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The present invention relates to methods and engineered microbial hosts useful for treating lignin or a derivative thereof. In some embodiments, the host has one or more exogenous nucleic acid sequences that encode a ligninase (e.g., a laccase and/or a peroxidase).
The present invention provides for a genetically modified host cell capable of producing isopentenol and/or 3-methyl-3-butenol, comprising (a) an increased expression of phosphomevalonate decarboxylase (PMD) (b) an increased expression of a phosphatase capable of converting isopentenol into 3-methyl-3-butenol, (c) optionally the genetically modified host cell does not express, or has a decreased expression of one or more of NudB, phosphomevalonate kinase (PMK), and/or PMD, and (d) optionally one or more further enzymes capable of converting isopentenol and/or 3-methyl-3-butenol into a third compound, such as isoprene.
The VTB hosts 20 distinct models, 8 of which were recently developed during 2022 and will be discussed in more detail here. This article provides an overview of the various models hosted and their capabilities. Part of the intent of the VTB is to showcase a ‘block’ style approach to modeling and simulation: if a capability is not showcased for a specific reactor type, it could, in theory, be easily ported/replicated from another. The breadth of advanced reactor types considered is representative of the large variety of concepts being proposed for demonstration.
The usefulness of Hosting Capacity Analysis (HCA) is dependent on users' confidence that the results accurately reflect grid conditions. This report provides the findings and recommendations from research, conducted by The National Renewable Energy Lab (NREL) and The Interstate Renewable Energy Council (IREC), into HCA data validation best practices. The goal is to provide utilities, regulators, and stakeholders best practices for HCA data validation procedures so that future HCA deployments avoid uncertainties and potential mistakes from earlier rollouts and provide useful and accurate data from the day they are published. Utilities can use this report to develop or refine their HCA data validation procedures. Regulators can use the report to inform their oversight of utilities' HCA data validation practices, while stakeholders can use it to evaluate the effectiveness of utility efforts.
The usefulness of Hosting Capacity Analysis (HCA) is dependent on users' confidence that the results accurately reflect grid conditions. This report provides the findings and recommendations from research, conducted by The National Renewable Energy Lab (NREL) and The Interstate Renewable Energy Council (IREC), into HCA data validation best practices. The goal is to provide utilities, regulators, and stakeholders best practices for HCA data validation procedures so that future HCA deployments avoid uncertainties and potential mistakes from earlier rollouts and provide useful and accurate data from the day they are published.
The National Renewable Energy Laboratory (NREL), in partnership with the Interstate Renewable Energy Council (IREC), has recently released a report which identifies a suite of best practices for producing trusted, validated hosting capacity analysis results reflecting real-world grid conditions.
Transitioning from a liquid electrolyte to an inorganic solid electrolyte offers a potential pathway to enable highly reversible lithium-metal anodes. However, the solid-electrolyte-protected lithium-metal anode suffers from poor interfacial ionic contacts and requires an impractically high stack pressure (>1.0 MPa) to maintain the interfacial contacts. It is demonstrated here that combining a hollow silver/carbon fiber scaffold and an inorganic/organic composite electrolyte enables a highly reversible lithium-metal anode. Since the high surface area of the electrode provides intimate contact and the hollow structure of the electrode relieves the lithium plating-induced stress, the electrode/electrolyte interface remains stable during cycling even under mild stack pressure. This unconventional structural design is validated in practical pouch cells assembled with the 3D lithium host containing a onefold excess of lithium and a high-nickel cathode. At a mild stack pressure of 320 kPa, the pouch cell displays 83% capacity retention for 200 cycles. This work illustrates that combining a hollow electrode architecture and a robust solid electrolyte offers a practical pathway toward rechargeable lithium-metal anodes.
High penetrations of distributed photovoltaics (PV) could cause adverse grid impacts, such as voltage violations. The recent development in inverter technologies provides the opportunity to develop control systems to realize effective PV governance and thus to improve dynamic PV hosting capacity for distribution grids. In this paper, a novel distributed energy resource management system (DERMS) solution is proposed by adopting the real-time optimal power flow (OPF) approach for coordinated control of the distributed PV inverters. The proposed approach eliminates the dependence on load knowledge via measurement feedback correction, and it can be implemented in real time. One challenge is that the technique is sensitive to the data availability and integrity of voltage measurements. Therefore, a decentralized DERMS approach is developed by leveraging the concepts of state estimation. The framework and the effectiveness of the solution approach are numerically demonstrated on a real distribution feeder in Southern California.
Physical constraints must be enforced when dis-tributed energy resources, such as PV, are integrated into distribution network. Hosting capacity (HC) is thus introduced to define the maximum renewable that distribution system can accommodate. When the grid is further pushed towards low-carbon, many research efforts are devoted to increasing HC. Security and cost-efficiency become even more important in determining HC. This work proposes an uncertainty-proof HC with surrogate affine policy. Flexible resources are leveraged to increase HC. We propose a novel hybrid two-stage AC model with variable uncertainty set. An iterative algorithm is designed to solve the problem. The proposed model and solution approach are validated in modified 141-node feeders, and HC performance is also analyzed.
Evaluating Photovoltaic Hosting Capacity (PVHC) is an essential step in the process of integrating solar energy into power grids, particularly when focusing on the distribution network (DN) as the primary integration target. PVHC needs to be investigated, especially in cases where the grids are unbalanced, and their operational conditions vary spatially and temporally. This motivation prompted us to propose a scalable model tailored to this application. In this paper, we applied linearization to the alternating current optimal power flow (AC-OPF) and solar inverters, transforming the original problem into a mixed-integer linear programming (MILP) problem. Additionally, we accounted for the battery energy storage system (BESS) as a time-coupling factor for calculating PVHC. We then compared the PVHC results between the IEEE-13 bus and SMART-DS San Francisco (SFO) cases and discussed the extent to which BESS can enhance the PVHC of a DN. Furthermore, we designed a web-based graphical visualization for the SFO case, enabling user interaction with raw data and simulation results on a map through a graphical user interface (GUI). In summary, our results and findings provide valuable insights for future three-phase unbalanced AC-OPF PVHC practices and their visualization.
This paper models the sensitivity of dynamic hosting capacity analysis to the spatial distribution of distributed PV scenarios, random, close and far from substation deployments, at various penetration levels on a real distribution feeder, in a quasi-static time series (QSTS) power flow simulation using high resolution time-aware metrics. These spatial distribution scenarios were chosen to capture a wide range of potential operation impacts of these deployments using a set of thermaland voltage-based metrics such as instantaneous violations and moving averages for both thermal and voltage constraints. This study uses actual load and PV data up-scaled from 1-hour time step to 1-minute resolution to fully characterize the interaction between the daily changes in load and PV output, and their impacts on distribution system operations.
The shifts in adaptive strategies revealed by ecological succession and the mechanisms that facilitate these shifts are fundamental to ecology. These adaptive strategies could be particularly important in communities of arbuscular mycorrhizal fungi (AMF) mutualistic with sorghum, where strong AMF succession replaces initially ruderal species with competitive ones and where the strongest plant response to drought is to manage these AMF. Although most studies of agriculturally important fungi focus on parasites, the mutualistic symbionts, AMF, constitute a research system of human-associated fungi whose relative simplicity and synchrony are conducive to experimental ecology. First, we hypothesize that, when irrigation is stopped to mimic drought, competitive AMF species should be replaced by AMF species tolerant to drought stress. We then, for the first time, correlate AMF abundance and host plant transcription to test two novel hypotheses about the mechanisms behind the shift from ruderal to competitive AMF. Surprisingly, despite imposing drought stress, we found no stress-tolerant AMF, probably due to our agricultural system having been irrigated for nearly six decades. Remarkably, we found strong and differential correlation between the successional shift from ruderal to competitive AMF and sorghum genes whose products (i) produce and release strigolactone signals, (ii) perceive mycorrhizal-lipochitinoligosaccharide (Myc-LCO) signals, (iii) provide plant lipid and sugar to AMF, and (iv) import minerals and water provided by AMF. These novel insights frame new hypotheses about AMF adaptive evolution and suggest a rationale for selecting AMF to reduce inputs and maximize yields in commercial agriculture.
Background: DNA methylation in prokaryotes is involved in many different cellular processes including cell cycle regulation and defense against viruses. To date, most prokaryotic methylation systems have been studied in culturable microorganisms, resulting in a limited understanding of DNA methylation from a microbial ecology perspective. Here, we analyze the distribution patterns of several microbial epigenetics marks in the ocean microbiome through genome-centric metagenomics across all domains of life. Results: We reconstructed 15,056 viral, 252 prokaryotic, 56 giant viral, and 6 eukaryotic metagenome-assembled genomes from northwest Pacific Ocean seawater samples using short- and long-read sequencing approaches. These metagenome-derived genomes mostly represented novel taxa, and recruited a majority of reads. Thanks to single-molecule real-time (SMRT) sequencing technology, base modification could also be detected for these genomes. This showed that DNA methylation can readily be detected across dominant oceanic bacterial, archaeal, and viral populations, and microbial epigenetic changes correlate with population differentiation. Furthermore, our genome-wide epigenetic analysis of Pelagibacter suggests that GANTC, a DNA methyltransferase target motif, is related to the cell cycle and is affected by environmental conditions. Yet, the presence of this motif also partitions the phylogeny of the Pelagibacter phages, possibly hinting at a competitive co-evolutionary history and multiple effects of a single methylation mark. Conclusions: Overall, this study elucidates that DNA methylation patterns are associated with ecological changes and virus-host dynamics in the ocean microbiome.
This study presents a solution for enhancing the security of the Personal Computer Transient Analyzer (PCTRAN) PC-based Nuclear Power Plant Simulator by integrating the software with an artificial intelligence (AI)-driven host-intrusion detection system (HIDS), in addition to a secured container system, for malware analysis. PCTRAN is a Windows XP-based software package that has the ability to simulate a variety of accident and transient conditions for nuclear power plants (NPPs). It offers a high-resolution replica of the Nuclear Steam Supply System (NSSS) and displays the status of important parameters allowing for operator interaction. By including AI-driven HIDS for the NSSS, the framework can identify security threats in real-time, ensuring the integrity of the nuclear simulation environment. Additionally, the secured container system offers the ability to isolate and analyze malware, preventing potential threats from affecting core systems. The integration process involves extensive testing and validation in order to ensure accuracy, reliability, and compliance with security policies. This framework sets a new precedent for secure simulation and training in NPP operations, and offers insight for future advancements in cybersecurity.
The purpose of this experiment was to evaluate the human host cellular response to wild-type Human coronavirus strain 229E (HCoV-229E) infection. Sample data was obtained for mock and infected immortalized human lung epithelial cells (A549) (MOI 5) nuclear extracts, immortalized human lung fibroblasts cells (MRC5) (MOI5) nuclear extracts, and primary human airway epithelial (HAE) (MOI 3) cells from lung tissue and processed for proteome analysis. Processed datasets are openly accessible from the download button and contain secondary processed proteomic results files and supporting metadata materials. Experimental proteomics samples were prepared using Limited Proteolysis (LiP) methods for Label-free quantification (LFQ) and global proteomic evaluation. Sample data was acquired using a Q-Exactive HF-X mass spectrometer and was processed and compiled using MaxQuant software (v.1.6.17.0). Processed proteomic data downloads include a sample naming key, processed MaxQuant results/parameters, and protein annotated relative abundance files. See corresponding primary data accessions below and Viral Experiment LiP Analysis source code supporting data transparency and reuse. Experimental transcriptomics samples were collected in parallel and processed for RNA sequencing (RNA-Seq) as summarized under ACS-DP1 (https://data.pnnl.gov/group/nodes/dataset/34069).
We study cool neutral gas traced by NaD absorption in 140 local ($z$ < 0.1) early-type red geyser galaxies. These galaxies show unique signatures in spatially resolved strong line emission maps that have been interpreted as largescale active galactic nucleus–driven ionized winds. To investigate the possible fuel source for these winds, we examine the abundance and kinematics of cool gas ($T$ ~ 100–1000 K) inferred from Na I D absorption in red geysers and matched control samples drawn from the Sloan Digital Sky Survey IV Mapping Nearby Galaxies at Apache Point Observatory survey. We find that red geysers host greater amounts of NaD-associated material. Substantial cool gas components are detected in more than 50% of red geysers (compared to 25% of the control sample) going up to 78% for radio-detected red geysers. Our key result is that cool gas in red geysers is predominantly infalling. Among our 30 radio-detected red geysers, 86% show receding NaD absorption velocities (with respect to the systemic velocity) between 40 and 50 km s-1 . We verify this result by stacking NaD profiles across each sample, which confirms the presence of infalling NaD velocities within red geysers (~40 km s -1 ) with no velocity offsets detected in the control samples. Interpreting our observations as signatures of inflowing cool neutral clouds, we derive an approximate mass inflow rate of $\dot{M}$ in ~ 0.1$M$ ⊙ yr -1 , similar to that expected from minor merging and internal recycling. In conclusion, some red geysers show much higher rates ($\dot{M}$ in ~ 5$M$ ⊙ yr -1 ), which may indicate an ongoing accretion event.
This presentation gives a high-level overview of past and current results related to advanced PV inverter functions, such as volt/var control, and how such functions enable higher PV hosting capacity levels in distribution systems.
Increasing electric vehicle (EV) charging loads can increase the magnitude and duration of conventional peaks in demand profiles and even shift them significantly, causing operational violations in the distribution grid. It is important to develop tools to quantify the impacts of injecting large number of EV charging loads and determine the available capacity of the existing distribution feeder for the safe operation of the grid. Such tools would enable utilities to better prepare for grid operations in the near future while exploring the impact and effectiveness of strategies such as peak pricing and smart charging in managing these loads. This paper evaluates the hosting capacity of some real-world feeders to accommodate EV charging loads, including extreme fast-charging (xFC) options.