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Presentation to be presented during the Site-Directed Research and Development (SDRD) program FY 2021 review meeting (Webex), September 22–23, 2021.
Abstract Environmental DNA (eDNA) sampling is increasingly used in surveys of species distribution as a potentially sensitive and efficient monitoring method. Yet access to modelling tools designed specifically for interpreting this new data type lags behind its ubiquity. While occupancy modelling software has dominated the analytical landscape for eDNA data analysis of single species, this type of model may not always be the most appropriate. The rate of eDNA detection often corresponds to species density, rather than just occupancy, and researchers often have access to observations from non‐genetic sampling methods at the same sites. To provide users access to a modelling framework designed to maximize the use of all available data, we developed an R package, eDNAjoint . The package provides an easy‐to‐use interface for fitting a ‘joint’ model that integrates data from paired or semi‐paired eDNA and traditional surveys in a Bayesian framework. The model can be used to estimate parameters like the probability of a false positive eDNA detection and mean catch rate at a site, and the package allows access to multiple model variations and Bayesian prior customization. Additional functionality can be used for model selection, summarising posteriors and comparing the relative sensitivities of the two survey methods. We demonstrate the use of eDNAjoint by fitting a variation of the model with site‐level covariates that scale the sensitivity of eDNA sampling relative to traditional sampling. The example workflow uses binary eDNA and seine count data for the endangered tidewater goby ( Eucyclogobius newberryi ) from a study by Schmelzle and Kinziger (2016). This use case includes a prior sensitivity analysis and an evaluation of the relationship between detection rates and environmental variables. eDNAjoint has the potential to greatly increase the range of users who will be able to rigorously analyse eDNA and traditional survey data in a Bayesian framework, understand if and how eDNA can improve monitoring practices, and gain confidence in the interpretability of eDNA data.
The APG (Optical Scientific Inc. All-Precipitation Gauge 815-DS) dataset contains one-minute measurements of precipitation rate, precipitation accumulation, air temperature, and present weather detection, both in 4680 format and decoded. Data were collected at the Argonne Testbed for Multiscale Observational Science (ATMOS), a 20-acre prairie site at Argonne National Laboratory in Lemont, Illinois. The data is presented as daily NetCDF (.nc) files, each containing approximately 24 hours of observations. Files follow the naming convention of: the project (CROCUS), location (atmos), instrument name (apg), data level (raw, a1), and date (year, month, day). The NetCDF format can be accessed using common scientific software such as Python using xarray, netCDF4 or act-doe.
The objective of the subtask was to develop a near-real-time monitoring system for seismic data at the Decatur, IL, geologic carbon sequestration (GCS) site and specifically include fiber-optic cable derived distributed acoustic signal (DAS) data in the process. Owing to the large volumes of data, we opted to utilize existing deep borehole conventional seismic sensors for detection and pull DAS and shallow borehole seismic data once a detection has been made. Unfortunately, the horizontal fiber-optic cables did not yield microseismic signals for use in locating events near the GCS site. Various stacking and filtering approaches were tested without any coherent detection becoming apparent. We attribute the insensitivity to local microseismic events to a lack of coupling in downhole, vertical cable and the non-ideal alignment of the horizontal fiber-optic cable to the vertically polarized seismic energy. Despite the inability to detect and utilize the DAS data from the fiber-optic cables, we developed a general processing framework that enables easy adaptation for future deployment of fiber-optic cables with better suited alignment at Decatur or elsewhere.
The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks.
Snow is a significant challenge for photovoltaic (PV) systems at northern latitudes, where the pace of deployment is rapid but snow-related power losses can exceed 30% of annual production. Accurate snow-related power loss estimation methods for utility-scale sites can support snow mitigation strategies, inform resource planning and validate predictive snow-loss models. This study builds on our previous work on inverter-based detection of snow, and its implications for utility-scale power production, by validating the accuracy of our snow-loss method across different PV sites and system designs and highlighting its value in bringing greater visibility to PV plant operations in winter. Our estimation method is both novel and scalable, requiring only standard monitoring data to correlate snow-related losses with meteorological data. As demonstrated here, our validation method involved three main steps: 1) estimation of performance losses for multiple systems by comparing measured inverter data to modeled data; 2) application of a detection framework to identify which performance losses are snow-related; and 3) comparison of snow-related losses among three utility-scale sites differing in tilt angle. Results show that utility-scale systems at higher tilt angles consistently shed snow more quickly/completely than their lower-tilt counterparts. Further, monthly and seasonal snow losses are inversely and non-linearly correlated with tilt angle when normalized for cumulative snowfall. These results are consistent with the findings of previous studies and support the broad applicability of this method to fixed-tilt utility-scale PV systems around the world that routinely experience snow-related performance losses.
Nuclear pore complexes (NPCs) are the main conduits for molecular exchange across the nuclear envelope. The NPC is a modular assembly of ~500 individual proteins, called nucleoporins or nups. Most scaffolding nups are organized in two multimeric subcomplexes, the Nup84 or Y complex and the Nic96 or inner ring complex. Working in S. cerevisiae, and to study the assembly of these two essential subcomplexes, we here develop a set of twelve nanobodies that recognize seven constituent nucleoporins of the Y and Nic96 complexes. These nanobodies all bind specifically and with high affinity. We present structures of several nup-nanobody complexes, revealing their binding sites. Additionally, constitutive expression of the nanobody suite in S. cerevisiae detect accessible and obstructed surfaces of the Y complex and Nic96 within the NPC. Overall, this suite of nanobodies provides a unique and versatile toolkit for the study of the NPC.
A review of prior bat records in literature and museum collections for the five counties around Site 300 (Alameda, Contra Costa, San Joaquin, Santa Clara, and Stanislaus) identified 1038 specimens of 15 species. No roosting bats or bat sign indicating roosting were observed in examining samples of structures on Site 300 both day and night. Acoustic monitoring and visual observation detected seven to nine species, including three special status species -- the pallid bat, Antrozous pallidus, the western red bat, Lasiurus blossevillii and the Yuma myotis, Myotis yumanensis. An additional special status species, the long-legged myotis, Myotis volans was possibly present. As a consequence of the windy conditions typically encountered, no bats were caught in mist net sampling, though simultaneous acoustic monitoring demonstrated bats were present at those locations.
The Escherichia coli CheY protein belongs to a large bacterial response regulator superfamily. X-ray hydroxy radical foot-printing with mass spectroscopy (XFMS) has shown that allosteric activation of CheY by its motor target triggers a concerted internalization of aromatic sidechains. We reanalyzed the XFMS data to compare polar versus non-polar CheY residue positions. The polar residues around and including the 57D phosphorylated site had an elevated hydroxy radical reactivity. Bioinformatic measures revealed that a water-mediated hydrogen bond network connected this ring of residues with the central 57D. These residues solvated 57D to energetically stabilize the apo-CheY fold. The abundance of these reactive residues was reduced upon activation. This result was supported by the bioinformatics and consistent with the previously reported activation-induced increase in core hydrophobicity. It further illustrated XFMS detection of structural waters. Direct contacts between the ring residues and the phosphorylation site would stabilize the aspartyl phosphate. In addition, we report that the ring residue, 18R, is a constant central node in the 57D solvation network and that 18R non-polar substitutions determine CheY diversity as assessed by its evolutionary trace in bacteria with well-studied chemotaxis. These results showcase the importance of structured water dynamics for phosphorylation-mediated signal transduction.
Vesicular stomatitis virus (VSV), comprising vesicular stomatitis New Jersey virus (VSNJV) and vesicular stomatitis Indiana virus (VSIV), emerges from its focus of endemic transmission in Southern Mexico to cause sporadic livestock epizootics in the Western United States. A dearth of information on the role of potential arthropod vectors in the endemic region hampers efforts to identify factors that enable endemicity and predict outbreaks. In a two-year, longitudinal study at five cattle ranches in Chiapas, Mexico, insect taxa implicated as VSV vectors (blackflies, sandflies, biting midges, and mosquitoes) were collected and screened for VSV RNA, livestock vesicular stomatitis (VS) cases were monitored, and serum samples were screened for neutralizing antibodies. VS cases were reported during the rainy (n = 20) and post-rainy (n = 2) seasons. Seroprevalence against VSNJV in adult cattle was very high (75–100% per ranch) compared with VSIV (0.6%, all ranches). All four potential vector taxa were sampled, and VSNJV RNA was detected in each of them (11% VSNJV-positive of 874 total pools), while VSIV RNA was only detected in four pools of mosquitoes. Our findings indicate that VSNJV is the dominant serotype across our sampling sites with a variety of potential insect vectors involved in its transmission throughout the year. Although no livestock cases were reported in Chiapas during the dry season, VSNJV was detected in insects during this period, suggesting that mechanisms other than transmission from livestock support VSV endemicity.
Owing to its roles in cellular signal transduction, protein phosphorylation plays critical roles in myriad cell processes. That said, detecting and quantifying protein phosphorylation has remained a challenge. We describe the use of a novel mass spectrometer (Orbitrap Astral) coupled with data-independent acquisition (DIA) to achieve rapid and deep analysis of human and mouse phosphoproteomes. With this method, we map approximately 30,000 unique human phosphorylation sites within a half-hour of data collection. The technology is benchmarked to other state-of-the-art MS platforms using both synthetic peptide standards and with EGF-stimulated HeLa cells. We apply this approach to generate a phosphoproteome multi-tissue atlas of the mouse. Altogether, we detect 81,120 unique phosphorylation sites within 12 hours of measurement. With this unique dataset, we examine the sequence, structural, and kinase specificity context of protein phosphorylation. Finally, we highlight the discovery potential of this resource with multiple examples of phosphorylation events relevant to mitochondrial and brain biology.
Studying bird, insect, and other wildlife interactions with photovoltaic (PV) solar energy facilities is difficult due to limited multi-season, multi-site data. Researchers can address such data gaps by combining passive monitoring and artificial intelligence (AI). As a part of the development of AI-enabled avian–solar monitoring software, we collected over 19,000 h of daytime videos at five PV sites across three U.S. regions between 2019 and 2024. We applied a moving object detection and tracking (MODT Version 1) AI model we developed earlier to 4373 h of the footage to extract moving objects in video frames, and human reviewers interpreted the model output and identified 68,646 bird, 25,968 insect, and 169 other wildlife instances to generate the training/validation dataset. We analyzed the data by site, region, and season, considering ground cover and landscapes. Songbirds were most common, with raptors as the next most frequent group. Most notably, no bird collisions were confirmed in our observations collected from the videos. Birds most often flew over or near panels, with the highest observations in the Midwest and Northeast (approximately 30 observations per hour on average) and fewer in the desert Southwest. Other behaviors included perching, foraging, and nesting. Bird abundance peaked during breeding and migration seasons. AI-assisted video monitoring proved effective for non-invasively studying flying wildlife at solar facilities to inform ecologically mindful energy development.
Networks of fixed-point continuous monitoring systems are becoming widely used in the detection and quantification of methane emissions from oil and gas facilities in the United States. Regulatory agencies and operators are developing performance metrics for these systems, such as minimum detection limits. Performance characteristics, such as minimum detection limits, would ideally be expressed in emission rate units; however, performance parameters such as detection limits for a continuous monitoring system (CMS) will depend on meteorological conditions, the characteristics of emissions at the site where the CMS is deployed, the positioning of CMS devices in relation to the emission sources, and the amount of time allowed for the CMS to detect an emission source. This means that certifying the performance of a CMS will require test protocols with well-defined emission rates and durations; initial protocols are now being used in field tests. Field testing results will vary, however, depending on meteorological conditions and the time allowed for detection. This work demonstrates methods for evaluating CMS performance characteristics using dispersion modeling and defines an approach for normalizing test results to standard meteorological conditions using dispersion modeling.
The Plowshare Program was initiated by the Atomic Energy Commission, a predecessor agency to the US Department of Energy (DOE), to investigate peaceful uses of nuclear devices. 'Vela Uniform,' part of the broader Project Vela, was designed to use nuclear and other explosions to increase the capability of the United States to detect nuclear tests through seismicity. There were underground nuclear tests conducted off the Nevada National Security Site (NNSS) as part of the Plowshare Program (e.g., the Gnome-Coach, New Mexico, Site) and the Vela Uniform Program (e.g., Project Shoal, Nevada, Site) at which the DOE Office of Legacy Management (LM) has been conducting long-term surveillance and maintenance since fiscal year (FY) 2006. There are many additional locations, particularly for the Plowshare Program, that were considered for nuclear tests or used for non-nuclear tests off the NNSS. Some were only named on paper, but at 30 sites, field work such as drilling test wells and conducting tests using conventional explosives was done. A Plowshare nuclear explosion had been proposed at Project Chariot but was canceled in 1962. In 1992, the discovery of soils contaminated with radionuclides from tracer experiments led to a cleanup by the DOE Office of Environmental Management (EM) of the Project Chariot, Alaska, Site, which was completed in 1994. Following the cleanup of the Project Chariot site, EM was concerned that there might be environmental liabilities at other Plowshare or Vela Uniform sites where field investigations were done, even if the nuclear test was never conducted. The EM Program at the Nevada Site Office began examining records and visiting selected locations that were evaluated for Plowshare and Vela Uniform non-nuclear tests. In total, 161 Plowshare and 14 Vela Uniform sites or proposed projects were identified. The Desert Research Institute (DRI) created summaries of the work carried out at these sites and developed a collection of unclassified documents for them. Preliminary investigations had been conducted at these sites by both LM and EM since the early 2000's. In 2016, in preparation for accepting responsibility for the sites, LM started a program to determine if there were any environmental or safety liabilities associated with them. In 2019, LM formally accepted responsibility for these sites off the NNSS where non-nuclear explosive tests were conducted or where underground nuclear tests were proposed as part of the Plowshare and Vela Uniform programs but were never conducted. LM responsibilities will include maintaining institutional knowledge of the sites, responding to public inquiries, and addressing any concerns at each site. To minimize risk associated with site management, LM has archived all historical records compiled by DRI and has performed limited investigations at sites identified by the prior studies as having a higher potential liability. Preserving records related to site operations reduces the risk of doing unnecessary site investigations in the future caused by the loss of institutional knowledge. During the investigations, some maintenance needs were identified and will be addressed by the end of FY 2021. At the end of FY 2021, investigations, maintenance, and archiving activities will be complete. Remnants of test activities found at sites include craters, mud pits, debris, and open boreholes. Open boreholes are being plugged and abandoned in accordance with state regulatory standards. Other issues are being addressed in accordance with land owner requests and preferences as well as state regulations. Archiving activities include the development of project summaries identifying key operational details and current condition to allow site managers to be easily briefed. The LM experience at non-nuclear Plowshare and Vela Uniform sites applies to other programs where there is the potential for liability associated with the non-nuclear aspects of nuclear programs. This may include support activities or early phases of planned nuclear projects. Preserving institutional knowledge enables land managers to adequately address any concerns associated with operations and lowers risk. (authors)
In this work, high-energy, wide-angle x-ray scattering (WAXS, x-ray diffraction) and small-angle x-ray scattering (SAXS) were used to study intact human second metacarpal bones (mc2) from two UK archeological sites. A novel method correcting for irregular mass distribution was applied in these transmission geometry experiments done at beamline 1-ID of the Advanced Photon Source. The authors asked whether there were age-at-death-related changes in carbonated apatite (cAp) lattice parameters and whether SAXS could detect collagen D-period peaks in the archeological mc2. For each of the two sites, Ancaster and Wharram Percy in England, six female mc2s were studied; for each site, two were from each of three age-at-death cohorts (young, 18-29 years; middle, 30-49 years; old >= 50 years) along with a modern control mc2. The Rietveld method was applied to the WAXS patterns to provide precise lattice parameter values. The cAp lattice parameters did not correlate with age-at-death estimated from dental wear. From WAXS and the 00.2 diffraction peak widths, four archeological mc2s possessed coherently scattering domain lengths (crystallite c-axis sizes) that matched that of the modern mc2; SAXS revealed the same four archeological mc2 had D-period peak intensities equivalent to that of the modern mc2. The other eight archeological mc2s had significantly larger crystallite sizes (than the modern mc2) and weak or absent D-period peaks, differences attributed to diagenetic changes. Based on these data, the authors suggest that WAXS 00.2 peak width and SAXS D-period peak intensity can be used with intact bones to select those likely to retain largely unaltered tissue nanostructure, which might be required for other analyses. Taken as a whole, the results suggest detecting age-related deterioration in nanostructural features may be difficult in bone showing significant bioerosion.
Los Alamos National Laboratory (LANL or Laboratory) is committed to solving national security challenges through scientific excellence and has been serving the nation and northern New Mexico for over 70 years. Being located on the Pajarito Plateau in the eastern flanks of the Jemez Mountains, the Laboratory is surrounded by a rich diversity of plants and animals. It is common to see many different species of wildlife on Laboratory property; however, sometimes interactions with wildlife can be negative. Vehicle accidents with wildlife have become a common occurrence. With the current and ongoing expansion of the Laboratory on the Pajarito Plateau, it has the potential to further impact wildlife movement including large game species. Local agencies and tribal Pueblos rely on large game species and do not want these species to be restricted from moving across property boundaries. Temporal and spatial aspects of where wildlife occur on the site is a phenomena that is either not well understood in uncommon species or needs periodic reevaluation for common species. Estimating the distribution of multiple species across the landscape provides wildlife biologists with crucial information for monitoring and conserving animal populations in a particular area. Utilizing motion activated wildlife cameras, also known as camera traps, to monitor wildlife populations has become an essential tool for biologists. Camera traps are non-invasive and cost-effective and can document multiple elusive or uncommon wildlife species, such as carnivores, simultaneously. Occupancy modeling provides a flexible framework for the analysis of the distribution for multiple wildlife species. It explicitly recognizes whether a species is spatially common or rare (occupancy = ψ) and if that species is easy or hard to detect (detection probability = p ). Multispecies and multi-season occupancy models can detect trends in species occupancy because individual species may vary in seasonal movements, detection probability, and transition rates between habitats. In this study, we assessed the site as a whole to ascertain when and where medium and large mammal species are present. Understanding wildlife patterns at the Laboratory will better inform future management decisions regarding land use and development strategies. We placed motion activated wildlife cameras in a random systematic sampling design and used these data to create occupancy models. We tested for differences in single-species occupancy and detection probability by season of mammal species captured on 20 camera traps placed across the Laboratory in a 40 mi² (103 km²) area. We focus the interpretation of our findings on seven mammal species found during this study. They are Rocky Mountain elk ( Cervus canadensis nelsoni ; hereafter “elk”), mule deer ( Odocoileus hemionus ; hereafter “deer”), mountain lion ( Puma concolor ; hereafter “lion”), American black bear ( Ursus americanus ; hereafter “bear”), coyote ( Canis latrans ), bobcat ( Lynx rufus ), and gray fox ( Urocyon cinereoargenteus ; hereafter “fox”).
Single-atom nanozymes (SANs) possess unique features of maximum atomic utilization and present highly assembled enzyme-like structure and remarkable enzyme-like activity. By introducing SANs into immunoassay, limitations of ELISA such as low stability of horseradish peroxidase (HRP) can be well addressed, thereby improving the performance of the immunoassays. In this work, we have developed novel Fe-N-C single-atom nanozymes (Fe-N x SANs) derived from Fe-doped polypyrrole (PPy) nanotube and substituted the enzymes in ELISA kit for enhancing the detection sensitivity of amyloid beta 1-40. Results indicate that the Fe-N x SANs contain high density of single-atom active sites and comparable enzyme-like properties as HRP, owing to the maximized utilization of Fe atoms and their abundant active sites, which could mimic natural metalloproteases structures. Further designed SAN-linked immunosorbent assay (SAN-LISA) demonstrates the ultralow limit of detection (LOD) of 0.88 pg/mL, much more sensitive than that of commercial ELISA (9.98 pg/mL). The results confirm that the Fe-N x SANs can serve as a satisfactory replacement of enzyme labels, which show great potential as an ultrasensitive colorimetric immunoassay.
Emerging pathogenic fungi have become a topic of conservation concern due to declines observed in several host taxa. One emerging fungal pathogen, Ophidiomyces ophidiicola, is well documented as the causative agent of ophidiomycosis, otherwise known as snake fungal disease (SFD). O. ophidiicola has been found to cause disease in a variety of snake species across the United States, including the eastern massasauga (Sistrurus catenatus), a federally threatened rattlesnake species. Most work to date has involved detecting O. ophidiicola for diagnosis of infection through direct sampling of snakes, and attempts to detect O. ophidiicola in the abiotic environment to better understand its distribution, seasonality, and habitat associations are lacking. We collected topsoil and groundwater samples from four macrohabitat types across multiple seasons in northern Michigan at a site where Ophidiomyces infection has been confirmed in eastern massasauga. Using a quantitative PCR (qPCR) assay developed for O. ophidiicola, we detected Ophidiomyces DNA in topsoil but observed minimal to no detection in groundwater samples. Detection frequency did not differ between habitats, but samples grouped seasonally showed higher detection during mid-summer. We found no relationships of detection with hypothesized environmental correlates such as soil pH, temperature, or moisture content. Furthermore, the distribution of Ophidiomyces positive samples across the site was not linked to estimated space use of massasaugas. Our data suggests that season has some effect on the presence of Ophidiomyces. Differences in presence between habitats may exist but are likely more dependent on the time of sampling and currently uninvestigated soil or biotic parameters. These findings build on our understanding of Ophidiomyces ecology and epidemiology to help inform where and when snakes may be exposed to the fungus in the environment.