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Abstract not provided.
ABSTRACT The clinical severity and annual occurrence of influenza virus epidemics, combined with the availability of just a single class of antivirals to treat infections, underscores the urgent need to develop new anti-influenza drugs. The endonuclease activity within the viral acidic polymerase (PA) protein is an attractive target for drug discovery due to the critical role it plays in viral gene transcription. RO-7 is a next-generation PA endonuclease inhibitor of influenza A and B viruses, but its drug resistance potential is unknown. Through serial passage of influenza A(H1N1) viruses in MDCK cells under selective pressure of RO-7, we identified an I38T substitution within the PA endonuclease domain that conferred in vitro resistance to RO-7 (up to a 287-fold change in 50% effective concentration [EC 50 ]). I38T emerged between 5 and 10 passages, and when introduced into recombinant influenza A(H1N1) viruses, alone conferred RO-7 resistance (up to an 81-fold change in EC 50 ). Cocrystal structures of mutant and wild-type endonuclease domains with RO-7 provided the structural basis of resistance, where a key hydrophobic interaction between RO-7 and the Ile38 side chain is compromised when mutated to the polar threonine. While Ile38 does not have a crucial role in coordinating the endonuclease active site, the switch to threonine does affect the polymerase activity of some viruses and influences RO-7 affinity for the PA N target (i.e., the ≈200-residue N-terminal domain of PA). However, the change does not lead to a complete loss of replication activity in vitro . Our results predict that RO-7-resistant influenza viruses carrying the I38T substitution may emerge under treatment. This should be taken into consideration for clinical surveillance and in refinement of these drugs. IMPORTANCE The effectiveness of antiviral drugs can be severely compromised by the emergence of resistant viruses. Therefore, determination of the mechanisms by which viruses become resistant is critical for drug development and clinical use. RO-7 is a compound that potently inhibits influenza virus replication and belongs to a new class of drugs in late-stage clinical trials for treatment of influenza virus infection. Here we demonstrate that a single amino acid change acquired under prolonged virus exposure to RO-7 renders influenza viruses significantly less susceptible to its inhibitory effects. We have discovered how the mutation can simultaneously interfere with drug activity and still maintain efficient virus replication. These findings have important implications for the development of more effective derivatives of RO-7-like drugs and provide guidance for how to monitor the emergence of resistance.
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This study describes a study of techniques for identifying Higgs bosons at high transverse momenta decaying into bottom-quark pairs, $H → b\bar{b}$, for proton–proton collision data collected by the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy $\sqrt{\bar{s}}$ = 13 TeV. These decays are reconstructed from calorimeter jets found with the anti-$k_t R$ = 1.0 jet algorithm. To tag Higgs bosons, a combination of requirements is used: b -tagging of $R$ = 0.2 track-jets matched to the large- R calorimeter jet, and requirements on the jet mass and other jet substructure variables. The Higgs boson tagging efficiency and corresponding multijet and hadronic top-quark background rejections are evaluated using Monte Carlo simulation. Several benchmark tagging selections are defined for different signal efficiency targets. The modelling of the relevant input distributions used to tag Higgs bosons is studied in 36 fb -1 of data collected in 2015 and 2016 using $g → b\bar{b}$ and $Z(→ b\bar{b})γ$ event selections in data. Both processes are found to be well modelled within the statistical and systematic uncertainties.
Stochastic differential equations (SDEs) are used to model ultrawideband (UWB) indoor wireless channels. We show that the impulse responses for time-varying indoor wireless channels can be approximated in a mean-square sense as close as desired by impulse responses that can be realized by SDEs. The state variables represent the inphase and quadrature components of the UWB channel. The expected maximization and extended Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Both resolvable and nonresolvable multipath received signals are considered and represented as small-scaled Nakagami fading. The proposed models together with the estimation algorithm are tested using UWB indoor measurement data demonstrating the method’s viability and the results are presented.
Cold pool events occur when deep layers of stable, cold air remain trapped in a valley or basin for multiple days, without mixing out from daytime heating. With large impacts on air quality, freezing events, and es-pecially on wind energy production, they are often poorly forecast by modern mesoscale numerical weather prediction (NWP) models. Understanding the characteristics of cold pools is, therefore, important to provide more accurate forecasts. This study analyzes cold pool characteristics with data collected during the Second Wind Forecast Improvement Project (WFIP2), which took place in the Columbia River basin and Gorge of Oregon and Washington from fall 2015 until spring 2017. A subset of the instrumentation included three microwave radiometer pro?lers, six radar wind pro?lers with radio acoustic sounding systems, and seven sodars, which together provided seven sites with collocated vertical pro?les of temperature, humidity, wind speed, and wind direction. Using these collocated observations, we developed a set of criteria to determine if a cold pool was present based on stability, wind speed, direction, and temporal continuity, and then developed an automated algorithm based on these criteria to identify all cold pool events over the 18 months of the ?eld project. Characteristics of these events are described, including statistics of the wind speed distributions and pro?les, stability conditions, cold pool depths, and descent rates of the cold pool top. The goal of this study is a better understanding of these characteristics and their processes to ultimately lead to improved physical parameterizations in NWP models, and consequently improve forecasts of cold pool events in the study region as well at other locations that experiences similar events.
Despite the central role of soil microbial communities in global carbon (C) cycling, little is known about soil microbial community structure and even less about their metabolic pathways. Efforts to characterize soil communities often focus on identifying differences in gene content across environmental gradients, but an alternative question is what genes are similar in soils. These genes may indicate critical species or potential functions that are required in all soils. Here we identified the “core” set of C cycling sequences widely present in multiple soil metagenomes from a fertilized prairie (FP). Of 226,887 sequences associated with known enzymes involved in the synthesis, metabolism, and transport of carbohydrates, 843 were identified to be consistently prevalent across four replicate soil metagenomes. This core metagenome was functionally and taxonomically diverse, representing five enzyme classes and 99 enzyme families within the CAZy database. Though it only comprised 0.4% of all CAZy-associated genes identified in FP metagenomes, the core was found to be comprised of functions similar to those within cumulative soils. The FP CAZy-associated core sequences were present in multiple publicly available soil metagenomes and most similar to soils sharing geographic proximity. In soil ecosystems, where high diversity remains a key challenge for metagenomic investigations, these core genes represent a subset of critical functions necessary for carbohydrate metabolism, which can be targeted to evaluate important C fluxes in these and other similar soils.
remixed low-pressure tetrahydrofuran/oxygen/argon flames are investigated by photoionization molecular-beam mass spectrometry using vacuum-ultraviolet synchrotron radiation. For two equivalence ratios (φ = 1.00 and 1.75), mole fractions are measured as a function of distance from the burner for almost 60 intermediates with molar masses ranging from 2 (H 2 ) to 88 (C 4 H 6 O 2 ), providing a broad database for flame modeling studies. The isomeric composition is resolved by comparisons between experimental photoionization efficiency data and theoretical simulations, based on calculated ionization energies and Franck-Condon factors. Special emphasis is put on the resolution of the first reaction steps in the fuel destruction. The photoionization experiments are complemented by electron-ionization molecular-beam mass-spectrometry measurements that provide data with high mass resolution. For three additional flames with intermediate equivalence ratios (φ = 1.20, 1.40 and 1.60), mole fractions of major species and photoionization efficiency spectra of intermediate species are reported, extending the database for the development of chemical kinetic models.
Rapid acidification of the culture medium by the production of organic acids and the production of acid-induced proteases are key characteristics of the filamentous fungus Aspergillus niger. The D15 mutant of A. niger is non-acidifying mutant and used often for the expression of recombinant proteins in A. niger, because of its reduced production of extracellular proteases under non-acidic conditions. In this study, the D15 mutant is characterized in detail. Strongly reduced levels of citric and oxalic acid were observed in the D15 mutant both in shake flask cultures and in controlled batch cultivations. To identify the mutation in the D15 mutant, we successfully combined high-throughput sequencing (Illumina) with bulk segregant analysis. Because of the lack of a sexual cycle for A. niger, the parasexual cycle was used to generate a pool of segregants. From the 52 single nucleotide polymorphisms (SNPs) between the parental strains, three SNPs were homozygous in the genomic DNA of pool of segregants. These three SNPs mapped to all the right arm of chromosome II, indicating that this region contains the genetic locus affecting the phenotype related to acid production. Of the three SNPs, one mutation resulted in a missense mutation in the gene encoding the A. niger homologue of the A. nidulans methyltransferase gene laeA. Complementation analysis of the original mutant with the laeA gene and targeted disruption of laeA further confirmed that LaeA is involved in citric acid production in A. niger lab (N402) and citric acid production strains (ATCC 11414). Analysis of the secondary metabolite (SM) profile of the laeA mutants indicate that LaeA is required for the production of several SMs (asperrubrol, atromentin and JBIR86), but deletion of laeA also resulted in the presence of SMs (aspernigrin A/B and BMS-192548) that were not detected in the wild-type strain. The levels of ten other SMs were not strongly affected as a result of laeA deletion indicating that only a limited number of SM gene clusters are controlled by LaeA activity.
Abstract not provided.
Microbial communities were examined in surface stream sediments at 5 contaminated sites and 1 control site near Oak Ridge, TN, USA, to identify bacteria that could be contributing to mercury (Hg) methylation. The phylogenetic composition of the sediment bacterial community was examined over 3 quarterly sampling periods (36 samples) using 16S rRNA gene pyrosequencing. Only 3064 sequences (0.85% of the total community) were identified as Deltaproteobacteria, the only group known to methylate Hg, using the Ribosomal Database Project classifier at the 99% confidence threshold. Constrained ordination techniques indicated statistically significant positive linear correlations between Desulfobulbus spp., Desulfonema spp. and Desulfobacca spp. and methyl-Hg concentrations at the Hg-contaminated sites. In contrast, the distribution of organisms related to Byssovorax spp. was significantly correlated to inorganic carbon, nitrate and uranium concentrations but not to Hg or methyl-Hg. Overall, the abundance and richness of Deltaproteobacteria sequences were higher in uncontaminated sediments, while the majority of the members present at the contaminated sites were either known potential metal-reducers/methylators or metal tolerant species. Given the abundance relative to other known Hg methylators and the association with methyl-Hg, Desulfobulbus spp. is considered a prime candidate for involvement in Hg methylation in these streams.
The grain growth process in the Caltech water–ice dusty plasma experiment has been studied using a high-speed camera and a long-distance microscope lens. It is observed that (i) the ice grain number density decreases fourfold as the average grain major axis increases from 20 to 80 μ m, (ii) the major axis length has a log-normal distribution rather than a power-law dependence, and (iii) no collisions between ice grains are apparent. The grains have a large negative charge resulting in strong mutual repulsion and this, combined with the fractal character of the ice grains, prevents them from agglomerating. In order for the grain kinetic energy to be sufficiently small to prevent collisions between ice grains, the volumetric packing factor (i.e., ratio of the actual volume to the volume of a circumscribing ellipsoid) of the ice grains must be less than ∼0.1 depending on the exact relative velocity of the grains in question. Thus, it is concluded that direct accretion of water molecules is very likely to dominate the observed ice grain growth.
Abstract not provided.
High-resolution (HR) MS instruments recording HR-full scan allow analysts to go further beyond pre-acquisition choices. Untargeted acquisition can reveal unexpected compounds or concentrations and can be performed for preliminary diagnosis attempt. Then, revealed compounds will have to be identified for interpretations. Whereas the need of reference standards is mandatory to confirm identification, the diverse information collected from HRMS allows identifying unknown compounds with relatively high degree of confidence without reference standards injected in the same analytical sequence. However, there is a necessity to evaluate the degree of confidence in putative identifications, possibly before further targeted analyses. This is why a confidence scale and a score in the identification of (non-peptidic) known-unknown, defined as compounds with entries in database, is proposed for (LC-) HRMS data. The scale is based on two representative documents edited by the European Commission (2007/657/EC) and the Metabolomics Standard Initiative (MSI), in an attempt to build a bridge between the communities of metabolomics and screening labs. With this confidence scale, an identification (ID) score is determined as [a number, a letter, and a number] (e.g., 2D3), from the following three criteria: I, a General Identification Category (1, confirmed, 2, putatively identified, 3, annotated compounds/classes, and 4, unknown); II, a Chromatography Class based on the relative retention time (from the narrowest tolerance, A, to no chromatographic references, D); and III, an Identification Point Level (1, very high, 2, high, and 3, normal level) based on the number of identification points collected. Three putative identification examples of known-unknown will be presented. .
System identification is becoming a relevant research area for numerous applications in power grids due to the increasing complexity of the system. A paradigm shift in power system infrastructure driven by renewable energy resources, controllable loads, and new power electronics technologies have given rise to new challenges in power systems operation and control. This increased complexity of power systems and unavailability of physics-based models of most inverter-based resources requires that traditional modeling of power systems approaches be complemented by system identification-based black or grey box modeling techniques. In light of the importance of system identification and little attention paid to the applications of these techniques in transforming power systems, this paper provides a comprehensive review of active probing-based system identification methods in the context of power system applications. It reviews applications of both linear and nonlinear system identification with discussion on their potential and key takeaways. To motivate a further practical use in power systems, the paper provides an example of system identification, to develop a state space model of an unknown plant with step-by-step details. The paper highlights the advantages of using modern power electronics-based sources in the identification process and discusses the emerging research directions for future.
In response to the U.S. Department of Energy (DoE)’s goal of achieving market ready, net-zero energy residential and commercial buildings by 2020 and 2025, Eaton partnered with the Department of Energy’s National Renewable Energy Laboratory (NREL) and Georgia Institute of Technology to develop an intelligent load identification and management technology enabled by a novel “smart power strip” to provide critical intelligence and information to improve the capability and functionality of building load analysis and building power management systems. Buildings account for 41% of the energy consumption in the United States, significantly more than either transportation or industrial. Within the building sector, plug loads account for a significant portion of energy consumption. Plug load consumes 15-20% of building energy on average. As building managers implement aggressive energy conservation measures, the proportion of plug load energy can increase to as much as 50% of building energy leaving plug loads as the largest remaining single source of energy consumption. This project focused on addressing plug-in load control and management to further improve building energy efficiency accomplished through effective load identification. The execution of the project falls into the following three major aspects; An intelligent load modeling, identification and prediction technology was developed to automatically determine the type, energy consumption, power quality, operation status and performance status of plug-in loads, using electric waveforms at a power outlet level. This project demonstrated the effectiveness of the developed technology through a large set of plug-in loads measurements and testing; A novel “Smart Power Strip (SPS) / Receptacle” prototype was developed to act as a vehicle to demonstrate the feasibility of load identification technology as a low-cost, embedded solution; and Market environment for plug-in load control and management solutions, in particular, advanced power strips (APSs) was studied. The project evaluated the market potential for Smart Power Strips (SPSs) with load identification and the likely impact of a load identification feature on APS adoption and effectiveness. The project also identified other success factors required for widespread APS adoption and market acceptance. Even though the developed technology is applicable for both residential and commercial buildings, this project is focused on effective plug-in load control and management for commercial buildings, accomplished through effective load identification. The project has completed Smart Receptacle (SR) prototype development with integration of Load ID, Control/Management, WiFi communication, and Web Service. Twenty SR units were built, tested, and demonstrated in the Eaton lab; eight SR units were tested in the National Renewable Energy Lab (NREL) for one-month of field testing. Load ID algorithm testing for extended load sets was conducted within the Eaton facility and at local university campuses. This report is to summarize the major achievements, activities, and outcomes under the execution of the project.
Software applications for high resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) continue to enhance chemical identification capabilities. Given the variety of available applications, determining the most fit-for-purpose tools and workflows can be difficult. The Critical Assessment of Small Molecule Identification (CASMI) contests were initiated in 2012 to provide a means to evaluate compound identification tools on a standardized set of blinded tandem mass spectrometry (MS/MS) data. Five CASMI contests have resulted in recommendations, publications, and invaluable datasets for practitioners of HRMS-based screening studies. The US Environmental Protection Agency’s (EPA) CompTox Chemicals Dashboard is now recognized as a valuable resource for compound identification in NTA studies. However, this application was too new and immature in functionality to participate in the five previous CASMI contests. In this work, we performed compound identification on all five CASMI contest datasets using Dashboard tools and data in order to critically evaluate Dashboard performance relative to that of other applications. CASMI data was accessed via the CASMI webpage and processed for use in our spectral matching and identification workflow. Relative to applications used by former contest participants, our tools, data, and workflow performed well, placing more challenge compounds in the top five of ranked candidates than did the winners of three contest years and tying in a fourth. In addition, we conducted an in-depth review of the CASMI structure sets and made these reviewed sets available via the Dashboard. Our results suggest that Dashboard data and tools would enhance chemical identification capabilities for practitioners of HRMS-based NTA.