3-component Model Research Articles

Cm(III) speciation in the presence of citrate from neutral to hyperalkaline conditions and the effect of calcium

We investigated the binary Cm-citrate system using time-resolved laser fluorescence spectroscopy (TRLFS), parallel factor analysis (PARAFAC), and quantum chemical calculations. Evidence collectively suggests the stepwise coordination and deprotonation of citrate alcohol groups in Cm-cit complexes with two bound citrate moieties upon increasing pH, which is supported by a bathochromic shift in emission spectra, an observed increase in lifetime measurements, and lower energy minima for citrate alcohol involvement versus hydrolysis of the Cm-citrate species. Our PARAFAC results agree with a 3-component model for the Cm-citrate system and offer pure component decompositions, yielding fraction species across the studied pH range that have a correlated slope = 1 as a function of pH. For the first time, evidence of ternary Ca-Cm-citrate complexes was revealed by TRLFS with increasing calcium concentration at fixed pHm. The formation of these ternary complexes was substantiated with density functional theory (DFT) calculations on simple model systems of the complexes. Shared citrate carboxylate groups between calcium and curium were proposed for all three ternary Ca-Cm-cit complexes based on DFT-determined Ca–O and Cm–O distances. Moreover, we found that the ternary complex with both alcohol groups deprotonated is most stable when it shares both two carboxylate and two alcohol groups between Ca and Cm. The presence of shared functional groups highlights the enhanced stability of these ternary complexes. Additional work is warranted to further constrain the stoichiometry, stability constants and dependence on ionic strength of these complexes for purposes of thermodynamic modeling of repository settings.

Roles of fuel composition on the ignition process of endothermic hydrocarbons

Due to the characteristics of heat absorption and decomposition, endothermic hydrocarbon fuels (EHFs) have been widely used in scramjets for thermal protection and heat recirculation. The understanding of ignition characteristics of EHFs is of great importance for their safe and efficient utilization. In this paper, the ignition processes of EHFs were numerically simulated at atmospheric pressure and with an initial temperature of 500 K. Three different ignition stages were identified based on the chemical heat release and flame kernel propagation. A 3-component kerosene surrogate model composed of n-dodecane, methyl cyclohexane and m-xylene was adopted, as well as the corresponding chemical kinetic model with 369 species and 2691 reactions. Results showed that the discrepant decomposition characteristics of n-alkanes and cycloalkanes affected the chemical heat release and propagation during the ignition process. Two-stage exothermic characteristic was observed in the time evolutions of chemical heat release rate and fuel decomposition. The mass production of molecules and accumulation of radicals dominated the first and second exothermic peaks, respectively. Furthermore, the minimum ignition energies (MIEs) of EHFs with various methyl cyclohexane were determined to quantify the effect of fuel composition on ignition performance. Characteristically, the MIE dramatically decreased from 10.2 to 2.15 mJ when 20% n-dodecane was replaced by methyl cyclohexane. However, it was slightly increased as methyl cyclohexane continued to increase. Analyses from both physical and chemical aspects were conducted to elaborate the dependence of MIE on fuel composition. The dominant effects of flame-dynamic and chemical effects on different ignition stages were analysed. The faster propagation speed and stronger endothermic ability of methyl cyclohexane led to the nonlinear variation of MIEs. The results in this study provide useful guidance for composition optimization and safety evaluation of EHFs.

Statistical characterization of full-margin rupture recurrence for Cascadia subduction zone using event time resampling and Gaussian mixture model

AbstractEarthquake occurrence modeling of large subduction events involves significant uncertainty, stemming from the scarcity of geological data and inaccuracy of dating techniques. The previous research on statistical modeling of full-margin ruptures of the Cascadia subduction zone attempted to address these issues. However, the adopted resampling method to account for the uncertain marine turbidite age data from the Cascadia subduction zone was not sufficient in the sample size. This study presents a statistical approach based on the Gaussian mixture model applied to significantly larger resampled Cascadia age data. The results suggest that the 3-component Gaussian mixture model outperforms the 2-component Gaussian mixture model and the 1-component renewal models by capturing the long gap and short-term clustering. The developed Gaussian mixture model is well suited to apply to probabilistic seismic and tsunami hazard analysis and the calculation of long-term probability of the future full-margin Cascadia events by considering the elapsed time since the last event.

Luminescence for sedimentary provenance quantification in river basins: A methodological advancement

Multiple luminescence signals such as optically stimulated luminescence, infrared stimulated luminescence and thermoluminescence etc. are characteristic of the crystal/material. Recent studies have shown the potential of luminescence signals for sedimentary provenance studies, sediment fingerprinting and tracking. It is based on the premise that different provenances have different compositions of trace impurities in minerals which are responsible for the luminescence properties of the minerals. However, use of luminescence for provenance studies is still in its developmental stage and needs validation in modern sediments. The current work utilizes the differences in the luminescence signals from different geological provenances to quantitatively estimate the sediment influx to the mainstream from its tributaries based on a 2-component mixing model. Samples from natural river confluences and control samples prepared by mixing two samples in a known proportion by weight were used to estimate the sediment flux based on the luminescence characteristics. This study successfully demonstrates the use of luminescence parameters for sediment fingerprinting and budgeting using natural and controlled samples.

Leading medical laboratory professionals toward change readiness: a correlational study.

To remain effective in the dynamic health care landscape, the laboratory must embrace the continuous improvement mindset to support a culture of change, and leadership must facilitate the change process, mitigating perceived barriers of change readiness in followers. This quantitative study was designed to determine whether there is an association between leadership style (Multifactor Leadership Questionnaire [MLQ]) and change readiness (3-component model [TCM] commitment to change/Employee Commitment Survey, and whether leadership style predicts change readiness. Laboratory professionals (n = 718) were recruited through national societies to complete a combined MLQ-TCM survey instrument. Multivariate analysis of variance, Pearson correlations, and multiple regression analyses were performed. A significant correlation between leadership style and change readiness (transformational leadership [TL] and affective commitment to change, r(716) = .12, P = .002; passive-avoidant behavior and continuance commitment to change, r(716) = .25, P < .001) and between leadership style and leadership outcomes (TL and effectiveness, r(716) = .90, P < .001) was identified. Transformational leadership was a significant predictor of change readiness (β = .17, P < .05). It is recommended that laboratory leaders use transformational leadership or situational leadership to improve followers' affective commitment to change and reduce followers' continuance commitment to change, thus improving commitment to continuous improvement. Leaders should also limit passive-avoidant behavior.

Mass transfer characteristics of chiral pharmaceuticals on membrane used for polar organic chemical integrative sampler

Passive sampling technology has good application prospects for monitoring trace pollutants in aquatic environments. Further research on the sampling mechanism of this technology is essential to improve the measurement accuracy and extend the application scope of this approach. In this study, adsorption and permeation experiments were performed to investigate the sorption and mass transfer properties of five chiral pharmaceuticals at the enantiomeric level on polyethersulfone (PES) and polytetrafluoroethylene (PTFE) membranes used in a polar organic chemical integrative sampler. Batch adsorption experiments showed that the PES membrane had an adsorption phenomenon for most selected pollutants and an insignificant sorption behavior was observed for all selected pharmaceuticals on the PTFE membrane except for R(S)-fluoxetine. The diffusion coefficients of selected pharmaceuticals onto the PTFE membrane were approximately one order of magnitude higher than those onto the PES membrane. The permeation experiment indicated that under different hydraulic conditions, the change of the relative pollutant concentration through the PTFE membrane for the composite pollutant system was more obvious than that for the single pollutant system, and mass transfer hysteresis exists for both contaminant systems through PES membranes. Using the first-order equation or 3-component model to estimate the overall mass transfer coefficients, the results showed that the overall mass transfer coefficient values of pollutants in the composite pollutant system onto both membranes were higher than those in the single pollutant system. This parameter was mainly influenced by the synergistic effects of the multi-analyte interaction and diminished water boundary layers during the mass transfer process.

Use of Exploratory Factor Analysis to Assess the Fitness Performance of Youth Football Players.

Perroni, F, Castagna, C, Amatori, S, Gobbi, E, Vetrano, M, Visco, V, Guidetti, L, Baldari, C, Luigi Rocchi, MB, and Sisti, D. Use of exploratory factor analysis to assess the fitness performance of youth football players. J Strength Cond Res 37(7): e430-e437, 2023-Football performance involves several physical abilities that range in aerobic, anaerobic, and neuromuscular domains; however, little is known about their interplay in profiling individual physical attributes. This study aimed to profile physical performance in youth football players according to their training status. One hundred seven young male soccer players (age 13.5 ± 1.4 years; height 168 ± 7 cm; body mass 57.4 ± 9.6 kg; and body mass index 20.2 ± 2.1 kg·m -2 ) volunteered for this study. Players' physical performance was assessed with football-relevant field tests for sprinting (10 m sprint), vertical jump (countermovement jump), intermittent high-intensity endurance (Yo-Yo Intermittent Recovery Test Level 1, YYIRT1), and repeated sprint ability (RSA). The training status was assumed as testosterone and cortisol saliva concentrations; biological maturation was estimated using the Pubertal Development Scale. Exploratory factor analysis (EFA) revealed 3 main variables depicting anthropometric (D1, 24.9%), physical performance (D2, 18.8%), and training status (D3, 13.3%), accounting for 57.0% of total variance altogether. The level of significance was set at p ≤ 0.05. The RSA and YYIRT1 performances were largely associated with D2, suggesting the relevance of endurance in youth football. This study revealed that for youth football players, a 3-component model should be considered to evaluate youth soccer players. The EFA approach may help to disclose interindividual differences useful to talent identification and selection.

Pedalling Cadence Affects V̇ o2 Kinetics in Severe-Intensity Exercise.

Hill, DW and Vingren, JL. Pedalling cadence affects V̇ o2 kinetics in severe-intensity exercise. J Strength Cond Res 37(6): 1211-1217, 2023-The purpose was to investigate the effects of pedalling cadence on V̇ o2 kinetics in severe-intensity cycling exercise. This question is pertinent to exercise testing, where cadence is an important (and often confounding) variable, and to performance, where V̇ o2 kinetics determines the initial reliance upon anaerobic reserves. Eighteen university students performed tests to exhaustion at 241 ± 31 W, using cadences of 60, 80, and 100 rev·min -1 . V̇ o2 data were fitted to a 2-component model (primary phase + slow component). Responses during the 3 tests were compared using a repeated-measures analysis of variance, with significance at p < 0.05. The mean response time of the primary phase of the V̇ o2 response (time to reach 63% of the response) was progressively smaller (response was faster) at higher cadences (37 ± 4 seconds at 60 rev·min -1 , 32 ± 5 seconds at 80 rev·min -1 , 27 ± 4 seconds at 100 rev·min -1 ), and there was a concomitantly faster heart rate response. In addition, the time delay before the slow component was shorter, the amplitude of the primary phase was greater, and the amplitude of the slow component was smaller at the higher cadence. The results suggest that pedalling cadence itself-and not just the higher metabolic demand associated with higher cadences-may be responsible for differences in temporal characteristics (time delays, time constants) of the primary and slow phases of the V̇ o2 response. Exercise scientists must consider, and coaches might apply, the relationship between V̇ o2 kinetics and pedalling cadence during exercise testing.

Reliability of the 3-Component Model of Aerobic, Anaerobic Lactic, and Anaerobic Alactic Energy Distribution (PCr-LA-O2) for Energetic Profiling of Continuous and Intermittent Exercise.

To assess the test-retest reliability of the continuous (PCr-LA-O2) and intermittent (PCr-LA-O2int) version of the 3-component model of energy distribution in an applied setting. Sixteen male handball players (age 23 [3]y, height 185 [7]cm, weight 85 [14]kg) completed the 30-15 Intermittent Fitness Test (30-15IFT) twice. Performance was assessed by peak speed (speed of the last successfully completed stage of the 30-15IFT [VIFT], in kilometers per hour) and time to exhaustion (in seconds). Oxygen uptake (in milliliters per kilogram per minute) and blood lactate concentrations (in millimoles per liter) were obtained before, during, and until 15minutes after exercise. Total metabolic energy (in joules per kilogram), total metabolic power (in watts per kilogram), and energy shares (in joules per kilogram and percentage) of the aerobic (energy contribution of the aerobic system [WAERint]), anaerobic lactic, and anaerobic alactic (anaerobic alactic energy [WPCrint]) systems were calculated using both model versions, respectively. Test-retest reliability was very good for VIFT (limits of agreement [LoA]: -1.13 to 0.63km·h-1, coefficient of variation [CV%] 1.68), time to exhaustion (LoA: -101 to 38s, CV% 2.92), peak oxygen uptake (LoA: -2.68 to 4.04mL·min-1·kg-1, CV% 1.48), and peak heart rate (-6.9 to 7.7 beats·min-1, CV% 1.1), but moderate for change in blood lactate concentration (LoA: -3.84 to 4.07mmol·L-1, CV% 11.43). Reliability of the modeled total energy and its fractions were high for total metabolic energy (LoA: -1489 to 1177J·kg-1, CV% 2.88), total metabolic power (LoA: -2.0 to 1.9W·kg-1, CV% 3.58), contribution of aerobic (LoA: -1673 to 1283J·kg-1, CV% 3.62), WAERint (LoA: -1760 to 2160J·kg-1, CV% 6.04), and moderate for anaerobic alactic (LoA: -368 to 439J·kg-1, CV% 14.85), WPCrint (LoA: -1707 to 988J·kg-1, CV% 9.98), and energy share of anaerobic lactic concentration (LoA: -229 to 235J·kg-1, CV% 11.43). Considering the inherent fluctuations of the underlying energetics, the reliabilities of both versions of the 3-component model of energy distribution are acceptable for applied settings.

Tumor Growth Rate as a Predictive Marker for Recurrence and Survival After Liver Resection in Patients with Liver Metastases of Uveal Melanoma.

Surgical management of liver metastases of uveal melanoma (LMUM) is associated with the best survival rates, especially for patients with a low tumor burden in the liver. The aim was to determine whether the tumor growth rate (TGR0) before liver resection helps predict survival in patients with resectable LMUM. This retrospective study included 99 patients with LMUM treated with liver resection between November 2007 and November 2020. TGR0 was expressed as the percentage change in tumor volume over 1 month according to two pretreatment imaging scans. Multivariate Cox analyses identified independent predictors of disease-free survival (DFS) and overall survival (OS). DFS and OS had a statistically significant positive linear relationship (Spearman correlation r = 0.68, p < 0.001). A disease-free interval (DFI) > 24 months and a TGR0 ≤ 50%/month were independent factors associated with better DFS and OS. The 2-component model including TGR0 and DFI had a mean time-dependent area under the curve (AUC) of 0.81 (95% CI, 0.75-0.86) and 0.77 (95% CI, 0.67-0.87), respectively, for predicting DFS and OS. DFI with TGR0 defined three kinetic risk groups that had distinct DFS and OS outcomes (p < 0.001). Cytogenetic alterations at baseline were partially predictive factors of the kinetic risk score based on TGR0 and DFI. The assessment of TGR0 improves prognostic stratification by identifying patients at high risk of recurrence and poor survival after liver resection. TGR0 and DFI, reflecting tumor aggressivity, have the potential to be important markers for systemic adjuvant decisions.

Time to revisit the passive overconsumption hypothesis? Humans show sensitivity to calories in energy-rich meals

ABSTRACTBackgroundA possible driver of obesity is insensitivity (passive overconsumption) to food energy density (ED, kcal/g); however, it is unclear whether this insensitivity applies to all meals.ObjectivesWe assessed the influence of ED on energy intake (kcal) across a broad and continuous range of EDs comprised of noncovertly manipulated, real-world meals. We also allowed for the possibility that the association between energy intake and ED is nonlinear.MethodsWe completed a secondary analysis of 1519 meals which occurred in a controlled environment as part of a study conducted by Hall and colleagues to assess the effects of food ultra-processing on energy intake. To establish the generalizability of the findings, the analyses were repeated in 32,162 meals collected from free-living humans using data from the UK National Diet and Nutrition Survey (NDNS). Segmented regressions were performed to establish ED “breakpoints” at which the association between consumed meal ED and mean centered meal caloric intake (kcal) changed.ResultsSignificant breakpoints were found in both the Hall et al. data set (1.41 kcal/g) and the NDNS data set (1.75 and 2.94 kcal/g). Centered meal caloric intake did not increase linearly with consumed meal ED, and this pattern was captured by a 2-component (“volume” and “calorie content” [biologically derived from the sensing of fat, carbohydrate, and protein]) model of physical meal size (g), in which volume is the dominant signal with lower energy-dense foods and calorie content is the dominant signal with higher energy-dense foods.ConclusionsThese analyses reveal that, on some level, humans are sensitive to the energy content of meals and adjust meal size to minimize the acute aversive effects of overconsumption. Future research should consider the relative importance of volume and calorie-content signals, and how individual differences impact everyday dietary behavior and energy balance.

Equivalent Circuit Approximation to the Connector-Line Transition at High Frequencies using Two Microstrip Lines and Data Fitting

This article presents a method of obtaining an equivalent lumped element circuit to model the electrical connector-line transitions in the ultra-high frequency (UHF) band. First, the scattering matrices of two microstrip transmission lines that are otherwise identical but have the physical lengths of dd and 2d2⁢d are measured. Next, the theoretical model of the lines cascaded with the connector-line transitions modeled as lumped element circuits is established. The selection of the line lengths to be dd and 2d2⁢d results in an over determined system of equations that links the circuit component values to the two-port network parameters of the cascaded system. Finally, the least-squares data fitting procedure yields the best-fit component values. The results show that in our tested scenario, 3-component reactive circuit models well the transitions. Compared with the previous methods, the proposed approach does not require knowledge of the dielectric properties of the substrate of the measured transmission lines. This property integrates the method with our previous work on estimating a microstrip line substrate’s relative permittivity and loss tangent. The obtained transition circuit model is also validated through the testing of two quarter-wave transformers. The lines and transformers are implemented on a textile substrate to highlight the method’s applicability to wearable textile-based electronics.

Validation of MINORMIX Approach for Estimation of Low Birthweight Prevalence Using a Rural Nepal Dataset

BackgroundThe Global Nutrition Target of reducing low birthweight (LBW) by ≥30% between 2012 and 2025 has led to renewed interest in producing accurate, population-based, national LBW estimates. Low- and middle-income countries rely on household surveys for birthweight data. These data are frequently incomplete and exhibit strong “heaping.” Standard survey adjustment methods produce estimates with residual bias. The global database used to report against the LBW Global Nutrition Target adjusts survey data using a new MINORMIX (multiple imputation followed by normal mixture) approach: 1) multiple imputation to address missing birthweights, followed by 2) use of a 2-component normal mixture model to account for heaping of birthweights. ObjectivesTo evaluate the performance of the MINORMIX birthweight adjustment approach and alternative methods against gold-standard measured birthweights in rural Nepal. MethodsAs part of a community-randomized trial in rural Nepal, we measured “gold-standard” birthweights at birth and returned 1–24 mo later to collect maternally reported birthweights using standard survey methods. We compared estimates of LBW from maternally reported data derived using: 1) the new MINORMAX approach; 2) the previously used Blanc–Wardlaw adjustment; or 3) no adjustment for missingness or heaping against our gold standard. We also assessed the independent contribution of multiple imputation and curve fitting to LBW adjustment. ResultsOur gold standard found 27.7% of newborns were LBW. The unadjusted LBW estimate based on maternal report with simulated missing birthweights was 14.5% (95% CI: 11.6, 18.0%). Application of the Blanc–Wardlaw adjustment increased the LBW estimate to 20.6%. The MINORMIX approach produced an estimate of 26.4% (95% CI: 23.5, 29.3%) LBW, closest to and with bounds encompassing the measured point estimate. ConclusionsIn a rural Nepal validation dataset, the MINORMIX method generated a more accurate LBW estimate than the previously applied adjustment method. This supports the use of the MINORMIX method to produce estimates for tracking the LBW Global Nutrition Target.

Effect of circadian system disruption on the concentration and daily oscillations of cortisol, progesterone, melatonin, serotonin, growth hormone, and core body temperature in periparturient dairy cattle

Metabolic, circadian, sleep, and reproductive systems are integrated and reciprocally regulated, but the understanding of the mechanism is limited. To study this integrated regulation, the circadian timing system was disrupted by exposing late pregnant nonlactating (dry) cows to chronic shifts in the light-dark phase, and rhythms of body temperature and circulating cortisol (CORT), progesterone (P4), serotonin (5HT), melatonin (MEL), and growth hormone (GH) concentrations were measured. Specifically, across 2 identical studies (1 and 2), at 35 d before expected calving (BEC) multiparous cows were assigned to control (CON; n = 24) and exposed to 16 h light and 8 h dark or phase shift (PS; n = 24) treatments and exposed to 6-h light-dark phase shifts every 3 d until parturition. All cows were exposed to control lighting after calving. Blood samples were collected in the first study at 0600 h on d 35 BEC, d 21 BEC, and 2 d before calving, and d 0, 2, 9, 15, and 22 postpartum (PP). A subset of cows (n = 6/group) in study 1 was blood sampled every 4 h over 48 h beginning on d 23 BEC, 9 BEC, and 5 PP. Body temperature was measured every 30 min (n = 8-16/treatment) for 48 h at 23 BEC and 9 BEC in both studies; and at 14 PP and 60 PP only in study 2. Treatment did not affect levels of CORT, GH, or P4 at 0600 h, but overall level of 5HT was lower and MEL higher in PS cows across days sampled. A 2-component versus single-component cosinor model better described [>coefficient of determination (R2); <Akaike information criterion and <Bayesian information criterion] daily oscillations of all hormones and temperature for both treatments. Circadian rhythm fit (R2) of body temperature and MEL increased from 23 BEC to 9 BEC in CON and was marked by loss of feeding time influence on oscillations in both treatments. Both treatments exhibited circadian rhythms of CORT at 9 BEC, CON cows also exhibited circadian rhythms in P4 at 23 BEC, and 5HT at 9 BEC. Daily oscillations in temperature and hormones, except CORT, were affected by PS treatment in the prepartum and were associated with longer gestation. In the PP, circadian rhythmicity was lost or diminished for all hormones and body temperature in both treatments. Stronger rhythms of body temperature and multiple hormones at 1 wk prepartum may indicate a synchronizing cue to time parturition. Therefore, dairy systems may need to consider management factors that affect circadian clocks in late-gestation cows.

Characterization of the diffusion signal of breast tissues using multi-exponential models.

Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues. The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging. A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI. The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were D1,3 = 0, D2,3 = 1.5 × 10-3 , and D3,3 = 10.8 × 10-3 mm2 /s. The RSI-derived signal contributions of the slower diffusion components were larger in tumors than in fibroglandular tissues. Further, the contrast-to-noise and specificity at 80% sensitivity of DCE and a subset of RSI-derived maps were equivalent. Breast diffusion-weighted MRI signal was best described using a triexponential model. Tumor conspicuity in breast RSI model is comparable to that of DCE without the use of exogenous contrast. These data may be used as differential features between healthy and malignant breast tissues.

Model-Predicted Clinical Factors Impacting Patient-Specific Response of Sickle Cells to Voxelotor in a Microfluidic Platform

Background: Sickle cell disease (SCD) is a group of genetic disorders in which sickle hemoglobin polymerizes under deoxygenation, altering red blood cell (RBC) morphology and behavior. The properties of sickle RBCs contribute to increased viscosity of blood and occurrence of vaso-occlusions, a major aspect of SCD pathophysiology. Voxelotor is a novel FDA-approved treatment for SCD which modulates hemoglobin O 2 affinity, and while its known mechanism inhibits sickle polymerization, the impact on other aspects of SCD pathophysiology remain unknown. Thus, despite the new treatment option, highly variable clinical manifestation continues to be a hallmark of sickle cell and there is consequently a need to optimize the use of current therapies based on patient-specific factors. In this work, we leverage datasets generated from a unique microfluidic assay that measures blood flow behavior under varying oxygen tension in conjunction with novel statistical approaches to model and assess sources of variability in sickle blood flow response to voxelotor.Methods: RBCs from patients with SCD (n=28) were treated with voxelotor at 500 uM concentration. Treated samples and untreated controls were perfused through a microfluidic platform that dynamically modulates oxygen tension and measures flow velocity (Wood et al, 2012; Valdez et al, 2019). The area between curves (ABC) of the normalized velocity across the range of oxygen tension between treated and control conditions was calculated to quantify the effect of voxelotor for each sample (figure 1). A paired t-test was used to assess the difference in response between treated and untreated samples. Where available, clinical data including the hemoglobin fractions and complete blood count (CBC) were collected for each sample as predictor variables, and partial least squares regression (PLSR) modeling was used to assess the correlation of predictors and responses.Results: Voxelotor increased the velocity ABC from untreated to treated conditions (p<.0001). We observed that there were differences in response for velocity ABC between sickle cell genotypes (figure 2). Thus, generating separate PLSR models for distinct SCD genotypes revealed differences in sets of clinical factors that explained the most variance in response to voxelotor treatment. A 2-component model was constructed for the HbSC samples (n=6) that best explained variance in the data and had good predictive abilities (R 2X=.69, R 2Y=.97, Q 2=.74). Within this subset, clustering of variables related to hemolysis and inflammation were observed (figure 3). An equivalent model constructed for the HbSS samples (n=15) characterized the predictor variables but lacked predictive power of the response (R 2X=.74, R 2Y=.25, Q 2=.-0.21). Response to voxelotor for this model was most strongly correlated with HbA. Due to low sample size (n=2 samples with full set of predictors), predictive modeling was not performed for HbSβ 0 samples, however, these samples responded the least to voxelotor treatment.Conclusions: Our analysis quantified patient-specific differences in the blood flow response to voxelotor, showing a wide variability in response despite treatment by the same drug concentration. Genotype-specific multivariable models that take into account easily measurable clinical variables such as the CBC have the potential to explain the variability in patient response to voxelotor treatment. In HbSC samples, the WBC, platelet, and reticulocyte counts were highly correlated and strong predictors of response to voxelotor, which may point to markers of hemolysis and inflammation being useful in determining patients that can be optimally treated with this drug. In HbSS, response to voxelotor was mainly inversely correlated with HbA levels, which is a surrogate marker for blood transfusions, indicating that the effect of voxelotor is lessened for patients who are receiving transfusions. However, the low R2Y of this model highlights the clinical variability in this SCD genotype and consequent need for additional biomarkers of disease severity. In conclusion, our hybrid experimental-computational approach is able to identify clinical factors that highly impact the response of patient blood samples to treatment with voxelotor for HbSC patients, and highlights the need for precision therapy recommendations in SCD. [Display omitted] DisclosuresLam: Sanguina, Inc.: Current holder of individual stocks in a privately-held company. Kemp: Parthenon Therapeutics: Membership on an entity's Board of Directors or advisory committees.

Agreement Between A 2-Dimensional Digital Image-Based 3-Compartment Body Composition Model and Dual Energy X-Ray Absorptiometry for The Estimation of Relative Adiposity

The purpose of this study was to compare relative adiposity (%Fat) derived from a 2-dimensional image-based 3-component (3C) model (%Fat3C-IMAGE) and dual-energy X-ray absorptiometry (DXA) (%FatDXA) against a 5-component (5C) laboratory criterion (%Fat5C). 57 participants were included (63.2% male, 84.2% White/Caucasian, 22.5±4.7 yrs., 23.9±2.8 kg/m2). For each participant, body mass and standing height were measured to the nearest 0.1 kg and 0.1 cm, respectively. A digital image of each participant was taken using a 9.7 inch, 16g iPad Air 2 and analyzed using a commercially available application (version 1.1.2, made Health and Fitness, USA) for the estimation of body volume (BV) and inclusion in %Fat3C-IMAGE . %Fat3C-IMAGE and %Fat5C included measures of total body water derived from bioimpedance spectroscopy. The criterion %Fat5C included BV estimates derived from underwater weighing and bone mineral content measures via DXA. %FatDXA estimates were calculated from a whole-body DXA scan. A standardized mean effect size (ES) assessed the magnitude of differences between models with values of 0.2, 0.5, and 0.8 for small, moderate, and large differences, respectively. Data are presented as mean ± standard deviation. A strong correlation (r = 0.94, p <.001) and small mean difference (ES = 0.24, p <.001) was observed between %Fat3C-IMAGE (19.20±5.80) and %Fat5C (17.69±6.20) whereas a strong correlation (r = 0.87, p <.001) and moderate-large mean difference (ES = 0.70, p <.001) was observed between %FatDXA (22.01±6.81) and %Fat5C. Furthermore, %Fat3C-IMAGE (SEE = 2.20 %Fat, TE= 2.6) exhibited smaller SEE and TE than %FatDXA (SEE = 3.14 %Fat, TE = 5.5). The 3C image-based model performed slightly better in our sample of young adults than the DXA 3C model. Thus, the 2D image analysis program provides an accurate and non-invasive estimate of %Fat within a 3C model in young adults. Compared to DXA, the 3C image-based model allows for a more cost-effective and portable method of body composition assessment, potentially increasing accessibility to multi-component methods.

Validation of the estimation of the macrovascular contribution in multi-timepoint arterial spin labeling MRI using a 2-component kinetic model.

In this paper, the ability to quantify cerebral blood flow by arterial spin labeling (ASL) was studied by investigating the separation of the macrovascular and tissue component using a 2-component model. Underlying assumptions of this model, especially the inclusion of dispersion in the analysis, were studied, as well as the temporal resolution of the ASL datasets. Four different datasets were acquired: (1) 4D ASL angiography to characterize the macrovascular component and to study dispersion modeling within this component, (2) high temporal resolution ASL data to investigate the separation of the 2 components and the effect of dispersion modelling on this separation, (3) low temporal resolution ASL dataset to study the effect of the temporal resolution on the separation of the 2 components, and (4) low temporal resolution ASL data with vascular crushing. The model that included a gamma dispersion kernel had the best fit to the 4D ASL angiography. For the high temporal resolution ASL dataset, inclusion of the gamma dispersion kernel led to more signal included in the arterial blood volume map, which resulted in decreased cerebral blood flow values. The arterial blood volume and cerebral blood flow maps showed overall higher arterial blood volume values and lower cerebral blood flow values for the high temporal resolution dataset compared to the low temporal resolution dataset. Inclusion of a gamma dispersion kernel resulted in better fitting of the model to the data. The separation of the macrovascular and tissue component is affected by the inclusion of a gamma dispersion kernel and the temporal resolution of the ASL dataset.

A Cross-Cultural Investigation of the Five-Factor Narcissism Inventory Short Form: Narcissism as a Multidimensional Trait in the United Kingdom and Russia

Research on narcissism supports a multifactorial structure with each factor to be multidimensional. However, studies typically assess 1 factor of narcissism using scales such as the Short Dark Triad (SD3). The Five-Factor Narcissism Inventory and its short form (FFNI–SF) were developed recently to account for this issue. The extent to which the FFNI–SF assesses equivalent constructs across countries has, nonetheless, received little attention. This study evaluated the factor structure of the FFNI–SF in a Russian sample (N = 1,996) and compared the results with those derived from a UK sample (N = 1,292). Overall, the FFNI–SF evidenced satisfactory internal consistency (mean α = .82 and .79 for the Russian and UK sample for the factors of Antagonism, Extraversion and Neuroticism, respectively). Additionally, analyses yielded a 3-component model in both samples similar to that reported in the original validation study, which also demonstrated meaningful correlations with the SD3. Finally, Antagonism demonstrated significant differences between the 2 samples, perhaps reflecting societal differences between Russia and the United Kingdom. The study highlights the importance of using psychometrically sound measures to assess narcissism in its complexity and the need for additional research on how this trait operates within the personality sphere and across cultures.

Environmentally triggered shifts in steelhead migration behavior and consequences for survival in the mid-Columbia River.

The majority of Columbia River summer-run steelhead encounter high river temperatures (near or > 20°C) during their spawning migration. While some steelhead pass through the mid-Columbia River in a matter of days, others use tributary habitats as temperature refuges for periods that can last months. Using PIT tag detection data from adult return years 2004-2016, we fit 3-component mixture models to differentiate between "fast", "slow", and "overwintering" migration behaviors in five aggregated population groups. Fast fish migrated straight through the reach on average in ~7-9 days while slow fish delayed their migration for weeks to months, and overwintering fish generally took ~150-250 days. We then fit covariate models to examine what factors contributed to the probability of migration delay during summer months (slow or overwintering behaviors), and to explore how migration delay related to mortality. Finally, to account for the impact of extended residence times in the reach for fish that delayed, we compared patterns in estimated average daily rates of mortality between migration behaviors and across population groups. Results suggest that migration delay was primarily triggered by high river temperatures but temperature thresholds for delay were lowest just before the seasonal peak in river temperatures. While all populations groups demonstrated these general patterns, we documented substantial variability in temperature thresholds and length of average delays across population groups. Although migration delay was related to higher reach mortality, it was also related to lower average daily mortality rates due to the proportional increase in reach passage duration being larger than the associated increase in mortality. Lower daily mortality rates suggest that migration delay could help mitigate the impacts of harsh migration conditions, presumably through the use of thermal refuges, despite prolonged exposure to local fisheries. Future studies tracking individual populations from their migration through reproduction could help illuminate the full extent of the tradeoffs between different migration behaviors.