Mapping the spatial variation of soil moisture at the large scale using GPR for pavement applications

The characterization of shallow soil moisture spatial variability at the large scale is a crucial issue in many research studies and fields of application ranging from agriculture and geology to civil and environmental engineering. In this framework, this work contributes to the research in the area of pavement engineering for preventing damages and planning effective management. High spatial variations of subsurface water content can lead to unexpected damage of the load-bearing layers; accordingly, both safety and operability of roads become lower, thereby affecting an increase in expected accidents. A pulsed ground-penetrating radar system with ground-coupled antennas, i.e., 600-MHz and 1600-MHz center frequencies of investigation, was used to collect data in a 16 m × 16 m study site in the Po Valley area in northern Italy. Two ground-penetrating radar techniques were employed to non-destructively retrieve the subsurface moisture spatial profile. The first technique is based on the evalu¬ation of the dielectric permittivity from the attenuation of signal amplitudes. Therefore, dielectrics were converted into moisture values using soil-specific coefficients from Topp’s relationship. Ground-penetrating-radar-derived values of soil moisture were then compared with measurements from eight capacitance probes. The second technique is based on the Rayleigh scattering of the signal from the Fresnel theory, wherein the shifts of the peaks of frequency spectra are assumed comprehensive indi¬cators for characterizing the spatial variability of moisture. Both ground-penetrating radar methods have shown great promise for mapping the spatial variability of soil moisture at the large scale

Using eDNA and Habitat Suitability Modeling to Better Understand the Range and Habitat Requirements of the Eastern Black Rail

The Black Rail (Laterallus jamaicensis) is a marsh bird that is globally listed as Near Threatened and is being considered for listing under the federal Endangered Species Act. This species has experienced concerning population declines throughout its range. Black Rails are difficult to detect due to their small size, concealing habitat, and cryptic behavior. The most common survey method for rails uses audio callback but does not detect unresponsive individuals, is constrained seasonally as well as temporally, and requires significant personnel effort. New methods are needed to provide information on the distribution and habitat requirements for this threatened species. Here, I describe a novel detection method for Black Rail using environmental DNA (eDNA) and an ecological niche model identifying areas and characteristics of suitable habitat for this species. To detect Black Rail eDNA I developed a qPCR assay that targets a 219-bp region of the cytochrome c oxidase subunit 1 gene (COI) and uses a fluorescent reporter probe to increase specificity. The assay reliably produces a signal when sufficient copies of Black Rail template are present, and does not produce signal when tested for cross-species amplification using genomic DNA from sympatric rail species. The assay successfully amplified Black Rail eDNA from environmental samples taken from locations with positive detections. I tested statistically whether various environmental factors, as well as sampling and handling variables, affected eDNA detectability. Among the factors tested for their influence on amplification success (time between collection and DNA extraction, storage temperature before filtering, field detection method (audio, visual, camera trap, none), time between detection and sample collection, water salinity, and air temperature), only water depth was found to have a significant effect. I also created a habitat suitability model for the Eastern Black Rail focusing on the Atlantic coastal plain using eBird data contributed by citizen scientists and environmental variable data from the Esri databank using a maximum entropy model framework. The map generated by the MaxEnt model indicated habitat suitability in areas known for Black Rail occupation. The environmental factors that best predicted Black Rail presence were flooded areas of shrub and herbaceous vegetation, proximity to water, and flat plains. These environmental variable associations were congruent with other habitat association studies conducted in other parts of the species' range that focused on smaller areas and used presence data collected through surveys. My habitat suitability model had comparable statistical parameters to other MaxEnt models created for birds. Correlation with known areas of Black Rail occupation and previous habitat associations confirms the validity of the model and importance of high marsh habitat for the species. The uses of eDNA adds a novel tool to the avian conservation toolbox that can be improved and adapted for other species of concern. The habitat suability model provides a starting point for land management and habitat restoration efforts for Black Rail now and in the future. The information gained using these two techniques can add much needed insight into the range and ecological needs of this imperiled species

Offshore CO2 storage: Sleipner natural gas field beneath the North Sea

Sleipner is the world's longest-running industrial-scale storage project and the first example of underground CO2 storage arising as a direct response to environmental legislation. It began in 1996, injecting around one million tonnes (1 Mt) of CO2 per year into the Utsira Sand, a relatively shallow saline aquifer. By late 2011 over 13 Mt of CO2 had been securely stored. A comprehensive research-focused monitoring programme was carried out with multiple time-lapse surveys; predominantly 3D seismic but also 2D seismic, gravimetry and controlled-source electromagnetics (CSEM). The time-lapse seismic data image the CO2 plume clearly in the reservoir with very high detection capability and show no evidence of CO2 migration from the storage reservoir. Although not specifically designed for this purpose, the monitoring programme fulfils most of the requirements of the recently developed European regulatory framework for CO2 underground storage

Mechanisms for DNA Charge Transport

DNA charge transport (CT) chemistry has received considerable attention by scientific researchers over the past 15 years since our first provocative publication on long range CT in a DNA assembly.1,2 This interest, shared by physicists, chemists and biologists, reflects the potential of DNA CT to provide a sensitive route for signaling, whether in the construction of nanoscale biosensors or as an enzymatic tool to detect damage in the genome. Research into DNA CT chemistry began as a quest to determine whether the DNA double helix, a macromolecular assembly in solution with π-stacked base pairs, might share conductive characteristics with π-stacked solids. Physicists carried out sophisticated experiments to measure the conductivity of DNA samples, but the means to connect discrete DNA assemblies into the devices to gauge conductivity varied, as did the conditions under which conductivities were determined. Chemists constructed DNA assemblies to measure hole and electron transport in solution using a variety of hole and electron donors. Here, too, DNA CT was seen to depend upon the connections, or coupling, between donors and the DNA base pair stack. Importantly, these experiments have resolved the debate over whether DNA CT is possible. Moreover these studies have shown that DNA CT, irrespective of the oxidant or reductant used to initiate the chemistry, can occur over long molecular distances but can be exquisitely sensitive to perturbations in the base pair stack. Here we review some of the critical characteristics of DNA charge transport chemistry, taking examples from a range of systems, and consider these characteristics in the context of their mechanistic implications. This review is not intended to be exhaustive but instead to be illustrative. For instance, we describe studies involving measurements in solution using pendant photooxidants to inject holes, conductivity studies with covalently modified assemblies, and electrochemical studies on DNA-modified electrodes. We do not focus in detail on the differences amongst these constructs but instead on their similarities. It is the similarity among these various systems that allows us to consider different mechanisms to describe DNA CT. Thus we review also the various mechanisms for DNA CT that have been put forth and attempt to reconcile these mechanistic proposals with the many disparate measurements of DNA CT. Certainly the debate among researchers has shifted from "is DNA CT possible?" to "how does it work?". This review intends to explore this latter question in detail

Space Time MUSIC: Consistent Signal Subspace Estimation for Wide-band Sensor Arrays

Wide-band Direction of Arrival (DOA) estimation with sensor arrays is an essential task in sonar, radar, acoustics, biomedical and multimedia applications. Many state of the art wide-band DOA estimators coherently process frequency binned array outputs by approximate Maximum Likelihood, Weighted Subspace Fitting or focusing techniques. This paper shows that bin signals obtained by filter-bank approaches do not obey the finite rank narrow-band array model, because spectral leakage and the change of the array response with frequency within the bin create \emph{ghost sources} dependent on the particular realization of the source process. Therefore, existing DOA estimators based on binning cannot claim consistency even with the perfect knowledge of the array response. In this work, a more realistic array model with a finite length of the sensor impulse responses is assumed, which still has finite rank under a space-time formulation. It is shown that signal subspaces at arbitrary frequencies can be consistently recovered under mild conditions by applying MUSIC-type (ST-MUSIC) estimators to the dominant eigenvectors of the wide-band space-time sensor cross-correlation matrix. A novel Maximum Likelihood based ST-MUSIC subspace estimate is developed in order to recover consistency. The number of sources active at each frequency are estimated by Information Theoretic Criteria. The sample ST-MUSIC subspaces can be fed to any subspace fitting DOA estimator at single or multiple frequencies. Simulations confirm that the new technique clearly outperforms binning approaches at sufficiently high signal to noise ratio, when model mismatches exceed the noise floor.Comment: 15 pages, 10 figures. Accepted in a revised form by the IEEE Trans. on Signal Processing on 12 February 1918. @IEEE201

Radiation-Induced Error Criticality in Modern HPC Parallel Accelerators

In this paper, we evaluate the error criticality of radiation-induced errors on modern High-Performance Computing (HPC) accelerators (Intel Xeon Phi and NVIDIA K40) through a dedicated set of metrics. We show that, as long as imprecise computing is concerned, the simple mismatch detection is not sufficient to evaluate and compare the radiation sensitivity of HPC devices and algorithms. Our analysis quantifies and qualifies radiation effects on applications’ output correlating the number of corrupted elements with their spatial locality. Also, we provide the mean relative error (dataset-wise) to evaluate radiation-induced error magnitude. We apply the selected metrics to experimental results obtained in various radiation test campaigns for a total of more than 400 hours of beam time per device. The amount of data we gathered allows us to evaluate the error criticality of a representative set of algorithms from HPC suites. Additionally, based on the characteristics of the tested algorithms, we draw generic reliability conclusions for broader classes of codes. We show that arithmetic operations are less critical for the K40, while Xeon Phi is more reliable when executing particles interactions solved through Finite Difference Methods. Finally, iterative stencil operations seem the most reliable on both architectures.This work was supported by the STIC-AmSud/CAPES scientific cooperation program under the EnergySFE research project grant 99999.007556/2015-02, EU H2020 Programme, and MCTI/RNP-Brazil under the HPC4E Project, grant agreement n° 689772. Tested K40 boards were donated thanks to Steve Keckler, Timothy Tsai, and Siva Hari from NVIDIA.Postprint (author's final draft

SMAN : Stacked Multi-Modal Attention Network for cross-modal image-text retrieval

This article focuses on tackling the task of the cross-modal image-text retrieval which has been an interdisciplinary topic in both computer vision and natural language processing communities. Existing global representation alignment-based methods fail to pinpoint the semantically meaningful portion of images and texts, while the local representation alignment schemes suffer from the huge computational burden for aggregating the similarity of visual fragments and textual words exhaustively. In this article, we propose a stacked multimodal attention network (SMAN) that makes use of the stacked multimodal attention mechanism to exploit the fine-grained interdependencies between image and text, thereby mapping the aggregation of attentive fragments into a common space for measuring cross-modal similarity. Specifically, we sequentially employ intramodal information and multimodal information as guidance to perform multiple-step attention reasoning so that the fine-grained correlation between image and text can be modeled. As a consequence, we are capable of discovering the semantically meaningful visual regions or words in a sentence which contributes to measuring the cross-modal similarity in a more precise manner. Moreover, we present a novel bidirectional ranking loss that enforces the distance among pairwise multimodal instances to be closer. Doing so allows us to make full use of pairwise supervised information to preserve the manifold structure of heterogeneous pairwise data. Extensive experiments on two benchmark datasets demonstrate that our SMAN consistently yields competitive performance compared to state-of-the-art methods

Robust Detection of Rare Species Using Environmental DNA: the Importance of Primer Specificity

Environmental DNA (eDNA) is being rapidly adopted as a tool to detect rare animals. Quantitative PCR (qPCR) using probe-based chemistries may represent a particularly powerful tool because of the method\u27s sensitivity, specificity, and potential to quantify target DNA. However, there has been little work understanding the performance of these assays in the presence of closely related, sympatric taxa. If related species cause any cross-amplification or interference, false positives and negatives may be generated. These errors can be disastrous if false positives lead to overestimate the abundance of an endangered species or if false negatives prevent detection of an invasive species. In this study we test factors that influence the specificity and sensitivity of TaqMan MGB assays using co-occurring, closely related brook trout (Salvelinus fontinalis) and bull trout (S. confluentus) as a case study. We found qPCR to be substantially more sensitive than traditional PCR, with a high probability of detection at concentrations as low as 0.5 target copies/ml. We also found that number and placement of base pair mismatches between the Taqman MGB assay and non-target templates was important to target specificity, and that specificity was most influenced by base pair mismatches in the primers, rather than in the probe. We found that insufficient specificity can result in both false positive and false negative results, particularly in the presence of abundant related species. Our results highlight the utility of qPCR as a highly sensitive eDNA tool, and underscore the importance of careful assay design

Development and application of environmental DNA surveillance for the threatened crucian carp (Carassius carassius)

The crucian carp (Carassius carassius ) is one of few fish species associated with small ponds in the UK. These populations contain genetic diversity not found in Europe and are important to conservation efforts for the species which has declined across its range in Europe. Detection and monitoring of extant crucian carp populations are crucial for conservation success. Environmental DNA (eDNA) analysis could be very useful in this respect as a rapid, cost‐efficient monitoring tool. We developed a species‐specific quantitative polymerase chain reaction (qPCR) assay for eDNA surveillance of crucian carp to enable non‐invasive, large‐scale distribution monitoring. We compared fyke netting and eDNA analysis at ponds with (n = 10) and without (n = 10) crucian carp for presence–absence detection. We examined biotic (crucian carp density represented by catch‐per‐unit‐effort [CPUE] estimate) and abiotic influences on eDNA detection probability using a hierarchical occupancy model, and eDNA quantification using a mixed‐effects model. eDNA analysis achieved 90% detection for crucian carp (n = 10), failing in only one pond where presence was known. CPUE estimate and conductivity had positive and negative influences on eDNA detection probability in qPCR replicates respectively. Similarly, conductivity had a negative effect on DNA copy number, whereas copy number increased with CPUE estimate. Our results demonstrate that eDNA analysis could enable detection of crucian carp populations in ponds and benefit ongoing conservation efforts, but imperfect species detection in relation to biotic and abiotic factors and eDNA workflow requires further investigation. Nonetheless, we have established an eDNA framework for the crucian carp as well as sources of imperfect detection which future investigations can build upon

Environmental DNA Monitoring of Non-Native Mudpuppy (Necturus Maculosus) and Transient Rainbow Smelt (Osmerus Mordax)

Whether considering an expanding non-native species or a priority native species with a dwindling local population, the monitoring of low-abundance, sporadically distributed, or otherwise elusive populations, can prove difficult. In separate studies, we tested the viability of environmental DNA (eDNA) for monitoring a species in both of the above circumstances, the common mudpuppy (Necturus maculosus), a spreading non-native species, and rainbow smelt (Osmerus mordax), a declining species of concern. Mudpuppy are fully aquatic salamanders that were introduced to the Belgrade region of central Maine in 1939 and again in 1940. Though they had been present for nearly 80 years when this study began, their ecological impacts and secondary spread have not been well documented. Following a year of trapping through the winter ice, eDNA methods were added concurrently with traditional trapping techniques to demine if detection could be improved in order to better document secondary spread and estimate abundance. Overall, eDNA was helpful in this effort as mudpuppy were detected in all but one waterbody where they were trapped and in two where they were not. Occupancy models were used to estimate survey power and sampling efforts for 95% probability of detection based on our data. Trapping and eDNA showed comparable power at the level of lake regions and number of sampling holes. However, when looking at the level of technical replicates, trap data required 6.4 replicates (trapping events) while eDNA required 10.9 (qPCR replicates). However, the amount of work and expense to obtain qPCR replicates is likely less than to implement additional days of trapping. Trap and eDNA sampling depth data were also used to gain preliminary insight on environmental preferences. Kologorov Smirnoff tests comparing overall depth distribution and individual mudpuppy caught at a given trap site did not reveal an observable trend in depth preferences. T-tests revealed a modest preference for 4-8m depths, but this was likely due to depths available in study sites as opposed to true biological preference. Overall, the combined results of trapping and eDNA sampling both suggest that the mudpuppy invasion has been relatively gradual, and provided baseline occupancy information for potential future assessments of range expansion. In the second study of this thesis, we assessed eDNA as a means to monitor anadromous rainbow smelt (Osmerus mordax), a species of special conservation concern in Maine. As anadromous fish, rainbow smelt migrate up streams and rivers to spawn during the early spring period when typical nighttime visual surveys can be difficult or even dangerous. As such, the current use of many coastal streams for spawning is poorly known. We hypothesized that eDNA might facilitate improved survey efforts to define smelt spawning habitat. However, the lotic environments and behavior of smelt present potential challenges for eDNA. Rainbow smelt often enter smaller streams at night and depart by morning, such that fish eDNA might be flushed out of the system relatively quickly. By combining daytime eDNA sampling with fyke netting, we confirmed that smelt eDNA could be detected up to weeks following peak spawning events. Indeed, there was some evidence that concentration of eDNA (copies/L) rose over the approximately 8-13 days following spawning events, suggesting developing and hatching smelt larvae might be the primary source of residual eDNA. Adding to this study, we conducted eDNA surveys in four streams of varying smelt abundance and estimated sampling effort for 95% detection probability using occupancy modelling. Ultimately, results suggested that at the stream with least detections, sampling effort involving collection of three water samples, collected on three days, and analyzed with six qPCR replicates would provide ≥ 95% detection probability. Comparing those recommendations to the sampling design used in this study, the number of qPCR replicates used was the only sampling value below our generated recommendations. These results demonstrate that eDNA methods can be effective for monitoring smelt in lotic systems during their breeding period, particularly with a modest increase in sample processing effort to increase detection probabilities