that local adjustment of cluster files is required on a site-per-site basis. Information relating to the markers on the chip is available online at https://ics.hutton.ac.uk/50k.

Spatial variability of Australian soil texture: A multiscale analysis

Understanding how soil variability changes with spatial scale is critical to our ability to understand and model soil processes at scales relevant to decision makers. The compilation of large legacy data sets has opened up new possibilities to model spatial variability at the continental or even global scale. Using the National Soil Site Collation (NSSC) dataset of Australia we created empirical variograms for sand and clay fraction at extents from 1km to continental. The NSSC dataset is highly spatially clustered; a typical feature of legacy datasets. This leads to lumpy artefacts in the variograms. To reduce this lumpiness we employed grid based declustering. We used the declustered empirical variograms to calculate the Hausdorff Besicovitch Dimension – a unitless measure of spatial roughness. We first fit a power model to each declustered variogram and calculated the Hausdorff Besicovitch dimension at each modelled scale. This allowed us to assess the roughness or variability at each modelled extent, however this assessment was somewhat arbitrary and showed that roughness depends on the extent. We have proposed a new model that allows us to calculate the Hausdorff Besicovitch dimension continuously across all extents. The conceptual basis of this model moves away from a multi-fractal framework typically used by soil scientists. It allows us to describe spatial variability or stochasticity as a continuous function of spatial separation. Both our new model and the continental scale variograms of texture emphasise the high degree of short range variability in soil texture. Empirical variograms indicate that around 50% of spatial variability occurs at <10km, and 30% at <1km. Spatial variability of soil texture increases with depth consistently across all modelled extents. Beyond extents of around 100km, the Hausdorff Besicovitch Dimension remains relatively stable. Soil spatial variability is highly stochastic at fine scales however it changes gradually with extent and scale rather than abruptly.

Multi-view kernel completion

In this paper, we introduce the first method that (1) can complete kernel matrices with completely missing rows and columns as opposed to individual missing kernel values, with help of information from other incomplete kernel matrices. Moreover, (2) the method does not require any of the kernels to be complete a priori, and (3) can tackle non-linear kernels. The kernel completion is done by finding, from the set of available incomplete kernels, an appropriate set of related kernels for each missing entry. These aspects are necessary in practical applications such as integrating legacy data sets, learning under sensor failures and learning when measurements are costly for some of the views. The proposed approach predicts missing rows by modelling both within-view and between-view relationships among kernel values. For within-view learning, we propose a new kernel approximation that generalizes and improves Nystrom approximation. We show, both on simulated data and real case studies, that the proposed method outperforms existing techniques in the settings where they are available, and extends applicability to new settings.

Ground Parrots and fire in east Gippsland, Victoria: habitat occupancy modelling from automated sound recordings

The peak-and-decline in the density of eastern populations of Ground Parrot (Pezoporus wallicus) with increasing time since fire is one of the best-documented post-fire responses of any Australian animal. However, existing studies have typically shown this relationship within a set of sites highly likely to be occupied by Ground Parrots, rather than demonstrating that fire history affects whether a site is occupied. This paper describes a landscape-scale, targeted survey of Ground Parrots at 80 heathland sites between Marlo and Mallacoota, Victoria. Selection of sites was stratified by combinations of time since fire and modelled predictions of the distribution of Ground Parrots. All sites were sampled 10 times in late 2013 using automated sound recorders, and a subset of 23 sites had at least one traditional, observer-based physical survey. Both time since fire and the species-distribution model were significantly associated with the probability of Ground Parrots occupying a site. The most parsimonious occupancy model was applied to two earlier datasets from the study area, and provided indirect evidence for an inferred decline in Ground Parrot occupancy in recent decades. Landscape modelling of the expected total area occupied by Ground Parrots between 1979 and 2013 suggested a strong peak would have occurred in the late 1990s; suitability of habitat over much of the landscape is now declining. Finally, given that many Australian fauna species are responsive to fire, this study shows that assumptions behind the management of such species can be tested efficiently by combining legacy datasets and literature with contemporary data collection and modelling methods.

Eastern Mediterranean Economic Exchange during the Iron Age: Portable X-Ray Fluorescence and Neutron Activation Analysis of Cypriot-Style Pottery in the Amuq Valley, Turkey.

Two markers of regional exchange in the Eastern Mediterranean during the first millennium BCE are the White Painted and Bichrome Wares from Cyprus’s Cypro-Geometric and Cypro-Archaic periods. Although these ceramics are often assumed to be imports from Cyprus, excavations in southern Turkey at sites such as Tarsus-Gözlükule, Kilise Tepe, Sirkeli Höyük, and Kinet Höyük suggest that at least some of this pottery was produced locally, requiring a major revision of our understanding of economic interaction in the Eastern Mediterranean. We employ a combination of portable x-ray fluorescence and neutron activation analysis to investigate the White Painted and Bichrome Wares recovered from Tell Tayinat, Çatal Höyük, and Tell Judaidah, three sites in the Amuq Valley of southeastern Anatolia. Our results demonstrate that a clear geochemical distinction exists between imported and local versions of this pottery. Through comparison with legacy datasets, we locate the likely origin of the imported pottery in the Circum-Troodos sediments of central and southern Cyprus. The secondary and tertiary settlements of Çatal Höyük and Tell Judaidah had access only to this imported material. In contrast, the inhabitants of Tell Tayinat, capital city of the region, consumed both imported and locally produced White Painted and Bichrome Wares. This pattern cannot be explained in purely economic terms whereby the frequency of imports decreases as distance from the point of production increases. Instead, we suggest that elite feasting practices drove demand, resulting in either local potters producing Cypriot-style pottery or Cypriot potters settling in the vicinity of Tell Tayinat. These findings offer new insights into the relationship between historically attested Iron Age kingdoms in southern Turkey and Cyprus and complicate our understanding of exchange in the Eastern Mediterranean during the Iron Age.

Hybridizing Cartesian Genetic Programming and Harmony Search for adaptive feature construction in supervised learning problems

The advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very diverse approaches. In this context this work focuses on the automatic construction of features in supervised learning problems, which differs from the conventional selection of features in that new characteristics with enhanced predictive power are inferred from the original dataset. In particular this manuscript proposes a new iterative feature construction approach based on a self-learning meta-heuristic algorithm (Harmony Search) and a solution encoding strategy (correspondingly, Cartesian Genetic Programming) suited to represent combinations of features by means of constant-length solution vectors. The proposed feature construction algorithm, coined as Adaptive Cartesian Harmony Search (ACHS), incorporates modifications that allow exploiting the estimated predictive importance of intermediate solutions and, ultimately, attaining better convergence rate in its iterative learning procedure. The performance of the proposed ACHS scheme is assessed and compared to that rendered by the state of the art in a toy example and three practical use cases from the literature. The excellent performance figures obtained in these problems shed light on the widespread applicability of the proposed scheme to supervised learning with legacy datasets composed by already refined characteristics.

CADASTRAL POSITIONING ACCURACY IMPROVEMENT: A CASE STUDY IN MALAYSIA

Abstract. Cadastral map is a parcel-based information which is specifically designed to define the limitation of boundaries. In Malaysia, the cadastral map is under authority of the Department of Surveying and Mapping Malaysia (DSMM). With the growth of spatial based technology especially Geographical Information System (GIS), DSMM decided to modernize and reform its cadastral legacy datasets by generating an accurate digital based representation of cadastral parcels. These legacy databases usually are derived from paper parcel maps known as certified plan. The cadastral modernization will result in the new cadastral database no longer being based on single and static parcel paper maps, but on a global digital map. Despite the strict process of the cadastral modernization, this reform has raised unexpected queries that remain essential to be addressed. The main focus of this study is to review the issues that have been generated by this transition. The transformed cadastral database should be additionally treated to minimize inherent errors and to fit them to the new satellite based coordinate system with high positional accuracy. This review result will be applied as a foundation for investigation to study the systematic and effectiveness method for Positional Accuracy Improvement (PAI) in cadastral database modernization.

Rediscovering the rural landscape of Carthage's hinterland: a reassessment of the Carthaginian Countryside survey

AbstractThis article discusses a GIS analysis of the results of the Carthaginian Countryside survey conducted by Joseph Greene from 1979 to 1983. The 136 sites identified by the project constitute one of the largest datasets of site locations recorded in the hinterland of Carthage from multiple time periods. The results of this survey have not generally been integrated into modern studies of the region because the project was never fully published and has not been digitised. This paper discusses the challenges of working with legacy data and the importance of using GIS to both preserve and analyse the Carthaginian Countryside survey. The results presented here reveal new insights into the antiquity of the rural landscape around the city of Carthage and the importance of revisiting legacy datasets to contextualise current research.

High-density point-receiver seismic survey of a deep carbonate cave reservoir system: a case study from the Tarim Basin, Western China

In the northern rim of the Tarim Basin, western China, oil is being produced from an extensive system of interconnected caves, faults and fractures in Ordovician carbonates buried about 6000 to 7000 m below the surface. Although onshore, drilling costs are high due to the depth of the field, so, to produce the field economically, wells must be accurately located within the cavernous features to access as large a drainage area as possible. However, imaging fine details of the reservoir structures presents challenges due to a complex overburden. While the surface terrain and near-surface geology are relatively benign for land seismic operations, the subsurface exhibits large lateral velocity changes related to widely distributed Permian volcanic formations around 1000 and 1200 m above the target zone. These formations are considered to have led to errors in the imaging of the target layers in legacy datasets due to incorrect velocity modelling of the overburden. Improved imaging of detailed features of the deep cavernous carbonate reservoir beneath the complex overburden would require an accurate velocity model in addition to seismic data with both high temporal and spatial resolution. To determine whether new land seismic acquisition and processing technologies could provide a significant uplift in imaging quality, in 2013 PetroChina Tarim Oil Company (TOC) acquired a high-density full-azimuth 3D seismic survey using a point-source and point-receiver geometry. The dataset was recorded using an advanced high-channel count acquisition system and processed with high-end processing techniques designed to address the various challenges presented in the area. Three approaches were used over different depth ranges to develop an accurate velocity model for prestack depth migration (PSDM). Model velocities were constrained using available well data, providing confidence in the accuracy of the resulting image. Relative amplitude preserving (RAP) methods were used for noise attenuation and signal processing to maximise the reliability of subsequent inversion and interpretation work.

A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors.

Test systems to identify developmental toxicants are urgently needed. A combination of human stem cell technology and transcriptome analysis was to provide a proof of concept that toxicants with a related mode of action can be identified and grouped for read-across. We chose a test system of developmental toxicity, related to the generation of neuroectoderm from pluripotent stem cells (UKN1), and exposed cells for 6 days to the histone deacetylase inhibitors (HDACi) valproic acid, trichostatin A, vorinostat, belinostat, panobinostat and entinostat. To provide insight into their toxic action, we identified HDACi consensus genes, assigned them to superordinate biological processes and mapped them to a human transcription factor network constructed from hundreds of transcriptome data sets. We also tested a heterogeneous group of ‘mercurials’ (methylmercury, thimerosal, mercury(II)chloride, mercury(II)bromide, 4-chloromercuribenzoic acid, phenylmercuric acid). Microarray data were compared at the highest non-cytotoxic concentration for all 12 toxicants. A support vector machine (SVM)-based classifier predicted all HDACi correctly. For validation, the classifier was applied to legacy data sets of HDACi, and for each exposure situation, the SVM predictions correlated with the developmental toxicity. Finally, optimization of the classifier based on 100 probe sets showed that eight genes (F2RL2, TFAP2B, EDNRA, FOXD3, SIX3, MT1E, ETS1 and LHX2) are sufficient to separate HDACi from mercurials. Our data demonstrate how human stem cells and transcriptome analysis can be combined for mechanistic grouping and prediction of toxicants. Extension of this concept to mechanisms beyond HDACi would allow prediction of human developmental toxicity hazard of unknown compounds with the UKN1 test system.Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-015-1573-y) contains supplementary material, which is available to authorized users.

What can legacy datasets tell us about soil quality trends? Soil acidity in Victoria

Purpose-built soil monitoring networks have been established in many countries to identify where soil functionality is threatened and to target remediation initiatives. An alternative to purpose-built soil monitoring networks is to use legacy soils information. Such information yields almost instant assessments of soil change but the results should be interpreted with caution since the information was not collected with monitoring in mind. We assess the threat of soil acidification in Victoria using two legacy datasets: (i) the Victorian Soils Information System (VSIS) which is a repository of the results of soil analyses conducted for scientific purposes since the 1950s and (ii) a database of 75 000 routine soil test results requested by farmers between 1973 and 1993. We find that the VSIS measurements are clustered in space and time and are therefore suitable for local rather than broad-scale assessments of soil change. The farmers’ results have better spatial and temporal coverage and space-time models can be used to quantify the spatial and temporal trends in the pH measurements. However, careful validation of these findings is required since we do not completely understand how the measured paddocks were selected and we cannot be certain that sampling or laboratory protocols have not changed with time.

Making the Dead Visible: Problems and Solutions for “Big” Picture Approaches to the Past, and Dealing with Large “Mortuary” Datasets

There can be few “bigger” questions than the nature and development of human experience and self-awareness and few better ways to study it than through the changing treatment of the dead over time. Funded by the John Templeton Foundation, the ‘Invisible Dead’ project (Durham University) is exploring diachronic changes in mortuary practices across two regions: Britain and the Levant. In doing so, it uses archaeology as a way to approach fundamental questions about the human condition. This paper explores the principal difficulties faced during the construction of a database for this project and their wider relevance for the development of robust and successful methods for the study of large “mortuary” datasets in the future. It discusses the issues and biases identified within the mortuary record and how the project has sought to mitigate some of these. By adopting a flexible and ultimately expandable approach to data entry and analysis, value can be added to legacy datasets and “grey” literature, allowing us to make comparisons between regions which are both geographically and chronologically distinct.