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Provided herein, in some embodiments, are methods and compositions for gene editing using a trackable gene library, for example, a barcoded and gene edited library.
Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.
Saccharomyces cerevisiae is the dominant fermentative producer of ethanol in industry and a preferred host for production of other biofuels. That said, rewiring the metabolism of S. cerevisiae to produce other fermentation products, such as isobutanol, remains an academic challenge. Many studies report aerobic production of isobutanol, but ethanol remains a substantial by-product under these conditions due to the Crabtree effect. These studies indicate that the native isobutanol pathway is incapable of carrying sufficient flux to displace ethanol. In this report, we screened a combinatorial library of pathway enzymes to identify an isobutanol pathway cassette capable of supporting the growth of a non-ethanol producing S. cerevisiae . We began by identifying a diverse set of isobutanol pathway enzyme homologs and combined each open reading frame with varied-strength promoters in a combinatorial, pooled fashion. We applied a growth-coupled screen where a functional isobutanol pathway restored NAD + regeneration during glucose catabolism that is otherwise repressed via the Crabtree effect. Using this screen, we isolated a cassette consisting of a mosaic of bacterial and cytosol-localized fungal enzymes that conferred under aerobic conditions the ability to produce 364 mg/L isobutanol (8.8% of the theoretical maximum yield). We next shifted the cofactor usage of the isolated ketol-acid reductoisomerase enzyme in the cassette from NADPH to NADH-preferring to improve redox balance. The approach used herein isolated isobutanol producing strains that approach the best in the literature without producing substantial ethanol titers. Still, the best isolated cassette was insufficient to support anaerobic growth in the absence of ethanol fermentation - indicating the presence of further fundamental gaps in our understanding of yeast fermentation.
Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. In this work, an active learning approach based on conavigation of the hypothesis and experimental spaces is introduced. This is realized by combining the structured Gaussian processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. This approach is demonstrated for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO 3 using piezoresponse force microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis generation are available.
Abstract Plant biotechnology is rife with new advances in transformation and genome engineering techniques. A common requirement for delivery and coordinated expression in plant cells, however, places the design and assembly of transformation constructs at a crucial juncture as desired reagent suites grow more complex. Modular cloning principles have simplified some aspects of vector design, yet many important components remain unavailable or poorly adapted for rapid implementation in biotechnology research. Here, we describe a universal Golden Gate cloning toolkit for vector construction. The toolkit chassis is compatible with the widely accepted Phytobrick standard for genetic parts, and supports assembly of arbitrarily complex T‐DNAs through improved capacity, positional flexibility, and extensibility in comparison to extant kits. We also provision a substantial library of newly adapted Phytobricks, including regulatory elements for monocot and dicot gene expression, and coding sequences for genes of interest such as reporters, developmental regulators, and site‐specific recombinases. Finally, we use a series of dual‐luciferase assays to measure contributions to expression from promoters, terminators, and from cross‐cassette interactions attributable to enhancer elements in certain promoters. Taken together, these publicly available cloning resources can greatly accelerate the testing and deployment of new tools for plant engineering.
A National Nuclear Forensic Library (NNFL) is a useful nuclear forensics tool which consists of information and subject matter expertise on radioactive and nuclear (RN) materials produced, used or stored within a State. If RN material is found out of regulatory control the NNFL can be used as part of a nuclear forensics investigation to help identify whether or not the material is consistent with a country’s national holdings. In previous work, a number of signatures which can be useful to identify sealed sources of 241 Am were investigated. To validate the measurement results, an official query concerning information about two of the previously investigated 241 Am sources was sent to the United States Department of State, the international point-of-contact (POC) for the U.S. NNFL. The aim of this work is to show how data obtained in a characterization of a radioactive source can be used in conjunction with an NNFL to investigate the history of a source out of regulatory control.
The development of SARS-CoV-2 main protease (M pro ) inhibitors for the treatment of COVID-19 has mostly benefitted from X-ray structures and preexisting knowledge of inhibitors; however, an efficient method to generate M pro inhibitors, which circumvents such information would be advantageous. As an alternative approach, we show here that DNA-encoded chemistry technology (DEC-Tec) can be used to discover inhibitors of M pro . An affinity selection of a 4-billion-membered DNA-encoded chemical library (DECL) using M pro as bait produces novel non-covalent and non-peptide-based small molecule inhibitors of M pro with low nanomolar K i values. Furthermore, these compounds demonstrate efficacy against mutant forms of M pro that have shown resistance to the standard-of-care drug nirmatrelvir. Overall, this work demonstrates that DEC-Tec can efficiently generate novel and potent inhibitors without preliminary chemical or structural information.
This library contains g-functions (thermal response functions) for standard, regularly spaced vertical borehole ground heat exchangers. In total, it contains 34, 321 configurations. To permit interpolation, each configuration has g-functions for heights of 24, 48, 96, 192, and 384 m. All the g-functions were calculated with burial depths of 2m, and borehole diameters of 15 to 17.5 cm, depending on height. In configurations with uniform spacing, the spacing between the boreholes is set to 5m, though it can be scaled to other horizontal spacings.
This project dialogue provides an update on the Expansion of Verified, Archived, Library of Inputs and Data or VALID. VALID is a QA-Like (Quality Assurance) process to generate high quality models from reliable reference descriptions and make those models available to users. This presentation provides a brief project overview, a reminder of cases added in FY2021, cases currently in progress, and future plans for VALID.
This presentation was presented at the Aggregation and Grid Security Workshop - held on June 17-18, 2025, at NREL in Golden, Colorado. The goal of the two-day workshop was to address the critical cybersecurity challenges for the future electric grid. This presentation overviews the evolution and need for harmonized distributed energy resource standards and a new distributed energy resource standards library.
Protein farnesylation is a post-translational modification where a 15-carbon farnesyl isoprenoid is appended to the C-terminal end of a protein by farnesyltransferase (FTase). This process often causes proteins to associate with the membrane and participate in signal transduction pathways. The most common substrates of FTase are proteins that have C-terminal tetrapeptide CaaX box sequences where the cysteine is the site of modification. However, recent work has shown that five amino acid sequences can also be recognized, including the pentapeptides CMIIM and CSLMQ. In this work, peptide libraries were initially used to systematically vary the residues in those two parental sequences using an assay based on Matrix Assisted Laser Desorption Ionization–Mass Spectrometry (MALDI-MS). In addition, 192 pentapeptide sequences from the human proteome were screened using that assay to discover additional extended CaaaX-box motifs. Selected hits from that screening effort were rescreened using an in vivo yeast reporter protein assay. The X-ray crystal structure of CMIIM bound to FTase was also solved, showing that the C-terminal tripeptide of that sequence interacted with the enzyme in a similar manner as the C-terminal tripeptide of CVVM, suggesting that the tripeptide comprises a common structural element for substrate recognition in both tetrapeptide and pentapeptide sequences. Molecular dynamics simulation of CMIIM bound to FTase further shed light on the molecular interactions involved, showing that a putative catalytically competent Zn(II)-thiolate species was able to form. Bioinformatic predictions of tetrapeptide (CaaX-box) reactivity correlated well with the reactivity of pentapeptides obtained from in vivo analysis, reinforcing the importance of the C-terminal tripeptide motif. This analysis provides a structural framework for understanding the reactivity of extended CaaaX-box motifs and a method that may be useful for predicting the reactivity of additional FTase substrates bearing CaaaX-box sequences.
This study was conducted to validate the SCALE/Polaris v6.3.0–PARCS v3.4.2 code procedure with the Evaluated Nuclear Data File (ENDF)/B-VII.1 AMPX 56-group library for pressurized water reactor (PWR) analysis, by comparing simulated results with measured data for critical experiments and operating PWRs. Uncertainties of the SCALE/Polaris–PARCS code procedure for PWR analysis were evaluated in the validation for the PWR key nuclear parameters such as critical boron concentrations, reactivity, control bank work, temperature coefficients, and pin and assembly power peaking factors.
The SCALE/Polaris–PARCS code procedure has been used in the confirmatory analysis for boiling water reactors by the US Nuclear Regulatory Commission. In this study, the SCALE/Polaris v6.3.0–PARCS v3.4.2 code procedure with the Evaluated Nuclear Data File (ENDF)/B-VII.1 AMPX 56-group library was validated by comparing the simulated results with the measured data for operating boiling water reactors, including Peach Bottom Unit 2 cycles 1–3, Hatch Unit 1 cycles 1–3, and Quad Cities Unit 1 cycles 1–3. The uncertainties and biases of the SCALE/Polaris–PARCS code package for boiling water reactor physics analysis were evaluated in the validation for key nuclear parameters such as reactivity and traversing in-core probe data.
The advent of the World Wide Web (Web) and the ability to easily put data repositories on-line has resulted in a proliferation of digital libraries.
Too often, special libraries don't get the respect they deserve. This unfortunate fact has a long history. What I have tried to do is sum up the librarian's point of view from my personal perspective, and provide a catchy mnemonic device that can be called upon in times of need.
The HST Treasury Program Advanced Spectral Library Project: Cool Stars was designed to collect representative, high-quality UV spectra of eight evolved FM type cool stars. The Space Telescope Imaging Spectrograph (STIS)echelle spectra of these objects enable investigations of a broad range of topics, including stellar and interstellar astrophysics. This paper provides a guide to the spectra of the two evolved M stars, the M2 Iab supergiant Oriand the M3.4 giant Cru, with comparisons to the prototypical K1.5 giant Boo. It includes identifications of the significant atomic and molecular emission and absorption features and discusses the character of the photospheric and chromospheric continua and line spectra. The fluorescent processes responsible for a large portion of the emission-line spectrum, the characteristics of the stellar winds, and the available diagnostics for hot and cool plasmas are also summarized. This analysis will facilitate the future study of the spectra, outer atmospheres, and winds, not only of these objects but of numerous other cool, low-gravity stars, for years to come.
The HST (Hubble Space Telescope) Treasury Program Advanced Spectral Library Project: Cool Stars was designed to collect representative, high-quality ultraviolet spectra of eight evolved F-M type cool stars. The Space Telescope Imaging Spectrograph (STIS) echelle spectra of these objects enable investigations of a broad range of topics, including stellar and interstellar astrophysics. This paper provides a guide to the spectra of the two evolved M stars, the M2 Iab super giant alpha Ori and the M3.4 giant gamma Cru, with comparisons to the prototypical K1.5 giant alpha Boo. It includes identifications of the significant atomic and molecular emission and absorption features and discusses the character of the photospheric and chromospheric continua and line spectra. The fluorescent processes responsible for a large portion of the emission-line spectrum, the characteristics of the stellar winds, and the available diagnostics for hot and cool plasmas are also summarized. This analysis will facilitate the future study of the spectra, outer atmospheres, and winds, not only of these objects but of numerous other cool, low-gravity stars, for years to come.