Bioenergy sorghum nodal root bud development: morphometric, transcriptomic and gene regulatory network analysis
Sorghum bicolor TX08001 Nodal Root Tissue Development Gene Expression Profiling
bioenergy sorghum↗
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Root Cause Correlation Analysis of Software Failures via Orthogonal Defect Classification and Natural Language Processing
Systems theoretic process analysis (STPA) is becoming an increasingly popular technique to assess how complex digital software systems can fail. Rather than defining failures by their observable failure events, which may be sparse especially for safety rated nuclear digital instrumentation and control systems (DI&C), failures are defined as postulated unsafe actions under specific contextual conditions. This permits a top-down analysis of system hazards and identifies whether imposed constraints and requirements can sufficiently address undesirable hazards. However, STPA is a qualitative approach at identifying inadequacies in the development process and cannot currently be used to quantify unsafe action likelihoods for probabilistic risk assessment. Therefore, in this work, we examine the root causes of software failure and explore whether a consistent correlation can be linked to specific unsafe action classes. We implement Lbl2Vec, an unsupervised document classification and retrieval algorithm, on a database of 4,096 software defect reports acquired from various open-source software systems. By analyzing sentence structure, embedded labels, and word vectors, we show that certain defect types positively correlate to specific unsafe action classes over others. The correlations developed can be used to estimate the failure probability of safety intended DI&C systems which provides a licensing basis for nuclear plant modernization efforts.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Root Cause Correlation Analysis of Software Failures via Orthogonal Defect Classification and Natural Language Processing
Systems theoretic process analysis (STPA) is becoming an increasingly popular technique to assess how complex digital software systems can fail. Rather than defining failures by their observable failure events, which may be sparse especially for safety rated nuclear digital instrumentation and control systems (DI&C), failures are defined as postulated unsafe actions under specific contextual conditions. This permits a top-down analysis of system hazards and identifies whether imposed constraints and requirements can sufficiently address undesirable hazards. However, STPA is a qualitative approach at identifying inadequacies in the development process and cannot currently be used to quantify unsafe action likelihoods for probabilistic risk assessment. Therefore, in this work, we examine the root causes of software failure and explore whether a consistent correlation can be linked to specific unsafe action classes. We implement Lbl2Vec, an unsupervised document classification and retrieval algorithm, on a database of 4,096 software defect reports acquired from various open-source software systems. By analyzing sentence structure, embedded labels, and word vectors, we show that certain defect types positively correlate to specific unsafe action classes over others. The correlations developed can be used to estimate the failure probability of safety intended DI&C systems which provides a licensing basis for nuclear plant modernization efforts.
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
Improved Characterization of Soil Organic Matter by Integrating FT-ICR MS, Liquid Chromatography Tandem Mass Spectrometry, and Molecular Networking: A Case Study of Root Litter Decay under Drought Conditions
Understanding of how soil organic matter (SOM) chemistry is altered in a changing climate has advanced considerably; however, most SOM components remain unidentified, impeding the ability to characterize a major fraction of organic matter and predict what types of molecules, and from which sources, will persist in soil. Here we present a novel approach to better characterize SOM extracts by integrating information from three types of analyses, and we deploy this method to characterize decaying root-detritus soil microcosms subjected to either drought or normal conditions. To observe broad differences in composition, we employed direct infusion Fourier-transform ion cyclotron resonance mass spectrometry (DI-FT-ICR MS). We complemented this with liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify components by library matching. Since libraries contain only a small fraction of SOM components, we also used fragment spectral cosine similarity scores to relate unknowns and library matches through molecular networks. This integrated approach allowed us to corroborate DI-FT-ICR MS molecular formulas using library matches, which included fungal metabolites and related polyphenolic compounds. We also inferred structures of unknowns from molecular networks and improved LC-MS/MS annotation rates from ~5 to 35% by considering DI-FT-ICR MS molecular formula assignments. Under drought conditions, we found greater relative amounts of lignin-like vs condensed aromatic polyphenol formulas and lower average nominal oxidation state of carbon, suggesting reduced decomposition of SOM and/or microbes under stress. Our integrated approach provides a framework for enhanced annotation of SOM components that is more comprehensive than performing individual data analyses in parallel.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Rational Modulation of Plant Root Development Using Engineered Cytokinin Regulators
Achieving precise control over quantitative developmental phenotypes is a key objective in plant biology. Recent advances in synthetic biology have enabled tools to reprogram entire developmental pathways; however, the complexity of designing synthetic genetic programs and the inherent interactions between various signaling processes remains a critical challenge. Here, we leverage Type-B response regulators to modulate the expression of genes involved in cytokinin-dependent growth and development processes. We rationally engineered these regulators to modulate their transcriptional activity (i.e., repression or activation) and potency while reducing their sensitivity to cytokinin. By localizing the expression of these engineered transcription factors using tissue-specific promoters, we can predictably tune cytokinin-regulated traits. As a proof of principle, we deployed this synthetic system in Arabidopsis thaliana to either decrease or increase the number of lateral roots. The simplicity and modularity of our approach makes it an ideal system for controlling other developmental phenotypes of agronomic interest in plants.
Cell signaling↗
Quantum Light Imaging of the Plant Root─Rhizobacteria─System Using iLOV Fluorescence
It has been demonstrated recently that the use of specific quantum states of light for two-photon microscopy can lead to a substantial reduction of the intensity of light needed for the imaging of biological objects. This opens new ways to better understand the delicate mechanisms of the interactions of living organisms in the rhizosphere on an extended time scale without optical damage or alteration of their native behavior. Herein, we report a successful realization of entangled two-photon excited fluorescence microscopy of wheat plant roots and a set of biological samples, including purified iLOV protein, bacterial cells expressing iLOV, and combination of these under an extremely low excitation intensity. The entangled two-photon coupling strength of iLOV was modeled using electronic structure calculations which demonstrated that the FMN chromophore in the iLOV protein has an entangled two-photon absorption (ETPA) cross section that is large enough for imaging using weak quantum light for fluorescence excitation. Successful imaging of plant-associated Pantoea sp. YR343 cells expressing iLOV validates the feasibility of using iLOV as a molecular label for characterizing plant-microbe interactions based on entangled photon imaging. In conclusion, the results demonstrate the advantageous use of entangled photon imaging under noninvasive and nondestructive conditions.
Varnavksi, Oleg [Univ. of Michigan, Ann Arbor, MI ↗
Shifts in evolutionary lability underlie independent gains and losses of root-nodule symbiosis in a single clade of plants
Abstract Root nodule symbiosis (RNS) is a complex trait that enables plants to access atmospheric nitrogen converted into usable forms through a mutualistic relationship with soil bacteria. Pinpointing the evolutionary origins of RNS is critical for understanding its genetic basis, but building this evolutionary context is complicated by data limitations and the intermittent presence of RNS in a single clade of ca. 30,000 species of flowering plants, i.e., the nitrogen-fixing clade (NFC). We developed the most extensive de novo phylogeny for the NFC and an RNS trait database to reconstruct the evolution of RNS. Our analysis identifies evolutionary rate heterogeneity associated with a two-step process: An ancestral precursor state transitioned to a more labile state from which RNS was rapidly gained at multiple points in the NFC. We illustrate how a two-step process could explain multiple independent gains and losses of RNS, contrary to recent hypotheses suggesting one gain and numerous losses, and suggest a broader phylogenetic and genetic scope may be required for genome-phenome mapping.
59 BASIC BIOLOGICAL SCIENCES↗
Fine root and soil carbon stocks are positively related in grasslands but not in forests
Increasing fine root carbon (FRC) inputs into soils has been proposed as a solution to increasing soil organic carbon (SOC). However, FRC inputs can also enhance SOC loss through priming. Here, we tested the broad-scale relationships between SOC and FRC at 43 sites across the US National Ecological Observatory Network. We found that SOC and FRC stocks were positively related with an across-ecosystem slope of 7 ± 3 kg SOC m −2 per kg FRC m −2 , but this relationship was driven by grasslands. Grasslands had double the across-ecosystem slope while forest FRC and SOC were unrelated. Furthermore, deep grassland soils primarily showed net SOC accrual relative to FRC input. Conversely, forests had high variability in whether FRC inputs were related to net SOC priming or accrual. We conclude that while FRC increases could lead to increased SOC in grasslands, especially at depth, the FRC-SOC relationship remains difficult to characterize in forests.
54 ENVIRONMENTAL SCIENCES↗
Arsenic stress triggers active exudation of arsenic–phytochelatin complexes from Lupinus albus roots
Abstract Arsenic (As) contamination of soils threatens the health of millions globally through accumulation in crops. While plants detoxify As via phytochelatin (PC) complexation and efflux of arsenite from roots, arsenite efflux mechanisms are not fully understood. Here, white lupin (Lupinus albus) was grown in semi-hydroponics, and exudation of glutathione (GSH) derivatives and PCs in response to As was measured using LC-MS/MS. Inhibiting synthesis of the PC precursor GSH with l-buthionine sulfoximine (BSO) or ABC transporters with vanadate drastically reduced (>22%) GSH derivative and PC2 exudation, but not PC3 exudation. This was accompanied by As hypersensitivity in plants treated with BSO and moderate sensitivity with vanadate treatment. Investigating As–PC complexation revealed two distinct As–PC complexes, As bound to GSH and PC2 (GS–As–PC2) and As bound to PC3 (As–PC3), in exudates of As-treated lupin plants. Vanadate inhibited As–PC exudation, while BSO inhibited both the synthesis and exudation of As–PC complexes. These results demonstrate a role for GSH derivatives and PC exudation in lupin As tolerance and reveal As–PC exudation as a new potential mechanism contributing to active As efflux in plants. Overall, this study uncovers insights into rhizosphere As detoxification with potential to help mitigate pollution and reduce As accumulation in crops.
Plant Sciences↗
Double copy root of Hawking thermality
The Hawking radiation spectrum from a collapsing null shell can be derived via the double copy of a simpler gauge theory calculation. Analyzing the non-abelian Yang-Mills root of this process, we demonstrate that the radiation spectrum is thermal in the color charge eigenvalue 𝜆, not energy. Considering the 𝑆𝑈(𝑁 𝑐 ) gauge theory in the large 𝑁 𝑐 limit, we find the differential spectrum ⅆ𝑁/ⅆ𝜆 is a product of the gravitationally familiar Planck-like factor and the color phase space density, modeled here as the Wigner semicircle from random matrix theory. This reveals that apparent energy thermality in gravity is the direct dual of charge thermality in its underlying non-abelian gauge theory.
Carrasco, John Joseph M. [Northwestern University,↗
Topological solitons in square-root graphene nanoribbons controlled by electric fields
Conjugational defects, also known as solitons, play an important role in the electronic, magnetic, and optical properties of materials. Understanding solitons can uncover intriguing physics and provide insights for designing quantum materials with tailored band structures and electronic properties. Here we propose a framework to create and control solitons via topological phase transitions in a class of graphene nanoribbons (GNRs) called square-root GNRs, using a transverse electric field. To demonstrate the experimental feasibility, we design and synthesize a representative GNR with a bottom-up approach, with first-principles calculations revealing topological soliton states at the domain wall induced by the electric field. In conclusion, the framework introduced in this work can potentially enable direct manipulation of solitons and provide a platform to study them systematically.
36 MATERIALS SCIENCE↗
Rolling Root Mean Square Based Multimodal Anomaly Detection for Real Time Monitoring of Smart Grid
Reliable real-time monitoring is valuable for maintaining the operational integrity of modern electrical smart grids. Deployment of heterogeneous sensing technologies in substations has enabled high-resolution, multichannel waveform monitoring, but also introduces challenges for anomaly detection due to noise, baseline drift, and modality-dependent signal characteristics. In this work, we present a computationally efficient unsupervised method for multimodal event detection based on Rolling Root Mean Square based Event Detection (RRMSED). The method is developed using in-house, field deployed sensors collecting data at a utility substation. The sensing system comprises voltage and current sensors, triaxial accelerometers, and magnetometers, collectively capturing electrical, vibrational, and magnetic waveform measurements at high temporal resolution. RRMSED operates by extracting rolling RMS energy features and their first-order temporal differences from consecutive waveform segments for each channel and then applying channel-specific statistical thresholds learned from historical data. A persistence-based exceedance logic is employed to robustly identify transient events while suppressing impulsive noise, and to provide precise temporal localization with high resolution. The framework is designed for continuous server-side operation and can be deployed in real time without requiring complex models. Experiments on simulated waveform data with known ground truth demonstrate low false positive (FP) and false negative (FN) rates. Application to real substation data shows RRMSED to identify events that are not captured by conventional monitoring indicators including fast transient detection algorithm currently deployed in the system. These results indicate that rolling RMS based features provide an effective and practical basis for real-time multimodal event detection in smart-grid substations.
Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
Ecohydrological controls on root and microbial respiration in the East River watershed of Colorado
The main objective of this project was to conduct exploratory work to quantify how snow and rain water inputs influence the CO2 coming from the soil surface (soil CO2 flux), and its plant and microbial sources, in the East River watershed, near Crested Butte, Colorado. Knowledge gained from this effort laid the groundwork for a more comprehensive (ongoing) follow-on grant that is using a combination of experiments, field observations, machine learning and simulation modeling to fully disentangle these relationships. New field measurements were made at four locations along an elevational transect on Snodgrass Mountain at Rocky Mountain Biological Laboratory. These sites were chosen to differ in total snowpack and water table depth, and included both deciduous (aspen) and evergreen (spruce/fir) forest types. We used automated measurements of soil CO2 concentrations to quantify the total soil CO2 flux, and the vertical CO2 production within the soil profile at each site. Isotope (radiocarbon, 14C) measurements determined how much of the CO2 emitted from the soil surface came from plant respiration (root metabolism) versus microbial respiration (decomposition of soil organic matter) sources. Supporting data on plant phenology and microbial dynamics provided context for the observed variation in respiration sources. This work was motivated by our overarching hypothesis that quantifying belowground plant and microbial processes separately, and how they are influenced by snow and rain inputs, is necessary for understanding and predicting how the East River watershed ecosystems will respond to future environmental change.
54 ENVIRONMENTAL SCIENCES↗
A Single-Cell Transcriptomic Atlas of the Populus Root Uncovers Cell Type-Specific Methyl Jasmonate Response
This dataset contains single-nucleus RNA sequencing data from poplar (Populusspp.) roots under control and methyl jasmonate (MeJA) treatment conditions. It was generated to investigate cell type-specific stress responses and to establish a high-resolution transcriptomic atlas in a perennial woody species.
CBI↗
Code associated with "Assessing the Effect of a Deep-Rooted Grass on Belowground Carbon Storage in Cultivated Land: Insights from a Multi-Site US Study"
R code associated with "Assessing the Effect of a Deep-Rooted Grass on Belowground Carbon Storage in Cultivated Land: Insights from a Multi-Site US Study" These scripts can be used to analyze the accompanying dataset: https://doi.org/10.5281/zenodo.16620529. (1) resampling_functions.R : Creates functions for resampling data for nonparametric bootstrapping (2) bootstrap_depth_profiles.R : Applies resampling functions to the dataset (3) gapfill_masscalcs.R : Prepares data for equivalent mass calculations (4) get_14C_EM.R : Applies soilR to obtain the 14C end member for mixing model calculations (5) mass_aggregate.R : Performs equivalent mass and mass weighted averaging calculations.
Carbon↗
SBIR Phase II Final Report for Rhizomatic - Next Generation Image Processing for In Situ Fine Root Measurement
Explore the source record for details and available documents.
54 ENVIRONMENTAL SCIENCES↗