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Software calibration data and an optimum sequence for image processing technique application are developed for use in processing of S-056 telescope images.
Impedance-based methods are widely used for power system stability analysis with inverter-based resources (IBRs), e.g., assessing dynamic interactions between the power grid and an IBR, control interactions between multiple IBRs, and the sub-synchronous oscillation and damping phenomenon. Since it is difficult to get a numerical model 100% matching with the hardware IBR, using the hardware inverter directly to obtain its output impedance has become a prominent approach nowadays. Therefore, this article presents the impedance scan using hardware IBRs, and also a hardware diesel generator as it still stays with the grid before the grid completely goes to renewable. The devices under test (DuTs) for the impedance scan includes two 3-..phi.., 480 V, 60 Hz commercial grid-forming IBRs (one of 250 kVA and another of 125 kVA rating) in series with ..delta..-Y transformers, one 3-..phi.., 480 V, 60 Hz commercial grid-following IBR (of 125 kVA rating), and a 3-..phi.., 480 V, 60 Hz commercial diesel generator (of 187.5 kVA rating). Using voltage signals perturbed with sub-, inter-, and higher harmonic components, and measuring the current response, the positive-sequence impedances are computed via an offline-based post-analysis. Moreover, best-fit transfer functions are estimated that closely resemble the measured data points of the positive-sequence impedances. Based on the observations from various outcomes of the hardware experiments, this article also provides some fundamental insights on the equivalent positive-sequence impedance of a combination of multiple hardware components by comparing the estimated and the empirically computed impedances. A comparative insight on the damping capability of the DuTs using the positive-sequence impedances of the hardware is also discussed.
The purpose of this investigation was to examine microbial communities of simulated wastewater effluent from hollow fiber membrane bioreactors collected from the Space Life Science Laboratory and Texas Technical University. Microbes were characterized using quantitative polymerase chain reaction where a total count of bacteria and fungi were determined. The primers that were used to determine the total count of bacteria and fungi were targeted for 16S rDNA genes and the internal transcribed spacer, respectively. PCR products were detected with SYBR Green I fluorescent dye and a melting curve analysis was performed to identify unique melt profiles resulting from DNA sequence variations from each species of the community. Results from both the total bacteria and total fungi count assays showed that distinct populations were present in isolates from these bioreactors. This was exhibited by variation in the number of peaks observed on the melting curve analysis graph. Further analysis of these results using species-specific primers will shed light on exactly which microbes are present in these effluents. Information gained from this study will enable the design of a system that can efficiently monitor microbes that play a role in the biogeochemical cycling of nitrogen in wastewater on the International Space Station to assist in the design of a sustainable system capable of converting this nutrient.
SPAR is a large-scale computer program for finite element structural analysis. The program allows user specification of the order in which the joints of a structure are to be eliminated since this order can have significant influence over solution performance, in terms of both storage requirements and computer time. An efficient elimination sequence can improve performance by over 50% for some problems. Obtaining such sequences, however, requires the expertise of an experienced user and can take hours of tedious effort to affect. Thus, an automatic elimination sequence optimizer would enhance productivity by reducing the analysts' problem definition time and by lowering computer costs. Two possible methods for automating the elimination sequence specifications were examined. Several algorithms based on the graph theory representations of sparse matrices were studied with mixed results. Significant improvement in the program performance was achieved, but sequencing by an experienced user still yields substantially better results. The initial results provide encouraging evidence that the potential benefits of such an automatic sequencer would be well worth the effort.
Here, we report on the analysis of 34 years of photometric observations of the pulsating helium atmosphere white dwarf GD358. The complete data set includes archival data from 1982 to 2006, and 1195.2 hr of new observations from 2007 to 2016. From this data set, we extract 15 frequencies representing g-mode pulsation modes, adding 4 modes to the 11 modes known previously. We present evidence that these 15 modes are ℓ = 1 modes, 13 of which belong to a consecutive sequence in radial overtone k. We perform a detailed asteroseismic analysis using models that include parameterized, complex, carbon and oxygen core composition profiles to fit the periods. Recent spectroscopic analyses place GD358 near the red edge of the DBV instability strip, at 24,000 ± 500 K and a $\mathrm{log}\,{\text{}}g$ of 7.8 ± 0.08 dex. The surface gravity translates to a mass range of 0.455–0.540 ${M}_{\odot }$. Our best-fit model has a temperature of 23,650 K and a mass of 0.5706 ${M}_{\odot }$. That is slightly more massive than what is suggested by the most recent spectroscopy. We find a pure helium layer mass of 10–5.50, consistent with the result of previous studies and the outward diffusion of helium over time.
Problem: In the case of hypertrophy caused by pressure overload (hypertension) there is an increase in cardiac mass and modification cardiac metabolism. Aim: This study was designed to study the changes in glucose metabolism, ejection fraction, and deformation in the left ventricle with the progression of hypertrophy in spontaneous hypertensive rats (SHR). Methods: Dynamic PET data were acquired using the microPET II at UC Davis. Two rats were imaged at 10-week intervals for 18 months. Each time a dose of approximately 1- 1.5 mCi of F-18-FDG was injected into a normotensive Wistar Kyoto (WKY) rat and the same dose was injected into a SHR rat. Each rat was imaged using a gated dynamic acquisition for 80 minutes acquiring list mode data with cardiac gating of approximately 600-900 million total counts. For the analysis of glucose of metabolism, the list mode data were histogrammed into a dynamic sequence (42 frames over 80 mins). For each time frame, projection data of 1203 140x210 sinograms of 0.582 mm bins were formed by summing the last three gates before and one after the R-wave trigger to correspond to the diastolic phase of the cardiac cycle. Dynamic sequences of 128x128x83 matrices of 0.4x0.4x0.582 mm3 voxels in x, y, and z were reconstructed using an iterative MAP reconstruction which used a prior that penalized the high frequency components of the reconstruction using appropriate weighting between 26 nearest neighboring voxels. Time activity curves were generated from the dynamic reconstructed sequence for the blood and left ventricular tissue regions of interest which were fit to a 2-compartment model to obtain a least squares fit for the kinetic parameters. For the analysis of deformation, the list mode data were histogrammed into 8 gates of the cardiac cycle, each gate was the total sum of the later 60 mins of the 80 min acquisition. Images of 128x128x83 matrices for each gate were reconstructed using the same iterative MAP reconstruction used to reconstruct the dynamic sequence. The in-plane image dimensions were doubled to 256x256x83 in order to increase the resolution for the Warping analyses. These image data sets were then cropped to 128x128x83. The end-systolic image data sets were designated as the template images and the end-diastole image data sets were designated as the target images, thus providing an analysis of the diastolic relaxation and filling phases of the cardiac cycle. The template images were manually segmented to create surface definitions representing the epi- and endocardial surfaces. Finite element models of the left ventricles were created using the segmented surfaces and defining a transversely isotropic material with fiber angles varying from the epicardial surface to the endocardial surface. A Warping analyses was performed to obtain the LV strain tensor and fiber stretch distributions. Results: In one study, the average first principal Green-Lagrange strain, fiber stretch, ejection fraction, and metabolic rate of F-18-FDG was 0.22, 1.08, 80%, 0.1 for the WKY rat and 0.16, 1.06, 50%, 0.25 for the SHR rat, respectively. These same rats studied a year later presented with a metabolic rate of F-18-FDG of 0.11 and 0.25 for the WKY and SHR, respectively. A follow-up study the average strain (n=10) and ejection fraction (n=18) was 0.21, 72.7% for WKY and 0.17, 69.8% for the SHR, respectively. Conclusion: In the case of pressure overload there is an increased reliance on carbohydrate oxidation in an attempt to maintain contractile function.
Streptococcus suis is a zoonotic bacterial swine pathogen causing substantial economic and health burdens to the pork industry. Mechanisms used by S. suis to colonize and cause disease remain unknown and vaccines and/or intervention strategies currently do not exist. Studies addressing virulence mechanisms used by S. suis have been complicated because different isolates can cause a spectrum of disease outcomes ranging from lethal systemic disease to asymptomatic carriage. The objectives of this study were to evaluate the virulence capacity of nine United States S. suis isolates following intranasal challenge in swine and then perform comparative genomic analyses to identify genomic attributes associated with swine-virulent phenotypes. No correlation was found between the capacity to cause disease in swine and the functional characteristics of genome size, serotype, sequence type (ST), or in vitro virulence-associated phenotypes. A search for orthologs found in highly virulent isolates and not found in non-virulent isolates revealed numerous predicted protein coding sequences specific to each category. While none of these predicted protein coding sequences have been previously characterized as potential virulence factors, this analysis does provide a reliable one-to-one assignment of specific genes of interest that could prove useful in future allelic replacement and/or functional genomic studies. Collectively, this report provides a framework for future allelic replacement and/or functional genomic studies investigating genetic characteristics underlying the spectrum of disease outcomes caused by S. suis isolates.
The African swine fever virus (ASFV) is currently causing a world-wide pandemic of a highly lethal disease in domestic swine and wild boar. Currently, recombinant ASF live-attenuated vaccines based on a genotype II virus strain are commercially available in Vietnam. With 25 reported ASFV genotypes in the literature, it is important to understand the molecular basis and usefulness of ASFV genotyping, as well as the true significance of genotypes in the epidemiology, transmission, evolution, control, and prevention of ASFV. Historically, genotyping of ASFV was used for the epidemiological tracking of the disease and was based on the analysis of small fragments that represent less than 1% of the viral genome. The predominant method for genotyping ASFV relies on the sequencing of a fragment within the gene encoding the structural p72 protein. Genotype assignment has been accomplished through automated phylogenetic trees or by comparing the target sequence to the most closely related genotyped p72 gene. To evaluate its appropriateness for the classification of genotypes by p72, we reanalyzed all available genomic data for ASFV. We conclude that the majority of p72-based genotypes, when initially created, were neither identified under any specific methodological criteria nor correctly compared with the already existing ASFV genotypes. Based on our analysis of the p72 protein sequences, we propose that the current twenty-five genotypes, created exclusively based on the p72 sequence, should be reduced to only six genotypes. To help differentiate between the new and old genotype classification systems, we propose that Arabic numerals (1, 2, 8, 9, 15, and 23) be used instead of the previously used Roman numerals. Furthermore, we discuss the usefulness of genotyping ASFV isolates based only on the p72 gene sequence.
Common Bolted Joint Analysis Tool (comBAT) is an Excel/VB-based bolted joint analysis/optimization program that lays out a systematic foundation for an inexperienced or seasoned analyst to determine fastener size, material, and assembly torque for a given design. Analysts are able to perform numerous what-if scenarios within minutes to arrive at an optimal solution. The program evaluates input design parameters, performs joint assembly checks, and steps through numerous calculations to arrive at several key margins of safety for each member in a joint. It also checks for joint gapping, provides fatigue calculations, and generates joint diagrams for a visual reference. Optimum fastener size and material, as well as correct torque, can then be provided. Analysis methodology, equations, and guidelines are provided throughout the solution sequence so that this program does not become a "black box:" for the analyst. There are built-in databases that reduce the legwork required by the analyst. Each step is clearly identified and results are provided in number format, as well as color-coded spelled-out words to draw user attention. The three key features of the software are robust technical content, innovative and user friendly I/O, and a large database. The program addresses every aspect of bolted joint analysis and proves to be an instructional tool at the same time. It saves analysis time, has intelligent messaging features, and catches operator errors in real time.
Cowpea is one of the major legumes cultivated in arid and semiarid regions of the world. Four soil-microbial samples (SS-1 through SS-4) collected from semiarid soils in Punjab, Pakistan were planted with cowpea (Vigna unguiculata) crops, which were grown under salinity stress to analyze bacterial composition in the rhizosphere and within nodules using cultivation-dependent and -independent methods. Two varieties, 603 and the salt-tolerant CB 46, were each inoculated with or without the four different native soil samples or grown in medium either N-deficient (-N) or supplemented with N (+N). Plants inoculated with soil samples SS-2 and SS-4 grew better than plants inoculated with SS-1- and SS-3 and grew comparably with the +N controls. Environmental DNA (eDNA) was isolated from SS-1 and SS-4, and, by 16S ribosomal RNA sequencing, the soil microbiomes consisted mainly of Actinobacteria, Firmicutes, Proteobacteria, and other nonproteobacterial genera. However, analysis of eDNA isolated from cowpea nodules established by the trap plants showed that the nodule microbiome consisted almost exclusively of proteobacterial sequences, particularly species of Bradyrhizobium. Bacteria were isolated from both soils and nodules, and 34 of the 51 isolates tested positive for plant-growth-promoting rhizobacteria traits in plate assays. Many could serve as future inocula for crops in arid soils. The discrepancy between the types of bacteria isolated by culturing bacteria isolated from surface-sterilized cowpea nodules (proteobacteria and nonproteobacteria) versus those detected by sequencing DNA isolated from the nodules (proteobacteria) from cowpea nodules (proteobacteria and nonproteobacteria) versus those detected in the nodule microbiome (proteobacteria) needs further study.
The greatest potential for carbon storage in the northeastern United States lies in the offshore geologic formations comprising the outer continental shelf (Monteverde et al., 2011). Offshore storage can be linked to large point-sources of carbon dioxide (CO 2 ) while avoiding many of the logistical difficulties and potential risks encountered when siting onshore projects, especially in densely populated areas of the East Coast. Offshore CO 2 storage assessment and research in the United States is underway to address uncertainty in potential storage resources, particularly in the mid- and north-Atlantic offshore area. Given the current knowledge base and access to publicly available data, the objectives of the Mid-Atlantic U.S. Offshore Carbon Storage Resource Assessment Project (MAOCSRAP) were fourfold: (1) complete a systematic carbon storage resource assessment of the mid-Atlantic offshore coastal region from the Georges Bank Basin (GBB) through the Long Island Platform (LIP) to the southern Baltimore Canyon Trough (BCT); (2) define key input parameters to reduce uncertainty for offshore storage resource and efficiency estimates; (3) perform a preliminary assessment of risk factors, uncertainties, and data gaps; and (4) engage industry and regulatory stakeholders through development of a Road Map to assist future project planning and implementation. The MAOCSRAP project has compiled, inventoried, and assimilated various publicly available data sets to provide a strong technical basis on which future carbon storage studies and applications can be built. The knowledge infrastructure necessary to support the development of full-scale offshore carbon storage include: Defining the geologic characteristics of candidate storage sites (Chapter 2). Using existing seismic data to better define the continuity of the storage zones and seals (Chapter 3). Cataloguing the hydrogeologic properties of mid-Atlantic offshore storage sites (Chapter 4). Calculating prospective CO 2 storage resources using net effective pore volumes and fluid displacement properties specific to offshore lithologies (Chapter 5). Examining risk factors related to offshore storage (Chapter 6). Communicating with industry and other stakeholders about the future prospects for offshore storage (Chapter 7). Ensuring technology transfer to industry and other stakeholders (Chapter 8). Led by Battelle, this project was conducted by public and private entities with expertise in offshore geology and resources for the study region, including state geological surveys of Delaware, Maryland, and Pennsylvania; United States Geological Survey-Woods Hole Coastal and Marine Science Center and Haifa University; Rutgers University; Harvard University; and Lamont-Doherty Earth Observatory (LDEO) at Columbia University. The storage resource assessment was completed for a broad region offshore of the U.S. East Coast, from Massachusetts to Virginia. The results include high-level storage resource estimates for areas not previously characterized and improved storage resource estimates for geographically expansive portions of offshore geologic units. Results of the project provide a foundation for developing CO 2 storage along offshore areas of the mid-Atlantic United States. A combination of information was used to provide confidence in project results. Key findings of the major project tasks are summarized as follows. Subsurface Data Analysis: Legacy seismic, well-log, core, and biostratigraphic data were digitized, reprocessed, and analyzed using modern techniques, augmenting previous characterization efforts. The sequence stratigraphic framework for the Cretaceous and Jurassic strata was developed by correlating well-log depositional sequences to seismic reflectors identified in the BCT, LIP, and GBB. The integration of well-log stratigraphy with seismic stratigraphy and petrophysical analysis helped to identify and map potential storage zones within sequences of the Logan Canyon (LC), Missisauga, and Mohawk formations, as well as the regionally extensive caprock comprised of the Dawson Canyon shale. Approximately 4,000 kilometers (km) of seismic data was reprocessed to create a structural framework that was used to tie together all available well data, constrain sequence stratigraphic interpretations, and make regional assessments of offshore carbon storage resources. Prospective storage resource estimates were 150 to 1136 megatons (Mt) for the combined storage zones. This suggests mid-Atlantic U.S. offshore formations can store decades of CO 2 from industrial sources in the region. Preliminary local reservoir simulations suggest that injection rates of 1 Mt CO 2 /year may be sustained for 30 years in single injection wells in the BCT. Advanced geologic modeling and new data acquisition are needed to address data gaps and advance carbon capture and storage (CCS) in key offshore areas selected for further investigation. Offshore Risk Factors: Offshore geologic risk factors include soft sediment deformation, unit continuity, sedimentological and structural features, seismicity and hydrates. CO 2 storage risks include inadequate seals, migration/leakage, and chemical interactions leading to decreased storage. Sensitive habitats, environmental impacts, disturbance to seafloor, and other risks need to be identified in advance of project activities and integrated into detailed mitigation plans for all project phases. Stakeholder Engagement: Input and participation from government, industry, and environmental groups provided input into the Road Map and address next steps needed for project deployment. Early engagement and ongoing communication, as well as policy framework development, is key to large-scale deployment of CCS. Next Steps: This project represents an important first step by completing a high-level CO 2 storage resource assessment and building the knowledge infrastructure necessary to improve quantitative storage resource estimates. The data sets that have been curated under this project provide an opportunity to conduct research and development (R&D) needed to address data gaps and reduce risk and uncertainty. Offshore characterization and validation strategies that are systematically designed to provide data and infrastructure that can be upscaled to meet commercial requirements could be developed off of this foundation.
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.
Gram-negative bacteria expel various toxic chemicals via tripartite efflux pumps belonging to the resistance-nodulation-cell division superfamily. These pumps span both the inner and outer membranes of the cell. The three components of these tripartite systems are an inner-membrane, substrate-binding transporter (or pump); a periplasmic membrane fusion protein (or adaptor); and an outer-membrane-anchored channel. These three efflux proteins interact in the periplasmic space to form the three-part complexes. We previously presented the crystal structures of both the inner-membrane transporter CusA and membrane fusion protein CusB of the CusCBA tripartite efflux system from Escherichia coli. We also described the co-crystal structure of the CusBA adaptor-transporter, revealing that the trimeric CusA efflux pump assembles with six CusB protein molecules to form the complex CusB{sub 6}-CusA{sub 3}. We here report three different conformers of the crystal structures of CusBA-Cu(I), suggesting a mechanism on how Cu(I) binding initiates a sequence of conformational transitions in the transport cycle. Genetic analysis and transport assays indicate that charged residues, in addition to the methionine pairs and clusters, are essential for extruding metal ions out of the cell.