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Various program analysis techniques are efficient at discovering failures and properties. However, it is often difficult to evaluate results, such as program traces. This calls for abstraction and visualization tools. We propose an approach based on UML sequence diagrams, addressing shortcomings of such diagrams for concurrency. The resulting visualization is expressive and provides all the necessary information at a glance.
State Analysis is a model-based systems engineering methodology employing a rigorous discovery process which articulates operations concepts and operability needs as an integrated part of system design. The process produces requirements on system and software design in the form of explicit models which describe the behavior of states and the relationships among them. By applying State Analysis to an actual MER flight mission scenario, this study addresses the specific real world challenges of complex space operations and explores technologies that can be brought to bear on future missions. The paper describes the tools currently used on a daily basis for MER operations planning and provides an in-depth description of the planning process, in the context of a Martian day's worth of rover engineering activities, resource modeling, flight rules, science observations, and more. It then describes how State Analysis allows for the specification of a corresponding goal-based sequence that accomplishes the same objectives, with several important additional benefits.
Sequence-function data provides valuable information about the protein functional landscape but is rarely obtained during directed evolution campaigns. Here, we present Long-read every variant Sequencing (LevSeq), a pipeline that combines a dual barcoding strategy with nanopore sequencing to rapidly generate sequence-function data for entire protein-coding genes. LevSeq integrates into existing protein engineering workflows and comes with open-source software for data analysis and visualization. The pipeline facilitates data-driven protein engineering by consolidating sequence-function data to inform directed evolution and provide the requisite data for machine learning-guided protein engineering (MLPE). LevSeq enables quality control of mutagenesis libraries prior to screening, which reduces time and resource costs. Simulation studies demonstrate LevSeq’s ability to accurately detect variants under various experimental conditions. Lastly, we show LevSeq’s utility in engineering protoglobins for new-to-nature chemistry. Widespread adoption of LevSeq and sharing of the data will enhance our understanding of protein sequence-function landscapes and empower data-driven directed evolution.
Deployment and operation of ocean energy devices does not represent the first foray into industrialization of the oceans; shipping, nearshore development, waste disposal, subsea mining, oil and gas extraction, and large-scale commercial fishing all coexist in various states of equilibrium with the marine environment. In most cases these industries were developed without a clear understanding of the likely outcomes of large-scale development. In virtually every country where the harvest of ocean energy is emerging, regulators and stakeholders require that the industry examine potential effects of devices, minimize the footprint of effects, and provide management measures that either avoid the impacts or mitigate to further reduce the residual impacts. The ERES analysis is based on scenarios that are consistent with sequences of events that lead to adverse impacts, distinguishing between episodic, intermittent, and chronic risks. In the context of ocean energy development, an episodic scenario might involve the exceedingly rare but potentially devastating event of an oil spill from vessels caused by the presence of the device, while vulnerable receptors are present; understanding the risk of such a scenario involves determining the probability of the occurrence by examining factors such as the petroleum content of ocean energy devices, the vessel traffic volume and the proximity of shipping lanes to the ocean energy devices, the reliability of the control measures to avoid an episodic event, and the likely presence of seabirds, marine mammals, or fish that may be affected by oil. In contrast, chronic risk scenarios involve events or circumstances that are continuous, so that risk characterization involves assessing only the severity of the consequences. An example of a chronic risk scenario might be the toxicity to marine organisms due to low-level chemical releases from anti-biofouling paints and coatings that may be used on devices, and the effect that the level of toxicity may have on marine flora and fauna. Between these two extremes are intermittent events, such as encounters between fish and rotating tidal turbine blades that will occur only when fish are present and the tidal device is turning. A key feature of understanding risk is describing the uncertainty associated with the occurrence of an episodic, intermittent, or chronic event, as well as the uncertainty of the resulting consequences.
Methyl formate (MF, CH3OCHO), the simplest ester, is a representative oxygenated fuel with high oxygen content, and low sooting tendency. However, its oxidation behavior under high-pressure and intermediate-temperature conditions remains insufficiently understood, especially where low-temperature peroxy radical chemistry, methanol chemistry, and pressure-dependent reaction pathways play a critical role. In this study, MF oxidation experiments were conducted in the Princeton supercritical-pressure jet-stirred reactor (SP-JSR) at 20 and 100 atm over the temperature range of 400–950 K under both fuel-lean and fuel-rich conditions. Based on the experimental results, an updated HP-Mech was developed by incorporating previous MF sub-mechanisms, expanded low-temperature peroxy pathways, and evaluated pressure-dependent decomposition kinetics. The newly updated HP-Mech shows greatly improved performance in predicting the onset temperature, the key intermediate species fractions, methanol formation, and the progression of MF oxidation across all the experimental conditions. Path flux analysis indicates that MF consumption at the onset stage is dominated by H-abstraction at the methyl site, forming CH2OCHO radicals that lead to the formation and isomerization of O2CH2OCHO, driving low-temperature chain propagation. Moreover, H-abstraction at the formate site forms CH3OCO radicals that preferentially decompose to CH3, initiating the methanol formation pathway linked to CH3O2 and HO2 chemistry. At the same time, HO2 formation is strongly coupled to MF oxidation through multiple MF-derived radical pathways. HCO originates from MF oxidation and acts as a key coupling species linking fuel consumption to HO2 buildup, especially under high-pressure and intermediate-temperature conditions. In addition to this dominant channel, supplementary HO2 formation pathways involving CH3, CH3O, CH2OH, and CH3O2 reacting with O2 further connect methanol chemistry and oxygenated radical chemistry to the HO2 pool, indicating the central role of HO2 in governing MF oxidation. Sensitivity analysis identifies MF with OH/HO2/CH3O2 reactions and the HO2/H2O2/OH sequence as the key factors controlling reactivity in the high-pressure and intermediate-temperature regime. MF directly reacts with OH/HO2/CH3O2 to consume the fuel and produce reactive radicals like CH2OCHO and CH3OCO that undergo subsequent oxidation pathways. Moreover, HO2 recombination suppresses oxidation at lower temperatures, while thermal decomposition of H2O2 accelerates OH production and promotes fuel consumption as temperature increases. The direct formation of active OH from HO2 radicals further completes the mechanism, improving its prediction especially during the oxidation onset stage.
Microtubule-associated serine/threonine kinase-like (MASTL) (or Greatwall kinase (GWL)) is an important cell cycle regulating kinase that regulates the G2-M transition. Uncontrolled MASTL activity is implicated in breast cancer progression. To date, very few inhibitors have been reported against this protein. Here, structure-based computational modeling indicates that the natural product flavopiridol (FLV) binds strongly to MASTL and these results are validated using molecular dynamics simulation studies. Further, an in vitro kinase assay reveals an EC 50 (effective concentration) value of FLV to be 82.1 nM and a better IC 50 compared to the positive reference compound, staurosporine. FLV is found to inhibit MASTL kinase activity, arresting the cell growth in the G1 phase and inducing apoptosis in breast cancer cells. Consistent with these results differential gene expression obtained using RNA sequencing studies, and validated by RT PCR and immunoblot analysis, indicate that MASTL inhibition induces cell cycle arrest and apoptotic-related genes. Furthermore, metastasis- and inflammation- related genes are downregulated. Thus, the deregulation of MASTL signaling pathways on targeted inhibition of its kinase activity is revealed. This study lays a strong foundation for investigating FLV as a lead compound in breast cancer therapeutics.
Here, fungi have developed a wide assortment of enzymes to break down pectin, a prevalent polymer in plant cell walls that is important in plant defense and structure. One enzyme family used to degrade pectin is the glycosyl hydrolase family 28 (GH28). In this studywe developed primers for the amplification of GH28 coding genes from a database of 293 GH28 sequences from40 fungal genomes. The primerswere used to successfully amplify GH28 pectinases from all Ascomycota cultures tested, but only three out of seven Basidiomycota cultures. In addition, we further tested the primers in PCRs on metagenomic DNA extracted from senesced tree leaves from different forest ecosystems, followed by cloning and sequencing. Taxonomic specificity for Ascomycota GH28 genes was tested by comparing GH28 composition in leaves to internal transcribed spacer (ITS) amplicon composition using pyrosequencing. All sequences obtained from GH28 primers were classified as Ascomycota; in contrast, ITS sequences indicated that fungal communitieswere up to 39% Basidiomycetes. Analysis of leaf samples indicated that both forest stand and ecosystemtype were important in structuring fungal communities. However, site played the prominent role in explaining GH28 composition, whereas ecosystem type was more important for ITS composition, indicating possible genetic drift between populations of fungi. Overall, these primers will have utility in understanding relationships between fungal community composition and ecosystem processes, as well as detection of potentially pathogenic Ascomycetes.
Large scale implementation of high level computational theoretical chemical kinetics offers the prospect for dramatically improving the fidelity of combustion chemical modeling. To facilitate such efforts, we have developed a suite of codes, collectively referred to as AutoMech, that allow for the automatic prediction of the kinetics for large sets of reactions via ab initio transition-state-theory based master-equation calculations. The primary input is simply the mechanism, a dictionary relating chemically identifiable species descriptors (e.g., SMILES or InChIs) to species labels in the mechanism, and a specification of the electronic structure and transition state theory models to be implemented. Here we illustrate the current utility of AutoMech through a study of the initial stages of pyrolysis for 3 sets of fuels: sequences of alkanes, alcohols, and aldehydes. For simplicity, the analysis focuses on abstractions from the fuel by H, CH 3 , and OH, and the decomposition of the resulting radicals. Altogether, there are a total of 166 input channels in these sets (more than 363 forward reactions when expanded to the full set of elementary reactions). The code successfully produces high quality rate estimates (with apparent uncertainties less than a factor of two in limited comparisons with experiment) for > 95% of these. For the radical decomposition reactions, the analysis includes predictions for the pressure dependence of the kinetics. This wide-ranging exploration illustrates (i) the effect of different levels of prediction on the expected accuracy, (ii) the branching between abstractions at different sites for different abstractors, (iii) the dependence of the rates on the chemical structure, and (iv) the variation in radical stabilities across chemical families. These results, as well as the demonstrated feasibility of the methodology, should find further utility in the development of accurate rate expressions for arbitrary fuels. (c) 2020 The Combustion Institute. Published by Elsevier Inc. All rights reserved.
Generating top-down tandem mass spectra (MS/MS) for complex mixtures of proteoforms has become possible through improvements in fractionation, on-line separation, dissociation, and mass analysis. The algorithms to match tandem mass spectra to sequences have undergone a parallel evolution, with both spectral alignment and peak matching being paired with diverse methods for scoring proteoform-spectral matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification through three distinct challenges. The first is identifying a large yield of PrSMs while controlling false discovery rate (FDR) in identifying thousands of proteoforms from complex cell lysates via four software workflows: ProSight Proteome Discoverer, TopPIC, Informed Proteomics, and pTop. The second is the deconvolution of data from both Thermo Orbitrap-class and Bruker maXis Q-TOF instruments to produce consistent precursor charge and mass determinations while generating fragment mass lists to optimize identification. The third attempts to detect diverse post-translational modifications (PTMs) in proteoforms from cow milk and human ovarian tissue. The data demonstrate that existing software suites produce admirable sensitivity, in some cases identifying a third of collected tandem mass spectra with FDR controlled below 2%; the overlap in these PrSMs, however, illustrates real value in searching data with multiple search engines. Differences among identification workflows seem to result from each search algorithm incorporating its own deconvolution algorithm. By transmitting deconvolution data from multiple deconvolution routes (Thermo Xtract, Bruker Auto MSn, Mascot Distiller, TopFD, and FLASHDeconv) to the downstream TopPIC search algorithm, we were able to detect common causes of deconvolution disagreement. The detection of PTMs was very inconsistent among search algorithms, with some workflows suggesting as little as 1% of PrSMs from cow’s milk were singly-phosphorylated while other workflows found that 18% of PrSMs were singly-phosphorylated. Taken together, these results make a strong argument for top-down researchers to adopt a standard practice of analyzing each MS/MS experiment with at least two different search engines.
The pathological changes in epigenetics and gene regulation that accompany the progression of low-grade to high-grade gliomas are under-studied. The authors use a large set of paired atac-seq and RNA-seq data from surgically resected glioma specimens to infer gene regulatory relationships in glioma. Thirty-eight glioma patient samples underwent atac-seq sequencing and 16 samples underwent additional RNA-seq analysis. Using an atac-seq/RNA-seq correlation matrix, atac-seq peaks were paired with genes based on high correlation values (|r 2 | > 0.6). Samples clustered by IDH1 status but not by grade. Surprisingly there was a trend for IDH1 mutant samples to have more peaks. The majority of peaks are positively correlated with survival and positively correlated with gene expression. Constructing a model of the top six atac-seq peaks created a highly accurate survival prediction model (r 2 = 0.68). Four of these peaks were still significant after controlling for age, grade, pathology, IDH1 status and gender. Grade II, III, and IV (primary) samples have similar transcription factors and gene modules. However, grade IV (recurrent) samples have strikingly few peaks. Patient-derived glioma cultures showed decreased peak counts following radiation indicating that this may be radiation-induced. This study supports the notion that IDH1 mutant and IDH1 wildtype gliomas have different epigenetic landscapes and that accessible chromatin sites mapped by atac-seq peaks tend to be positively correlated with expression. The data in this study leads to a new model of treatment response wherein glioma cells respond to radiation therapy by closing open regions of DNA.
Abstract Roots are a primary source of organic carbon input in most soils. The consumption of living and detrital root inputs involves multi-trophic processes and multiple kingdoms of microbial life, but typical microbial ecology studies focus on only one or two major lineages. We used Illumina shotgun RNA sequencing to conduct PCR-independent SSU rRNA community analysis (“community RNA-Seq”) and simultaneously assess the bacteria, archaea, fungi, and microfauna surrounding both living and decomposing roots of the annual grass, Avena fatua. Plants were grown in 13CO2-labeled microcosms amended with 15N-root litter to identify the preferences of rhizosphere organisms for root exudates (13C) versus decaying root biomass (15N) using NanoSIMS microarray imaging (Chip-SIP). When litter was available, rhizosphere and bulk soil had significantly more Amoebozoa, which are potentially important yet often overlooked top-down drivers of detritusphere community dynamics and nutrient cycling. Bulk soil containing litter was depleted in Actinobacteria but had significantly more Bacteroidetes and Proteobacteria. While Actinobacteria were abundant in the rhizosphere, Chip-SIP showed Actinobacteria preferentially incorporated litter relative to root exudates, indicating this group’s more prominent role in detritus elemental cycling in the rhizosphere. Our results emphasize that decomposition is a multi-trophic process involving complex interactions, and our methodology can be used to track the trajectory of carbon through multi-kingdom soil food webs.
With increased penetration of converter interfaced generation (CIG) in power systems, it becomes imperative to adequately model them in both steady-state and dynamic simulations. There are several modeling approaches and simulation platforms for CIG. In addition, it is noted that suitability of modeling approaches and models is also dependent on the system conditions. However, their suitability has yet to be systematically analyzed, and this becomes a challenge in the industry without a systemic method. To tackle these challenges, in this paper, we first develop a methodology for identifying the maximum penetration of aggregated rooftop sources that can be represented as negative loads in both the steady state and dynamic analysis. Then we verify the suitability of positive-sequence performance-based model and detailed electromagnetic transient (EMT) model for utility-scale PV plants under different fault conditions. Based on the results, we perform dynamic simulations on a large practical system with both positive-sequence transient stability(TS) and EMT-TS hybrid simulation. Our study shows that positive-sequence performance based PV plant model is more suitable for study cases with faults relatively far away from the point of connection, and that positive-sequence models and TS simulation approach can produce comparable results with reference to EMT-TS hybrid simulation for normally-cleared single-line-to-ground faults, but the inadequacy of positive-sequence based model and simulation approach becomes obvious for prolonged-tripping of unbalanced faults in proximity of the PV plants.
It has been shown previously that for magic angle spinning (MAS) solid state NMR the refocused INADEQUATE spin-echo (REINE) experiment can usefully quantify scalar (J) couplings in disordered solids. This paper focuses on the two z filter components in the original REINE pulse sequence, and investigates by means of a product operator analysis and fits to density matrix simulations the effects that their removal has on the sensitivity of the experiment and on the accuracy of the extracted J couplings. The first z filter proves unnecessary in all the cases investigated here and removing it increases the sensitivity of the experiment by a factor ∼1.1–2.0. Furthermore, for systems with broad isotropic chemical shift distributions (namely whose full widths at half maximum are greater than 30 times the mean J coupling strength), the second z filter can also be removed, thus allowing whole-echo acquisition and providing an additional √2 gain in sensitivity. Considering both random and systematic errors in the values obtained, J couplings determined by fitting the intensity modulations of REINE experiments carry an uncertainty of 0.2–1.0 Hz (∼1−10 %).
Introduction—Commensal gut microbiota play an important role in regulating metabolic and inflammatory conditions. Reshaping intestinal microbiota through pharmacologic means may be a viable treatment option. Here we sought to delineate the functional characteristics of glucocorticoid-mediated alterations on gut microbiota and their subsequent repercussions on host mucin regulation and colonic inflammation. Methods—Adult male C57Bl/6 mice, germ-free (GF), Muc2-heterozygote (±), or Muc2-knockout (-/-) were injected with dexamethasone, a synthetic glucocorticoid, for four weeks. Fecal samples were collected for gut microbiota analysis via 16S rRNA T-RFLP and amplicon sequencing. Intestinal mucosa was collected for mucin gene expression studies. GF mice were conventionalized with gut microbes from treated- and non-treated groups to determine their functional capacities in recipient hosts. Results—Exposure to DEX in WT mice led to substantial shifts in gut microbiota over a four-week period. Furthermore, a significant down-regulation of colonic Muc2 gene expression was observed after treatment. Muc2-knockout mice harbored a pro-inflammatory environment of gut microbes, characterized by the increase or decrease in prevalence of specific microbiota populations such as Clostridiales and Lactobacillaceae, respectively. This colitogenic phenotype was transmissible to IL10-knockout (IL10-KO) mice, a genetically susceptible model of colonic inflammatory disorders. Microbiota from donors pre-treated with DEX, however, ameliorated symptoms of inflammation. We conclude that commensal gut bacteria may be a key mediator of the anti-inflammatory effects observed in the large intestine after GC exposure. These findings underscore the notion that intestinal microbes comprise a “microbial organ” essential for host physiology that can be targeted by therapeutic approaches to restore intestinal homeostasis.
Ground-state properties of the Hubbard model on a two-dimensional square lattice are studied by the auxiliary-field quantum Monte Carlo method. Accurate results for energy, double occupancy, effective hopping, magnetization, and momentum distribution are calculated for interaction strengths of U/t from 2 to 8, for a range of densities including half-filling and n = 0.3,0.5,0.6, 0.75, and 0.875. At half-filling, the results are numerically exact. Away from half-filling, the constrained path Monte Carlo method is employed to control the sign problem. Our results are obtained with several advances in the computational algorithm, which are described in this paper in detail. We discuss the advantages of generalized Hartree-Fock trial wave functions and its connection to pairing wave functions, as well as the interplay with different forms of Hubbard-Stratonovich decompositions. We study the use of different twist angle sets when applying the twist averaged boundary conditions. We propose the use of quasirandom sequences, which improves the convergence to the thermodynamic limit over pseudorandom and other sequences. With it and a careful finite size scaling analysis, we are able to obtain accurate values of ground-state properties in the thermodynamic limit. Detailed results for finite-sized systems up to 16 × 16 are also provided for benchmark purposes.
Microorganisms catalyze carbon cycling and biogeochemical reactions in the deep subsurface and thus may be expected to influence the fate of injected super-critical (sc) CO 2 following geological carbon sequestration (GCS). We hypothesized that natural subsurface scCO 2 reservoirs, which serve as analogs for the long-term fate of sequestered scCO 2 harbor a ‘deep carbonated biosphere’ with carbon cycling potential. We sampled subsurface fluids from scCO 2 - water separators at a natural scCO 2 reservoir at McElmo Dome, Colorado for analysis of 16S rRNA gene diversity and metagenome content. Sequence annotations indicated dominance of Sulfurospirillum, Rhizobium, Desulfovibrio and four members of the Clostridiales family. Genomes extracted from metagenomes using homology and compositional approaches revealed diverse mechanisms for growth and nutrient cycling, including pathways for CO 2 and N 2 fixation, anaerobic respiration, sulfur oxidation, fermentation and potential for metabolic syntrophy. Differences in biogeochemical potential between two production well communities were consistent with differences in fluid chemical profiles, suggesting a potential link between microbial activity and geochemistry. In conclusion, the existence of a microbial ecosystem associated with the McElmo Dome scCO 2 reservoir indicates that potential impacts of the deep biosphere on CO 2 fate and transport should be taken into consideration as a component of GCS planning and modelling.
Here, quantitative color analysis is performed on videos of high contrast, low power reversible electrophoretic deposition (EPD)-based displays operated under different applied voltages. This analysis is coded in an open-source software, relies on a color differentiation metric, ΔE * 00 , derived from digital video, and provides an intuitive relationship between the operating conditions of the devices and their performance. Time-dependent ΔE * 00 color analysis reveals color relaxation behavior, recoverability for different voltage sequences, and operating conditions that can lead to optimal performance.