Rhenium elemental and isotopic variations at magmatic temperatures

This work was funded by Natural Environment Research Council UK Standard Grant to RGH, AJD, and JP (NE/T001119).Recent analytical advances in the measurement of rhenium (Re) isotope ratios allow its potential as a palaeoredox and chemical weathering proxy to be explored. However, a successful isotopic proxy must be grounded by an understanding of its composition and behaviour in the solid Earth. Here, we present Re concentrations and Re isotopic (δ187Re) compositions for a well-characterised sequence of lavas from Hekla volcano, Iceland. The concentration of Re varies from 0.02 to 1.4 ng/g, decreasing from basalt to more evolved lavas. We show that the crystallisation and removal of magnetite is responsible for the Re decrease in this system. By contrast, δ187Re values for the same suite of samples show a relatively narrow range (−0.45 to −0.22 0/1000), suggesting minimal resolvable Re isotope fractionation between magnetite and the silicate melt. Together with other samples, including mid-ocean ridge basalts, these first igneous data can be used to estimate a baseline for terrestrial materials (δ187Re = −0.33 ± 0.15 0/1000, 2 s.d., n = 14), from which low-temperature Re isotope variations in Earth’s surficial environments can be assessed, alongside the global isotope mass balance of Re.Peer reviewe

The Use of Hibernation Modes for Deep Space Missions as a Method to Lower Mission Operations Costs

Hibernation modes have been used in the past in the commercial and military sector under the name of ‘on-orbit storage’ or ‘onorbit spare’. Satellites that have used this technique include: METEOSAT-5 (Europe). SOOS (Navy), NOVA (Navy), GOES-10 (NOAA). These spares are used to quickly restore functionality of the system if there should be an on-orbit failure. Up until now, scientific satellites have not incorporated hibernation modes in their baseline mission timeline – though several missions used them to perform extended missions: GIOTTO and ISEE- 3/ICE. During its competitive proposal phase, CONTOUR baselined a hibernation mode as a method to reduce mission operations costs and to reduce the burden on DSN resources. CONTOUR’s hibernation mode reduces the spacecraft’s required functionality to a minimum in a power stable, thermally stable, spin stabilized attitude. The European Space Agency (ESA) Rosetta mission is also implementing a hibernation mode in its baseline mission. This paper is intended to educate the reader on the different facets of CONTOUR’s hibernation mode and issues that were taken into account during design

Transatlantic developmental migrations of loggerhead sea turtles demonstrated by mtDNA sequence analysis

Molecular markers based on mitochondrial (mt) DNA control region se quences were used to test the hypothesis that juvenile loggerhead sea turtles (Caretta caretta) in pelagic habitats of the eastern Atlantic are derived from nesting populations in the western Atlantic. We compared mtDNA haplotypes from 131 pelagic juvenile turtles (79 from the Azores and 52 from Madeira) to mtDNA haplotypes observed in major nesting colonies of the Atlantic Ocean and Mediterranean Sea. A subset of 121 pelagic samples (92%) contained haplotypes that match mtDNA sequences observed in nesting colonies. Maximum likelihood analyses (UCON, SHADRACQ) estimate that 100% of these pelagic juveniles are from the nesting populations in the southeastern United States and adjacent Yucatan Peninsula, Mexico. Estimated contributions from nesting populations in south Florida (0.71, 0.72), northern Florida to North Carolina (0.19, 0.17), and Quintana Roo, Mexico (0.11, 0.10) are consistent with the relative size of these nesting aggregates. No contribution was detected from nesting colonies in the Mediterranean (Greece) or South Atlantic (Brazil), although samples sizes are insufficient to exclude these locations with finality. The link between west Atlantic nesting colonies and east Atlantic feeding grounds provides a more complete scientific basis for assessing the impact of subadult mortality in oceanic fisheries. Demographic models for loggerhead turtles in the western Atlantic can now be improved by incorporating growth and mortality data from juvenile turtles in pelagic habitats. These data demonstrate that the appropriate scale for loggerhead turtle conservation efforts is vastly larger than the current scale of management plans based on political boundaries.info:eu-repo/semantics/publishedVersio

The closest elastic tensor of arbitrary symmetry to an elasticity tensor of lower symmetry

The closest tensors of higher symmetry classes are derived in explicit form for a given elasticity tensor of arbitrary symmetry. The mathematical problem is to minimize the elastic length or distance between the given tensor and the closest elasticity tensor of the specified symmetry. Solutions are presented for three distance functions, with particular attention to the Riemannian and log-Euclidean distances. These yield solutions that are invariant under inversion, i.e., the same whether elastic stiffness or compliance are considered. The Frobenius distance function, which corresponds to common notions of Euclidean length, is not invariant although it is simple to apply using projection operators. A complete description of the Euclidean projection method is presented. The three metrics are considered at a level of detail far greater than heretofore, as we develop the general framework to best fit a given set of moduli onto higher elastic symmetries. The procedures for finding the closest elasticity tensor are illustrated by application to a set of 21 moduli with no underlying symmetry.Comment: 48 pages, 1 figur

Influence of Acute Water Ingestion on Bioelectrical Impedance Analysis Estimates of Body Composition

Body composition estimation is a significant component of health and fitness assessments. Multi-frequency bioelectrical impedance analysis (MFBIA) uses multiple electrical frequencies that travel through body tissues in order to estimate fluid content and body composition. Prior to body composition assessments, it is common to implement a wet fast (i.e., a fasting period that allows water intake); however, the influence of a wet fast as compared to a dry fast (i.e., disallowing water intake) is relatively unknown. PURPOSE: To determine the effects of acute water consumption on MFBIA body composition estimates. METHODS: A randomized crossover study was conducted in 16 adults (8 F, 8 M; age: 22.0 ± 2.9 y; height: 173.6 ± 9.9 cm; weight: 74.3 ± 21.6 kg; body mass index: 24.6 ± 4.7; body fat % [BF%]: 16.7 ± 8.1%). On two occasions, participants reported to the laboratory after an overnight food and fluid fast. After a baseline MFBIA assessment, participants either consumed 11 mL/kg of bottled water (W condition) or consumed no fluid as the control (CON condition). The 11 ml/kg dose of water corresponded to absolute intakes of 531 to 1360 mL. After the water consumption time point, MFBIA tests were performed every 10 minutes for one hour. Participants stood upright for the entire research visit. MFBIA estimates of body mass (BM), fat mass (FM), fat-free mass (FFM), and BF% were analyzed using 2 x 7 (condition x time) analysis of variance with repeated measures, follow-up pairwise comparisons, and evaluation of the partial eta-squared (ηp2) effect sizes. RESULTS: No variables differed between conditions at baseline. Condition x time interactions were present for all variables (BM: pp2=0.89; FM: p=0.0008, ηp2=0.30; BF%: p=0.005, ηp2=0.23) except FFM (p=0.69, ηp2=0.03). Follow-up testing indicated that BM was ~0.6 kg higher in W as compared to CON at all post-baseline time points (pp2=0.32), regardless of condition. CONCLUSION: Up to one hour after ingestion, acute water intake was exclusively detected as increased FM by MFBIA. This contrasts with the common belief that ingesting water prior to bioimpedance tests would result in inflated FFM and decreased BF%. Since body composition estimates never returned to baseline within the hour after water ingestion, it is not clear how long this effect would persist. These results suggest acute water ingestion can produce an inflation of MFBIA body fat estimates for at least one hour. These results indicate that water intake during fasting periods should be considered as part of pre-assessment standardization

Tracking Resistance Training-Induced Changes in Body Composition via 3-Dimensional Optical Scanning

Tracking changes in body composition is potentially useful for monitoring health status, disease risk, and results of lifestyle interventions. In active individuals, evaluating body composition changes over time may provide useful information regarding the effectiveness of nutrition and exercise programs. PURPOSE: The purpose of this study was to compare changes in body composition estimates obtained from a 4-compartment (4C) model and a 3-dimensional optical (3DO) scanner in resistance-trained males. METHODS: Twenty resistance-trained males underwent assessments via 4C and 3DO before and after 6 weeks of supervised resistance training plus overfeeding with a high-calorie protein/carbohydrate supplement. To generate the 4C model, tests were performed using dual-energy x-ray absorptiometry, air displacement plethysmography, and bioimpedance spectroscopy. Changes in fat mass (ΔFM) and fat-free mass (ΔFFM) detected by 3DO were compared with the reference 4C model using paired-samples t-tests, Bland-Altman analysis, equivalence testing, and evaluation of validity metrics. RESULTS: Both ΔFM (mean ± SD: 4C: 0.6 ± 1.1 kg; 3DO: 1.9 ± 1.9 kg) and ΔFFM (4C: 3.2 ± 1.7 kg; 3DO: 1.9 ± 1.4 kg) differed between methods (p \u3c 0.002). The correlation (r) for ΔFM was 0.49 (95% confidence interval [CI]: 0.06 to 0.77) and was 0.42 (95% CI: -0.03 to 0.73) for ΔFFM. The total error for ΔFM and ΔFFM estimates was 2.1 kg. ΔFFM demonstrated equivalence between methods based on a ± 2 kg (~62% of 4C change) equivalence interval, whereas ΔFM failed to exhibit equivalence even with a 100% equivalence interval. Proportional bias was observed for ΔFM but not ΔFFM. CONCLUSION: Our data indicate that changes in FM and FFM detected by a 3D scanner did not exhibit strong agreement with changes detected by a 4C model. However, within the context of our study, agreement in FFM changes was superior to agreement in FM changes based on the results of equivalence testing and lack of proportional bias in FFM changes. Therefore, depending on the level of accuracy needed, the error in FFM changes observed for the 3D scanner may be potentially acceptable for some applications. Future research should investigate the utility of 3D scanners for monitoring changes in body composition and anthropometric variables in healthy and clinical populations, as well as investigate novel body phenotypes that may be associated with disease risk or health status

Comparison of Indirect Calorimetry and Common Prediction Equations for Evaluating Changes in Resting Metabolic Rate Induced by Resistance Training and a Hypercaloric Diet

An individual’s resting metabolic rate (RMR) is commonly the largest contributor to total daily energy expenditure. Prediction equations are most often employed by practitioners to estimate RMR, due to their superior practicality in many settings relative to laboratory methods like indirect calorimetry (IC). The ability to quantify RMR change over time may be more valuable than cross-sectional estimates as practitioners can then utilize these changes to prescribe adjustments to one’s nutritional intake. PURPOSE: The purpose of this study was to assess the validity of several commonly used prediction equations to track RMR changes during a hypercaloric nutrition intervention and supervised exercise training program. METHODS: Twenty generally healthy males (mean ± standard deviation; age: 21.9 ± 2.6 years; height: 178.1 ± 6.9 cm; body mass: 72.2 ± 7.3 kg; fat-free mass index: 18.9 ± 1.5 kg/m2 ; bench press strength: 1.3 ± 0.2 kg/kg BM; leg press strength: 3.4 ± 0.9 kg/kg BM) completed a supervised resistance training program in conjunction with a hypercaloric diet. The protocol lasted 6 weeks, and participants completed RMR assessments via IC pre-and post-intervention to obtain reference values. Existing RMR prediction equations based on body mass or fat-free mass were also evaluated. Equivalence testing was used to evaluate whether each prediction equation demonstrated equivalence with IC based on a ± 50 kcal/d equivalence region, and the confidence limits for the two-one-sided t-tests were calculated. Null hypothesis significance testing was performed, and Bland-Altman analyses were utilized alongside linear regression to assess the degree of proportional bias. RESULTS: IC RMR values increased by 165 ± 97 kcal/d. All prediction equations underestimated RMR changes, relative to IC, with magnitudes ranging from 75 to 132 kcal/d, while also displaying unacceptable levels of negative proportional bias. Additionally, all prediction equations significantly differed from measured IC values, and no equation demonstrated equivalence with IC. CONCLUSION: These findings suggest the examined prediction equations are not acceptable for tracking RMR changes in resistance-trained males, within the context of the present study. The consistent underestimation of RMR changes indicates that the input variables, and their weights within the prediction equations, were insufficient to adequately explain the observed changes in RMR

Influence of Subject Presentation on Body Composition Estimates from Dual-Energy X-Ray Absorptiometry, Air Displacement Plethysmography, and Bioelectrical Impedance Analysis

Body composition assessment devices are commonly employed to track changes associated with exercise or nutritional interventions. However, many individuals undergo body composition assessments with little to no pre-testing standardization of dietary intake or physical activity, potentially introducing error into their results. PURPOSE: To examine the validity of unstandardized body composition assessments relative to standardized assessments using three common body composition assessment devices. METHODS: Twenty-three resistance-trained males (Mean ± SD; 21.6 ± 2.6 years; 71.3 ± 6.8 kg; 177.4 ± 5.9 cm; 17.4 ± 4.1% DXA-derived percent body fat [%BF]) underwent paired body composition assessments via dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), and single-frequency bioelectrical impedance analysis (BIA). Each participant’s initial standardized body composition assessments were performed in the morning following an overnight food and fluid fast and 12 hours of exercise and caffeine abstention, and all unstandardized assessments were performed later during the same day following ad libitum daily activities. Unstandardized estimates of %BF and fat-free mass (FFM) for each device were compared with device-specific standardized values using paired-samples t-tests, line of identity analysis, evaluation of validity metrics, Bland-Altman analysis, and equivalence testing. RESULTS: The total error between standardized and unstandardized %BF estimates was 0.66% for DXA [95% confidence interval {CI}: 0.56-0.76%], 1.60% for ADP [95% CI: 1.50-1.70%], and 1.85% for BIA [95% CI: 1.75-1.95%]. The total error for FFM estimates was 0.75kg for DXA [95% CI: 0.65-0.85kg], 1.15kg for ADP [95% CI: 1.06-1.25kg], and 1.68 kg for BIA [95%CI: 1.58-1.78]. %BF estimates did not differ between paired measurements for DXA (p = 0.17) or ADP (p = 0.10) but differed between BIA (p \u3c 0.001) assessments. Similarly, FFM estimates did not differ between paired measurements for DXA (p = 0.40) or ADP (p = 0.78) but differed between BIA assessments (p \u3c 0.001). All paired assessments for each outcome produced regression line slopes which differed from the line of identity (p \u3c 0.001). Only BIA %BF estimates exhibited an intercept that differed from the line of identity (p \u3c 0.001). No proportional bias was detected for any outcome. Equivalence was demonstrated between %BF estimates for DXA but not ADP or BIA, based on a ±1%BF equivalence interval. Equivalence was demonstrated for all FFM estimates except BIA, based on a ±1kg equivalence interval. CONCLUSION: Our findings suggest that DXA body composition estimates are more robust when conducted in an unstandardized state relative to ADP or BIA. These results can inform the choice of body composition assessment methodology when pre-testing standardization is not possible

Influence of acute water ingestion and prolonged standing on raw bioimpedance and subsequent body fluid and composition estimates

This study evaluated the influence of acute water ingestion and maintaining an upright posture on raw bioimpedance and subsequent estimates of body fluids and composition. Twenty healthy adults participated in a randomized crossover study. In both conditions, an overnight food and fluid fast was followed by an initial multi-frequency bioimpedance assessment (InBody 770). Participants then ingested 11 mL/kg of water (water condition) or did not (control condition) during a 5-minute period. Thereafter, bioimpedance assessments were performed every 10 minutes for one hour with participants remaining upright throughout. Linear mixed effects models were used to examine the influence of condition and time on raw bioimpedance, body fluids, and body composition. Water consumption increased impedance of the arms but not trunk or legs. However, drift in leg impedance was observed, with decreasing values over time in both conditions. No effects of condition on body fluids were detected, but total body water and intracellular water decreased by ~0.5 kg over time in both conditions. Correspondingly, lean body mass did not differ between conditions but decreased over the measurement duration. The increase in body mass in the water condition was detected exclusively as fat mass, with final fat mass values ~1.3 kg higher than baseline and also higher than the control condition. Acute water ingestion and prolonged standing exert practically meaningful effects on relevant bioimpedance variables quantified by a modern, vertical multi-frequency analyzer. These findings have implications for pre-assessment standardization, methodological reporting, and interpretation of assessments

Relationship Between Muscular Performance Changes and Increases in Body Mass During Overfeeding Plus Resistance Training

Two critical components of muscular performance are muscular strength (e.g., the maximal load that can be lifted for a given exercise) and muscular endurance (e.g., the maximal number of repetitions that can be performed at a given load). When seeking improvements in muscular performance, it is common to employ nutritional strategies that create an energy surplus and a resultant gain in body mass. Varying rates of body mass gain are often prescribed to optimize training adaptations, including improvements in muscular performance; however, the relationship between rate of body mass gain and muscular performance improvements, if any, is not entirely clear. PURPOSE: The purpose of this analysis was to elucidate if there is a relationship between the rate of body mass gain and changes in muscular performance resulting from a resistance training program. METHODS: Nineteen resistance-trained males (age: 21.7 ± 2.6; body mass [BM]: 74.1 ± 11.5 kg; body fat percentage: 13.7 ± 5.2%; bench press maximal strength: 1.3 ± 0.2 x BM; leg press maximal strength: 3.4 ± 0.9 x BM) completed a supervised resistance training program plus overfeeding. Muscular performance testing took place at baseline and after the 6-week intervention. For the bench press and leg press exercises, strength was assessed via 1-repetition maximum (1RM), and endurance was assessed via repetitions to failure using 70% of the baseline 1RM. Simple linear regression analysis was used to determine if the relative rate of BM gain was related to relative improvements in maximal muscular strength and endurance. Standardized regression coefficients (β) and associated 95% confidence intervals (CI) were generated. RESULTS: The rate of BM gain was related to improvements in bench press 1RM (p=0.05; β=0.46 [0.02, 0.89], mean [95% CI]) and endurance (p=0.007, β =0.61 [0.23, 1.00]), but not leg press 1RM (p=0.16, β =0.33 [-0.11, 0.78]) or endurance (p=0.76, β = 0.08 [-0.42, 0.58]). A 1.0% increase in the relative rate of BM gain corresponded to relative increases of 1.2% (CI of 0.1 to 2.4%) in bench press 1RM and 6.7% (CI of 2.5 to 10.9%) in bench press repetitions to failure. CONCLUSION: The relative rate of body mass gain was positively related to performance improvements in the bench press exercise, but not the leg press exercise. One speculative explanation for this relationship is that the increase in upper body muscularity that results from body mass gain during resistance training could have decreased the range of motion on the bench press exercise, thereby facilitating easier execution of the movement for both strength and endurance tests