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We leverage the Kellogg Biological Station Long Term Ecological Research Main Cropping System Experiment (KBS MCSE) field experiment to test trade-offs among high growth yield (Y), resource acquisition (A), and stress tolerance (S) traits using metagenomic data from plots with different land use, drought manipulation, and C addition.
We leverage the Kellogg Biological Station Long Term Ecological Research Main Cropping System Experiment (KBS MCSE) field experiment to test trade-offs among high growth yield (Y), resource acquisition (A), and stress tolerance (S) traits using metagenomic data from plots with different land use, drought manipulation, and C addition.
The National Ignition Facility (NIF) is the world’s largest and most energetic laser facility. The NIF system is designed to produce high energy density (temperature and pressure) conditions through the application of its 192 laser beams. One of the users of NIF is the opacity platform developed to study the opacities at temperatures and densities relevant to the solar interior and stellar evolution. The platform was developed to study iron (Fe) opacity at temperatures relevant to the solar interior. The opacity campaign uses spectrometers to gather data. Spectrometers utilize crystals to produce x-ray spectra that are recorded on time-integrated and time-resolved detectors. The opacity spectrometer (OpSpec) currently fielded and in use at NIF uses a time integrated film channel to collect data. The opacity spectrometer time resolved (OpSpecTR) will utilize novel hCMOS detectors to capture time resolved images of spectra of interest. The key stakeholders identified for OpSpecTR included the physicists responsible for OpSpec and OpSpecTR, the Target Area Science and Engineering (TASE) department at NIF, the NIF and Photon Science (NIF & PS) Opacity program, the Nevada National Security Site (NNSS) Physics and Engineering program, the Sandia hCMOS manufacturing and testing program, and the Los Alamos National Laboratory (LANL) program sponsor. The Target and Experimental Operations (TEXOPS) was identified as a key stakeholder because the group includes the individuals that will physically interact with the OpSpecTR system as it participates in NIF experiments. The opacity platform collects data in a unique orientation relative to the existing diagnostics fielded at NIF. The existing infrastructure at NIF uses a diagnostic manipulator (DIM) to insert the diagnostic near the target chamber center to collect data during a NIF shot. Existing diagnostics collect data through the center line of the DIM axis and collect relevant data perpendicular to this axis. The opacity platform requires crystals mounted in a specific orientation which requires data collection parallel to the DIM axis. This deviation from standard NIF practices was a key factor in developing requirements.
This dataset and R script accompany the published paper Bruns et al. (2024) in Geophysical Research Letters. The data are from the first six years of a field manipulation of whole-ecosystem warming and elevated CO2 experiment (Salt Marsh Accretion Response to Temperature eXperiment, or SMARTX) in the Smithsonian's Global Change Research Wetland (GCReW), a brackish, microtidal wetland site on a subestuary of the Chesapeake Bay. These data were generated to understand how warming and elevated CO2 interact to structure ecosystem-level responses to global change, particularly in terms of carbon sequestration. The dataset covers 2017-2022 and includes peak annual above ground biomass, annual belowground fine root productivity, and porewater NH4 for each experimental plot. The overall experiment is replicated in two locations on the marsh, a lower elevation zone dominated the C3 sedge S. Americanus and a higher elevation plot dominated by the C4 species. This paper and its data release is only for the C3 plot. Variable descriptions for data file is available in variable_descriptions.pdf. The R script Bruns_et_al_2024_GRL_make_figures.Rmd contains model code and other scripts used to generate all paper figures.
Abstract Climatic drying is predicted for many tropical forests yet models remain poorly parameterized for these ecosystems, hampering predictions of forest‐climate interactions. We applied an integrated model–experiment approach, parameterizing an ecosystem model with tropical forest observational data and comparing model predictions to a field drying manipulation. We hypothesized that drying suppresses soil CO 2 fluxes (i.e., respiration) in relatively dry tropical forests but increases CO 2 fluxes in wetter tropical forests by alleviating anaerobiosis. We measured soil CO 2 fluxes during wet‐dry cycles from 2015 to 2022 in four Panamanian forests that vary in rainfall and soil fertility. Measured soil CO 2 fluxes declined in the dry season and peaked in the early wet season ahead of peak soil moisture, resulting in lower soil moisture optima for respiration than previously modeled. We then parameterized the model using field data and the new moisture‐respiration response functions. The updated model predicted increased soil CO 2 fluxes with drying in wetter and fertile forests and suppressed fluxes in drier, infertile forests. In contrast to model predictions, a chronic throughfall exclusion experiment initially suppressed soil respiration across forests, with sustained suppression for four years in the wettest forest only (−28% ± 4% during the dry season). In the fertile forest, drying eventually elevated CO 2 fluxes over this period (+75% ± 28% during the late wet season). The unexpected negative drying effect in the wettest, infertile forest could have resulted from reduced vertical flushing of nutrients into soils. Including hydro‐nutrient interactions in ecosystem models could improve predictions of tropical forest‐climate feedbacks.
In this study, we present PyKrev, a Python library for the analysis of complex mixture Fourier transform mass spectrometry (FT-MS) data. PyKrev is a comprehensive suite of tools for analysis and visualization of FT-MS data after formula assignment has been performed. These comprise formula manipulation and calculation of chemical properties, intersection analysis between multiple lists of formulas, calculation of chemical diversity, assignment of compound classes to formulas, multivariate analysis, and a variety of visualization tools producing van Krevelen diagrams, class histograms, PCA score, and loading plots, biplots, scree plots, and UpSet plots. The library is showcased through analysis of hot water green tea extracts and Scotch whisky FT-ion cyclotron resonance-MS data sets. PyKrev addresses the lack of a single, cohesive toolset for researchers to perform FT-MS analysis in the Python programming environment encompassing the most recent data analysis techniques used in the field.
Climate warming may accelerate decomposition of Arctic soil carbon, but few controlled experiments have manipulated the entire active layer. To determine surface-atmosphere fluxes of carbon dioxide and methane under anticipated end-of-century warming, we used heating rods to warm soil (by 3.8 °C) to the depth of permafrost in polygonal tundra over two growing seasons at the Barrow Environmental Observatory in Utqiaġvik (formerly Barrow), Alaska. This data product includes supporting data for the companion paper entitled "Large emissions of CO2 and CH4 due to active-layer warming in Arctic tundra", published in Nature Communications by Torn et al. This data package contains seven data files in csv format with corresponding data dictionaries and file-level metadata, describing vegetation biomass dry weight ("Utqiagvik_Vegetation_Biomass_2014.csv"), radiocarbon measurements of respired carbon dioxide ("Utqiagvik_Radiocarbon_2015_2016.csv"), surface-atmosphere fluxes of carbon dioxide and methane and associated soil temperature ("Utqiagvik_Fluxes_Temperature_2015_2016.csv"), and soil temperature measurements averaged every 15-min and 1-hr for 2015 and 2016 ("Utqiagvik_Temperature_15min_2015.csv", "Utqiagvik_Temperature_15min_2016.csv", "Utqiagvik_Temperature_1hr_2015.csv", "Utqiagvik_Temperature_1hr_2016.csv"). There are no specific software requirements to use these data. UIC Science Native Corporation facilitated our scientific research on the Barrow Environmental Observatory, which is Iñupiat land.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
The Fermilab Side-Coupled Linac accelerates H- beam from 116 MeV to 400 MeV through seven 805 MHz modules. Twelve wire scanners are present in the Side Coupled Linac and four are present in the transfer line between the Linac and the Booster synchrotron ring. These wire scanners act as important diagnostic instru-ments to directly collect information on the beam s transverse distribution. The manipulation of the condi-tions of wire scanner data collection enables further characterization of the beamline, such as calculating emittance and the Twiss parameters of the beam at select regions. Here we present the results of these studies and characterization of the non-Gaussian transverse beam distribution observed.
Nuclear sites across the Unites States and other nations in the world, are prone to extreme radiation effects. To understand the radiation hazards, plan the cleanup efforts; and to meet the quality standards and guidelines, it is important to accurately characterize any nuclear facility. One of the current methods in characterizing facilities is the use of advanced autonomous systems. These are safe, efficient, and cost-effective tools, which can safely deploy state-of-the-art instrumentation without exposing workers to radiation risks. Conventional methods of taking radiation measurements by hand within or around the containment areas, and analyzing the collected data to obtain the result are: Ineffective, Puts scientists at risk of Radiation, Not cost-effective. The platform uses the Robotic Operating Systems (ROS) to integrate multiple sensors seamlessly. It can operate near real time by using a distributed processing system to handle large amounts of data: Surrounding cameras, A multi-channel 3D Lidar, Manipulator arm, High-performance gamma-ray spectrometer.
This thesis presents the development of a laboratory-scale cyber–physical midstream pipeline testbed designed to address this gap and support research in industrial control systems security. The platform integrates pumps, valves, sensors, programmable logic controllers (PLCs), and a human–machine interface (HMI) to emulate the monitoring and control architecture of real pipeline operations. The physical process is implemented as a closed-loop liquid circulation system designed to replicate flow behavior characteristic of midstream pipeline infrastructure. The testbed enables real-time data acquisition of key process variables, including flow rate and pressure facilitating the generation of datasets representative of normal pipeline operation. A threat model encompassing common ICS attack vectors was developed, including sensor spoofing, command injection, false data injection, denial-of-service attacks, and relay manipulation. Multiple attack scenarios were implemented and evaluated to demonstrate how cyber intrusions targeting sensors, actuators, networks, and software propagate into measurable physical consequences in pipeline flow and pressure. The developed platform serves as a practical, cost-effective environment for experimentation, education, and future cybersecurity research in midstream pipeline systems.
Abstract Predicting vegetation phenology in response to changing environmental factors is key in understanding feedbacks between the biosphere and the climate system. Experimental approaches extending the temperature range beyond historic climate variability provide a unique opportunity to identify model structures that are best suited to predicting phenological changes under future climate scenarios. Here, we model spring and autumn phenological transition dates obtained from digital repeat photography in a boreal Picea ‐ Sphagnum bog in response to a gradient of whole ecosystem warming manipulations of up to +9°C, using five years of observational data. In spring, seven equally best‐performing models for Larix utilized the accumulation of growing degree days as a common driver for temperature forcing. For Picea , the best two models were sequential models requiring winter chilling before spring forcing temperature is accumulated. In shrub, parallel models with chilling and forcing requirements occurring simultaneously were identified as the best models. Autumn models were substantially improved when a CO 2 parameter was included. Overall, the combination of experimental manipulations and multiple years of observations combined with variation in weather provided the framework to rule out a large number of candidate models and to identify best spring and autumn models for each plant functional type.
This archive contains data used to support conclusions drawn in “Ecophysiological variation in two provenances of Pinus flexilis seedlings across an elevation gradient from forest to alpine”, by Reinhardt et al., 2011. Data were collected over one summer season in plots within the Alpine Treeline Warming Experiment (ATWE), before climate manipulations began. The experiment was located on Niwot Ridge, in the Front Range of the Colorado Rocky Mountains. This data package includes five comma-separated-values (.csv) files, five Microsoft Excel (.xlsx) files, one .pdf file, and two types of geospatial files: keyhole markup language (.kml), and ESRI shapefiles (.shp). .csv files can be opened using any simple text-editing software (such as Notepad and TextEdit), R, and Microsoft Excel. .xlsx files can only be opened using Microsoft Excel. The .pdf file can be opened using Adobe Acrobat Reader or any other compatible file viewing software. The .kml file can be opened using Google Earth and Google Maps, and shapefiles can be opened using any software compatible with the file type, such as ESRI’s ArcGIS suite and QGIS.Data archived contain gas exchange and plant physiology measurements, non-structural carbohydrate data, among others. Geospatial files are also provided for additional locational context. The files and their contents in this data package are summarized under "Data Summary" in the included Data User's Guide. All files (excluding geospatial) are available in both Microsoft Excel and in .csv format, and are indicated in the Data Summary list as well.-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Climate change is predicted to cause upward shifts in forest tree distributions, which will require seedling recruitment beyond current forest boundaries. However, predicting the likelihood of successful plant establishment beyond current species’ ranges under changing climate is complicated by the interaction of genetic and environmental controls on seedling establishment. To determine how genetics and climate may interact to affect seedling establishment, we transplanted recently germinated seedlings from high- and low-elevation provenances (HI and LO, respectively) of Pinus flexilis in common gardens arrayed along an elevation and canopy gradient from subalpine forest into the alpine zone and examined differences in physiology and morphology between provenances and among sites. Plant dry mass, projected leaf area and shoot:root ratios were 12–40% greater in LO compared with HI seedlings at each elevation. There were no significant changes in these variables among sites except for decreased dry mass of LO seedlings in the alpine site. Photosynthesis, carbon balance (photosynthesis/respiration) and conductance increased >2× with elevation for both provenances, and were 35–77% greater in LO seedlings compared with HI seedlings. There were no differences in dark-adapted chlorophyll fluorescence (Fv/Fm) among sites or between provenances. Our results suggest that for P. flexilis seedlings, provenances selected for above-ground growth may outperform those selected for stress resistance in the absence of harsh climatic conditions, even well above the species’ range limits in the alpine zone. This indicates that forest genetics may be important to understanding and managing species’ range adjustments due to climate change.
The management and processing of synchrotron and neutron computed tomography data can be a complex, labor-intensive and unstructured process. Users devote substantial time to both manually processing their data ( i.e. organizing data/metadata, applying image filters etc. ) and waiting for the computation of iterative alignment and reconstruction algorithms to finish. In this work, we present a solution to these problems: TomoPyUI , a user interface for the well known tomography data processing package TomoPy . This highly visual Python software package guides the user through the tomography processing pipeline from data import, preprocessing, alignment and finally to 3D volume reconstruction. The TomoPyUI systematic intermediate data and metadata storage system improves organization, and the inspection and manipulation tools (built within the application) help to avoid interrupted workflows. Notably, TomoPyUI operates entirely within a Jupyter environment. Herein, we provide a summary of these key features of TomoPyUI , along with an overview of the tomography processing pipeline, a discussion of the landscape of existing tomography processing software and the purpose of TomoPyUI , and a demonstration of its capabilities for real tomography data collected at SSRL beamline 6-2c.
A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can directly manipulate the input to a model, but are much more difficult to implement in the real world. In this paper we present a universal, time invariant attack for general time series data such that the attack has a frequency spectrum primarily composed of the frequencies present in the original data. The universality of the attack makes it fast and easy to implement as no computation is required to add it to an input, while time invariance is useful for real world deployment. Additionally, the frequency constraint ensures the attack can withstand filtering defenses. We demonstrate the effectiveness of the attack on two different classification tasks through both digital and real world experiments, and show that the attack is robust against common transform-and-compare defense pipelines.
A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can directly manipulate the input to a model, but are much more difficult to implement in the real-world. In this paper we present a universal, time invariant attack for general time series data such that the attack has a frequency spectrum primarily composed of the frequencies present in the original data. The universality of the attack makes it fast and easy to implement as no computation is required to add it to an input, while time invariance is useful for real-world deployment. Additionally, the frequency constraint ensures the attack can withstand filtering. We demonstrate the effectiveness of the attack in two different domains, speech recognition and unintended radiated emission, and show that the attack is robust against common transform-and-compare defense pipelines.
Eukaryotic organisms regulate the organization, structure, and accessibility of their genomes through chromatin remodeling that can be inherited as epigenetic modifications. These DNA and histone protein modifications are ultimately responsible for an organism’s molecular adaptation to the environment, resulting in distinctive phenotypes. Epigenetic manipulation of algae holds yet untapped potential for the optimization of biofuel production and bioproduct formation; however, epigenetic machinery and modes-of-action have not been well characterized in algae. We sought to determine the extent to which the biofuel platform species Picochlorum soloecismus utilizes DNA methylation to regulate its genome. We found candidate genes with domains for DNA methylation in the P. soloecismus genome. Whole-genome bisulfite sequencing revealed DNA methylation in all three cytosine contexts (CpG, CHH, and CHG). While global DNA methylation is low overall (~1.15%), it occurs in appreciable quantities (12.1%) in CpG dinucleotides in a bimodal distribution in all genomic contexts, though terminators contain the greatest number of CpG sites per kilobase. The P. soloecismus genome becomes hypomethylated during the growth cycle in response to nitrogen starvation. Algae cultures were treated daily across the growth cycle with 20 μM 5-aza-2'-deoxycytidine (5AZA) to inhibit propagation of DNA methylation in daughter cells. 5AZA treatment significantly increased optical density and forward and side scatter of cells across the growth cycle (16 days). This increase in cell size and complexity correlated with a significant increase (~66%) in lipid accumulation. Site specific CpG DNA methylation was significantly altered with 5AZA treatment over the time course, though nitrogen starvation itself induced significant hypomethylation in CpG contexts. Genes involved in several biological processes, including fatty acid synthesis, had altered methylation ratios in response to 5AZA; we hypothesize that these changes are potentially responsible for the phenotype of early induction of carbon storage as lipids. This is the first report to utilize epigenetic manipulation strategies to alter algal physiology and phenotype. Collectively, these data suggest these strategies can be utilized to fine-tune metabolic responses, alter growth, and enhance environmental adaption of microalgae for desired outcomes.
The past decade has seen significant progress in the field of metabolic engineering and synthetic biology. The exponentially growing multi-omics data and technological advances in the development of efficient genetic manipulation tools and techniques have allowed scientists to explore and expand their understanding of microbial metabolisms and further develop sophisticated engineering strategies to realize the use of industrial "workhorses" and non-conventional microorganisms for sustainable bioconversion and biorefinery. There is of great interest for the research community in using C1 compounds (i.e., CO 2 , CO/syngas, methane, methanol) as the next generation feedstocks for microbial cell factories and biocatalysts to promote the sustainable development of a green economy (Figure 1). Considering lowering input costs is also a main consideration for successful business ventures, the use of inexpensive, abundant, and widely accessible C1 compounds is envisioned as a promising route for the sustainable production of fine chemicals, fuels, and other high-value products. Many C1 compounds are waste gases from industrial activities and may have detrimental effects on climate change upon emission into the atmosphere. Therefore, promoting the use of C1 compounds as renewable carbon feedstocks can greatly contribute to the reduction of anthropogenic emission of air pollutants.
We consider methods for obtaining a multitude of structured laser beams (multiplexing) from an illuminating beam (both structured and unstructured) with the help of diffractive optical elements (DOEs). An approach of ‘intelligent multiplexing’ is proposed to describe and develop the methods. A DOE is calculated that forms a set of five diffraction orders located in a line. An example of focusing a set of doughnut-shaped azimuthally polarised laser beams using a diffraction beam splitter is presented. Efficient multiplexing of first-order vortex beams in a two-dimensional region is implemented by a two-dimensional diffraction grating. An approach is proposed and realised in which the transmission functions of a two-dimensional diffraction beam splitter and the elements forming structured laser beams with specified parameters are combined into the transmission function of one element. Such DOEs can be used in optical communication systems for encoding and decoding data. The possibility of using binary curved fork-shaped gratings for the formation of doughnut-shaped three-dimensional vortex beams, which are detected outside the focal plane, is demonstrated. This approach provides additional advantages for safe data transmission; it can be used in laser processing of materials and in laser manipulation applications. (paper)