Large Variations Research Articles

An examination of the criterion-related validity of varying methods of indexing clinically significant change in posttraumatic stress disorder treatment.

Clinicians, patients, and researchers need benchmarks to index individual-level clinically significant change (CSC) to guide decision making and inferences about treatment efficacy. Yet, there is no consensus best practice for determining CSC for posttraumatic stress disorder (PTSD) treatments. We examined criterion-related validity of the most common approach-Jacobson and Truax's (J&T; 1991) procedures for indexing CSC. We generated and compared four methods of calculating the J&T indices of CSC (two sets of sample-specific inputs, putatively norm-referenced benchmarks, and a combination of sample-specific and norm-referenced criteria) with respect to their association with a criterion index of quality of life (QoL). Participants were 91 women Veterans enrolled in a randomized clinical trial for PTSD who completed self-report measures on PTSD symptoms and various domains of QoL and functioning, pre- and posttreatment. For each of the four methods used to calculate CSC, the QoL composite was regressed onto the CSC categories. All methods explained large variance in change in QoL. Across all methods, participants categorized as unchanged had smaller changes in QoL, compared with those who improved or had probable recovery. The norm-referenced benchmarks accounted for the relatively largest amount of variance in QoL, but categorized the fewest patients as having made CSC. The J&T methodology for indexing CSC in PTSD symptoms has criterion-related validity, and a norm-referenced benchmark appears to be the most potent. However, the norm-referenced parameters may be overly specific, potentially leading to an underestimate of improvement. Research is needed to test the generalizability of these results. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Measurement and optimisation of the perceptual equivalence of the Dutch consonant-vowel-consonant (CVC) word lists using synthetic speech and list pairs

Objectives (1) to determine whether the standard Dutch word lists for speech audiometry are equally intelligible in normal-hearing listeners (Experiment 1), (2) to investigate whether synthetic speech can be used to create word lists (Experiment 1) and (3) to determine whether the list effect found in Experiment 1 can be reduced by combining two lists into pairs (Experiment 2). Design Participants performed speech tests in quiet with the original (natural) and synthetic word lists (Experiment 1.). In Experiment 2, new participants performed speech tests with list pairs from the original lists constructed from the results of Experiment 1. Study samples Twenty-four and twenty-eight normal-hearing adults. Results There was a significant list effect in the natural speech lists; not in the synthetic speech lists. Variability in intelligibility was significantly higher in the former, with list differences up to 20% at fixed presentation levels. The 95% confidence interval of a list with a score of approximately 70% is around 10%-points wider than of a list pair. Conclusions The original Dutch word lists show large variations in intelligibility. List effects can be reduced by combining two lists per condition. Synthetic speech is a promising alternative to natural speech in speech audiometry in quiet.

Penalized Regression Methods With Modified Cross-Validation and Bootstrap Tuning Produce Better Prediction Models.

Risk prediction models fitted using maximum likelihood estimation (MLE) are often overfitted resulting in predictions that are too extreme and a calibration slope (CS) less than 1. Penalized methods, such as Ridge and Lasso, have been suggested as a solution to this problem as they tend to shrink regression coefficients toward zero, resulting in predictions closer to the average. The amount of shrinkage is regulated by a tuning parameter, commonly selected via cross-validation ("standard tuning"). Though penalized methods have been found to improve calibration on average, they often over-shrink and exhibit large variability in the selected and hence the CS. This is a problem, particularly for small sample sizes, but also when using sample sizes recommended to control overfitting. We consider whether these problems are partly due to selecting using cross-validation with "training" datasets of reduced size compared to the original development sample, resulting in an over-estimation of and, hence, excessive shrinkage. We propose a modified cross-validation tuning method ("modified tuning"), which estimates from a pseudo-development dataset obtained via bootstrapping from the original dataset, albeit of larger size, such that the resulting cross-validation training datasets are of the same size as the original dataset. Modified tuning can be easily implemented in standard software and is closely related to bootstrap selection of the tuning parameter ("bootstrap tuning"). We evaluated modified and bootstrap tuning for Ridge and Lasso in simulated and real data using recommended sample sizes, and sizes slightly lower and higher. They substantially improved the selection of , resulting in improved CS compared to the standard tuning method. They also improved predictions compared to MLE.

Why are some individuals better at using negative attentional templates to suppress distractors? Exploration of interindividual differences in cognitive control efficiency.

Negative templates are based on foreknowledge of distractor features and can lead to more efficient visual search at the group level. However, large individual differences exist in the size of benefits induced by negative cues. The cognitive factors underlying these interindividual differences remain unknown. Previous research has suggested higher engagement of proactive control for negative templates compared to positive templates. We thus hypothesized that interindividual differences in proactive control efficiency may explain the large variability in negative cue benefits. A large data set made up of data from two previously published studies was reanalyzed (N = 139), with eye movements recorded in 36 participants. Individual proactive control efficiency was measured through reaction time (RT) variability. Participants with higher proactive control efficiency exhibited larger benefits after negative cues across two critical measures: Individuals with higher proactive control showed larger RT benefits following negative compared to neutral cues; similarly, individuals with higher proactive control exhibited lower first saccades to cued distractor items. No such relationship was observed for positive cues. Our results confirmed the existence of large interindividual differences in the benefits induced by negative attentional templates. Critically, we show that proactive control drives these interindividual differences in negative template use. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

From brain to education through machine learning: Predicting literacy and numeracy skills from neuroimaging data

Abstract The potential of using neural data to predict academic outcomes has always been at the heart of educational neuroscience, an emerging field at the crossroad of psychology, neuroscience and education sciences. Although this prospect has long been elusive, the exponential use of advanced techniques in machine learning in neuroimaging may change this state of affairs. Here we provide a review of neuroimaging studies that have used machine learning to predict literacy and numeracy outcomes in adults and children, both in the context of learning disability and typical performance. We notably review the cross-sectional and longitudinal designs used in such studies, and describe how they can be coupled with regression and classification approaches. Our review highlights the promise of these methods for predicting literacy and numeracy outcomes, as well as their difficulties. However, we also found a large variability in terms of algorithms and underlying brain circuits across studies, and a relative lack of studies investigating longitudinal prediction of outcomes in young children before the onset of formal education. We argue that the field needs a standardization of methods, as well as a greater use of accessible and portable neuroimaging methods that have more applicability potential than lab-based neuroimaging techniques.

Social determinants of health but not global genetic ancestry predict dementia prevalence in Latin America.

Leveraging the nonmonolithic structure of Latin America, which represents a large variability in social determinants of health (SDoH) and high levels of genetic admixture, we aim to evaluate the relative contributions of SDoH and genetic ancestry in predicting dementia prevalence in Latin American populations. Community-dwelling participants aged 65 and older (N=3808) from Cuba, Dominican Republic, Mexico, and Peru completed the 10/66 protocol assessments. Dementia was diagnosed using the cross-culturally validated 10/66 algorithm. Multivariate linear regression models adjusted for SDoH were used in the main analysis. This study used cross-sectional data from the 1066 population-based study. Individuals with higher proportions of Native American (>70%) and African American (>70%) ancestry were more likely to exhibit factors contributing to worse SDoH, such as lower educational levels (p<0.001), lower socioeconomic status (p<0.001), and higher frequency of vascular risk factors (p<0.001). After adjusting for measures of SDoH, there was no association between ancestry proportion and dementia probability, and ancestry proportions no longer significantly accounted for the variance in cognitive performance (African predominant p=0.31 [-0.19, 0.59] and Native predominant p=0.74 [-0.24, 0.33]). The findings suggest that social and environmental factors play a more crucial role than genetic ancestry in predicting dementia prevalence in Latin American populations. This underscores the need for public health strategies and policies that address these social determinants to effectively reduce dementia risk in these communities. Countries in Latin America express a large variability in social determinants of health and levels of admixture. After adjustment for downstream societal factors linked to SDoH, genetic ancestry shows no link to dementia. Population ancestry profiles alone do not influence cognitive performance. SDoH are key drivers of racial disparities in dementia and cognitive performance.

An Evaluation of Research Performance: A Survey Study of Undergraduate Media and Communication Students

Assessing research performance is essential for adopting enhancement procedures. The emphasis of the study was on undergraduate media students. The objectives of this study were to estimate their research abilities, evaluate the efficiency of some studies conducted, and identify areas for improvement. A survey was employed to collect data about the results of the final examination of the research methods subject. The study gathered 100 scores, representing three batches of male and female students. The study used the independent sample t-test to compare the means for the two groups of males and females and then explain the differences and effect size. The results showed that female students have much higher research method scores (79.3) than male students (74.9). The standard deviation for male research methods scores (10.9) is higher than that for male students (9.8), indicating higher variability in female scores. The standard error of the mean estimates the precision of the sample mean. It is higher for male students (1.5) than for female students (1.4). Hence, male students needed more accuracy in estimating the population mean due to the large variability in their scores. The effect size was (d = 0.41). There were differences (t (df = 98, p =.035) in the scores, with the mean score for the females (M = 79.3, SD = 9.8) being higher than the males (M = 74.9, SD = 10.9). In conclusion, testing students' research skills revealed a slight difference between male and female media B.Sc. students.

BoNuS: Boundary Mining for Nuclei Segmentation With Partial Point Labels.

Nuclei segmentation is a fundamental prerequisite in the digital pathology workflow. The development of automated methods for nuclei segmentation enables quantitative analysis of the wide existence and large variances in nuclei morphometry in histopathology images. However, manual annotation of tens of thousands of nuclei is tedious and time-consuming, which requires significant amount of human effort and domain-specific expertise. To alleviate this problem, in this paper, we propose a weakly-supervised nuclei segmentation method that only requires partial point labels of nuclei. Specifically, we propose a novel boundary mining framework for nuclei segmentation, named BoNuS, which simultaneously learns nuclei interior and boundary information from the point labels. To achieve this goal, we propose a novel boundary mining loss, which guides the model to learn the boundary information by exploring the pairwise pixel affinity in a multiple-instance learning manner. Then, we consider a more challenging problem, i.e., partial point label, where we propose a nuclei detection module with curriculum learning to detect the missing nuclei with prior morphological knowledge. The proposed method is validated on three public datasets, MoNuSeg, CPM, and CoNIC datasets. Experimental results demonstrate the superior performance of our method to the state-of-the-art weakly-supervised nuclei segmentation methods. Code: https://github.com/hust-linyi/bonus.

Comparison of Blood Volume Profiles in Heart Failure With Preserved and Reduced Ejection Fractions: Sex Makes a Difference.

Blood volume (BV) profiles vary markedly in patients with heart failure (HF), but how HF phenotypes and patient sex impact volume profiles remain to be explored. The aim of the study was to differentiate BV, plasma volume, and red blood cell mass profiles by phenotypes of preserved and reduced left ventricular ejection fractions and assess the impact of patient sex on profile heterogeneity. Retrospective analysis of clinical and BV data was undertaken in patients with chronic New York Heart Association II-III heart failure. BV was quantitated using the nuclear medicine indicator-dilution methodology. A total of 530 BV analyses (360 HF with reduced ejection fraction and 170 HF with preserved ejection fraction) were identified in 395 unique patients. Absolute BV was greater in HF with reduced ejection fraction (6.7±1.8 versus 5.9±1.6 liters: P<0.001); however, large variability in frequency distribution of volume profiles was observed in both phenotypes (-22% deficit to +109% excess relative to normal volumes). HF with reduced ejection fraction was characterized by a higher prevalence of BV expansion ≥+25% of normal (39% versus 26%; P=0.003), and HF with preserved ejection fraction was characterized a by more frequent normal BV (42% versus 24%; P<0.001). Male sex in both phenotypes was associated with a larger absolute BV (7.0±1.6 versus 5.1±1.3 liters; P<0.001) and higher frequency of large BV and plasma volume expansions above normal (both P<0.001), while females in both phenotypes demonstrated a higher prevalence of normal BV and plasma volume (both P<0.001). Findings support significant differences in BV, plasma volume, and red blood cell mass profile distributions between heart failure phenotypes, driven in large part by sex-specific factors. This underscores the importance of identifying and distinguishing individual patient volume profiles to help guide volume management strategies.

Are there sex differences in the variability of fasting metabolism?

There is evidence across species and across many traits that males display greater between-individual variance. In contrast, (premenopausal) females display large within-individual variance in sex hormone concentrations, which can increase within-individual variance in many other parameters. The latter may contribute to the lower representation of females in metabolic research. This study is a pooled secondary analysis of data from 7 crossover studies to investigate the between-individual and the within-individual variance in fasting plasma metabolites, resting metabolic rate (RMR) and body mass. Females demonstrated higher within-individual variability of plasma 17β-estradiol (CV 15±15 % for males vs 38±34 % for females, p<0.001) and progesterone concentrations (CV 13±11 % for males vs 52±51 % for females, p<0.001) but there were no meaningful differences in the variability of plasma glucose (CV 4±3 % for males vs 5 ± 5 % for females), insulin, lactate, triglycerides (CV 15±9 % for males vs 15 ± 10 % for females), and non-esterified fatty acid concentrations, nor in RMR and body mass (CV 0.43±0.34 % for males vs for 0.42±0.33 % females; p>0.05 for all outcomes). Males displayed higher between-individual variance in RMR compared to females (SD 224 kcal×day-1 for males vs 151 kcal×day-1 for females). In conclusion, these data do not provide evidence that females show greater within-individual variability in many fasting metabolic variables, RMR or body mass compared to males. We conclude that including females in metabolic research is unlikely to introduce greater within-individual variance when using the recruitment and control procedures described in these studies.

Energy Partition between Submesoscale Internal Waves and Quasigeostrophic Vortical Motion in the Pycnocline

Abstract Shipboard ADCP velocity and towed CTD chain density measurements from the eastern North Pacific pycnocline are used to segregate energy between linear internal waves (IW) and linear vortical motion [quasigeostrophy (QG)] in 2D wavenumber space spanning submesoscale horizontal wavelengths λx ∼ 1–50 km and finescale vertical wavelengths λz ∼ 7–100 m. Helmholtz decomposition and a new Burger number (Bu) decomposition yield similar results despite different methodologies. While these wavelengths are conventionally attributed to internal waves, both QG and IW contribute significantly at all measured scales. Partition between IW and QG total energies depends on Bu. For Bu &lt; 0.01, available potential energy EP exceeds horizontal kinetic energy EK and is contributed mostly by QG. In contrast, energy is nearly equipartitioned between QG and IW for Bu ≫ 1. For Bu &lt; 2, EK is contributed mainly by IW, and EP by QG, while, for Bu &gt; 2, contributions are reversed. Finescale near-inertial IW dominate vertical shear variance, implying negligible QG contribution to vertical shear instability. In contrast, both QG and IW at the smallest λx ∼ 1 km contribute large horizontal shear variance, so that both may lead to horizontal shear instability, while QG, with its longer time scales, likely dominates isopycnal stirring. Both QG and IW contribute to vortex stretching at small vertical scales. For QG, the relative vorticity contribution to linear potential vorticity anomaly increases with decreasing horizontal and increasing vertical scales.

Improving residential wood burning emission inventories with the integration of readily available data sources

Residential wood burning (RWB) is the largest anthropogenic source of PM2.5 in many North American and European cities in the winter. The current lack of information on the locations, types, and intensity of use of wood burning appliances limits the ability to accurately assess the exposure of the population to RWB emissions. In this study, we generated a high spatial resolution emission inventory for RWB in the province of Quebec, Canada using a novel data driven approach. The method first combines real estate and socioeconomic census data through machine learning models to estimate ownership rates of fireplaces and wood stoves. These ownership rates are then combined with household survey data (on wood consumption and types of appliances), emission factors and building registry data to generate the emission inventory at a 1Km2 resolution. Our proposed approach was able to capture spatial patterns in RWB appliance ownership and intensity of use, which may be overlooked by using simple urban vs rural or population based spatial proxies. The machine learning models explained 80.3 % and 63 % of the variability in wood stove and fireplace ownership rates with each appliance type exhibiting different spatial trends. Wood stoves were common in rural areas and among lower income households, whereas fireplaces were more common in urban areas. Additionally, we observed large spatial and regional variability in emissions per residence due to differences in wood consumption, appliance ownership rates, and appliance mixes (e.g. conventional vs certified). Our results suggest that using simple spatial proxies based on population, urbanization levels or residence type are not enough to explain the spatial distribution of RWB emissions as they might overlook other factors such as socioeconomic factors or regional heating preferences. Finally, our spatially distributed emissions were strongly correlated (r = 0.86) with the increase in PM2.5 concentrations during peak-RWB hours on winter weekends at 42 reference stations across the province of Quebec.

Inter-individual variability of aerobic capacity after rehabilitation exercise training in children with severe burn injury

IntroductionSevere burn trauma damages resting and exercise cardiac function that may affect long term cardiovascular health. The implementation of rehabilitation exercise training (RET) soon after hospital discharge improves cardiorespiratory fitness; however, does not fully restore aerobic capacity and presents large inter-individual variability. We tested the hypothesis that the inter-individual variability of aerobic capacity for responders (R) compared to nonresponders (NR) would differ for exercise frequency and intensity. MethodsThirty-three children (11 female, [mean±SD] 12 ± 3 years, 145 ± 18 cm, 40 ± 11 kg, 49 ± 31 BMI percentile) with severe burns (49 ± 15 % total body surface area burned, with 35 ± 22 % third-degree burns) completed a 6-week RET program. Cardiorespiratory fitness (peak VO2) was measured before and after RET. Frequency (session days/week), intensity (% peak heart rate), time (min/session), and volume (min/week) were compared between responders and non-responders. Significance was set at p < 0.05. ResultsSixty-four percent of the study population improved peak VO2 after RET whereas 36 % showed no improvements. Using a 2-way factorial ANOVA (group [G] × week [WK]), we found that exercise frequency and session time were similar and increased slightly over 6-weeks between R and NR (main effect for WK; P < 0.002). Exercise volume was significantly lower on week 2 for NR compared to R (G × WK interaction, P < 0.028). Exercise intensity over 6-weeks was significantly lower in the NR compared to the R group (G × WK interaction, P < 0.022). ConclusionsExercise intensity and volume may be important contributors for improving the interindividual response to exercise training for peak VO2. These data suggest that the appropriate dose-response requirement for exercise intensity may be > 80 % peak heart rate and exercise volume of > 150 min per week. Further understanding of the exercise prescription will provide insights important for cardiovascular rehabilitation in children with severe burns. Subject codeInter-individual Variability, Exercise, Pediatrics, Exercise Training

BAI-Net: Individualized Anatomical Cerebral Cartography Using Graph Neural Network.

Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individualization network (BAI-Net) is an algorithm that subdivides individual cerebral cortex into segregated areas using brain morphology and connectomes. The presented method integrates group priors derived from a population atlas, adjusts areal probabilities using the context of connectivity fingerprints derived from the fiber-tract embedding of tractography, and provides reliable and explainable individualized brain areas across multiple sessions and scanners. We demonstrate that BAI-Net outperforms the conventional iterative clustering approach by capturing significantly heritable topographic variations in individualized cartographies. The topographic variability of BAI-Net cartographies has shown strong associations with individual variability in brain morphology, connectivity as well as higher relationship on individual cognitive behaviors and genetics. This study provides an explainable framework for individualized brain cartography that may be useful in the precise localization of neuromodulation and treatments on individual brains.

Something to Chew on; Plate-Waste at an Ontario Veteran's Centre.

There is paucity of data assessing levels of food/beverage waste in long-term care (LTC) facilities, especially in Ontario. Observations in the Veteran's Centre (VC) at Sunnybrook Health Sciences Centre (Sunnybrook) indicated food/beverage waste may be high, potentially impacting sustainability efforts within our institution. Before proceeding with waste reduction efforts, we conducted a comprehensive 3-day waste-audit of food/beverage items provided to VC residents with the goal of understanding the extent of food/beverage waste at VC, items wasted, and any other factors that may inform future changes. Our results indicate that 28% of items served to residents were wasted. Lunch was the meal with greatest waste at 31% and waste of solid items was 12% higher than that of liquids. We observed a large variability in waste between residents and within each resident, with 15% of residents wasting >50% of items provided. This study provides a deeper insight into the magnitude of food/beverage waste in a LTC population and highlights the importance of considering individualized strategies to address waste to avoid negative impact on residents.

TAVR with self- expandable valves: what challenges in bicuspid aortic valve disease?

Background: The bicuspid aortic valve (BAV) represents a complex anatomic entity for transcatheter aortic valve replacement (TAVR) because of its unique technical challenges. All the randomized clinical trials comparing outcomes of TAVI with surgery till date, have excluded patients with bicuspid aortic valve. The only data in literature come from empirical observations, with large variability in implantation techniques across different centers and operators. Objective: These case reports describe the results of transcatheter aortic valve replacement (TAVR) in bicuspid aortic valve stenosis (BAV) using self-expandable valves with their clinical, imaging, procedural, and follow-up settings. Case reports: We report 3 cases of TAVR with BAV. The patients were at high risk of surgery. Baseline cardiac computed tomography (CT) was analyzed by interventional cardiologists. We used self-expandable valves in all the procedures. There were no cases of valve embolization or need for a second valve, no post-dilatation, no cases of moderate or severe aortic regurgitation, and none of the cases required a permanent pacemaker implantation. Conclusion: With the available observational data, it seems that TAVI is indeed technically feasible in selected BAV patients with acceptable results. Self-expandable valves can be safe and effective devices for these procedures. Most of the current data studies highlight the importance of preprocedural imaging determining the outcome of TAVI in BAV patients. Advancements device iterations and procedural refinements will continue to improve the outcomes of these patients treated by TAVR.

Population pharmacokinetics and CD20 binding dynamics for mosunetuzumab in relapsed/refractory B-cell non-Hodgkin lymphoma.

Mosunetuzumab (Mosun) is a CD20xCD3 T-cell engaging bispecific antibody that redirects T cells to eliminate malignant B cells. The approved step-up dose regimen of 1/2/60/30 mg IV is designed to mitigate cytokine release syndrome (CRS) and maximize efficacy in early cycles. A population pharmacokinetic (popPK) model was developed from 439 patients with relapsed/refractory B-Cell Non-Hodgkin lymphoma receiving Mosun IV monotherapy, including fixed dosing (0.05-2.8 mg IV every 3 weeks (q3w)) and Cycle 1 step-up dosing groups (0.4/1/2.8-1/2/60/30 mg IV q3w). Prior to Mosun treatment, ~50% of patients had residual levels of anti-CD20 drugs (e.g., rituximab or obinutuzumab) from prior treatment. CD20 receptor binding dynamics and rituximab/obinutuzumab PK were incorporated into the model to calculate the Mosun CD20 receptor occupancy percentage (RO%) over time. A two-compartment model with time-dependent clearance (CL) best described the data. The typical patient had an initial CL of 1.08 L/day, transitioning to a steady-state CL of 0.584 L/day. Statistically relevant covariates on PK parameters included body weight, albumin, sex, tumor burden, and baseline anti-CD20 drug concentration; no covariate was found to have a clinically relevant impact on exposure at the approved dose. Mosun CD20 RO% was highly variable, attributed to the large variability in residual baseline anti-CD20 drug concentration (median = 10 μg/mL). The 60 mg loading doses increased Mosun CD20 RO% in Cycle 1, providing efficacious exposures in the presence of the competing anti-CD20 drugs. PopPK model simulations, investigating Mosun dose delays, informed treatment resumption protocols to ensure CRS mitigation.

Large inter-individual variability in force-velocity profile changes in response to acute high-load resistance training.

While the acute effects of high-load resistance training on the force generating capacity of muscles have been widely examined, limited data exist on the relationship with the force-velocity profile (FV). Evidence suggests high sensitivity of the vertical FV profile to monitor changes in the muscle's mechanical properties according to the type of the exercise protocol. However, the interpretation of the findings seems not as straightforward. Therefore, the purpose of this study was to examine the effects of a high-load resistance training protocol on the muscle's mechanical properties during loaded jumps and on the vertical force-velocity profile (FV) in relation to maximal strength. 29 resistance-trained male (mean age±SD: 35.4 ± 7.8 years) and 29 female athletes (mean age±SD: 32.5 ± 7.0 years) participated in the study. Five-repetition maximum (5RM) in back squat, unloaded countermovement jump (CMJ) and FV profile were assessed. Loaded jumps were performed against 25, 50, 75, and 100 percent of body mass. Participants performed exercise protocols corresponding to their 5RM. Immediately after, unloaded CMJ and FV profile measurements were repeated. A significant decrease in CMJ height (∼5-6%) and in average power (∼4%) was recorded for both men and women. The FV profile did not change after the exercise protocol; however, there was a significant decrease in theoretical maximal power (from 4 to 5%) and in theoretical maximal velocity (∼3%). Maximal strength was not associated with the changes in FV profile. Findings suggest that an acute high-load exercise decreased vertical jump performance and maximal power output, but without a concomitant change in FV profile. The large interindividual variability in FV measures indicates a less straightforward connection of the applied exercise with the acute response in the FV profile, highlighting the complexity of the FV profile to monitor changes in response to an acute training load.

Effects of electrode shape in micro-electro-discharge patterning of carbon nanotube forests

This paper reports the performance of different tool electrode structures in micropatterning of vertically aligned carbon nanotube (CNT) forests through dry micro-electro-discharge machining (μEDM). Micro cylindrical electrodes of tungsten with flat and hemispherically rounded tips were employed in this study. The rounded-tip electrode was shaped using a custom wet μEDM process. Both types of electrodes were used to pattern arrays of micro test slots at different depths and discharge voltages on a multi-walled CNT forest from which the resultant discharge gaps were experimentally characterized. The rounded-tip electrode showed a 2.1–2.4× reduction in the discharge gap with applied voltages of 15–20 V, suggesting its potential to improve the lateral resolution of the patterning process when compared with the flat-tip case. Finite element analysis of the process configuration with these two electrodes was conducted and showed good agreement with the improvement factors of the discharge gap experimentally observed. However, the process using the rounded-tip electrode resulted in larger variations on the bottom surfaces of the created patterns, while the use of the flat-tip electrode provided higher uniformity on these surfaces, revealing an apparent trade-off relationship between the discharge gap and the patterning uniformity for the two electrode types under the tested conditions. Elemental analysis of the processed CNT forest surfaces showed no detectable contamination from the electrodes onto the forest structures patterned using either electrode.

An improved and extended parameterization of the CO2 15 µm cooling in the middle and upper atmosphere (CO2_cool_fort-1.0)

Abstract. The radiative infrared cooling of CO2 in the middle atmosphere, where it emits under non-local thermodynamic equilibrium (non-LTE) conditions, is a crucial contribution to the energy balance of this region and hence to establishing its thermal structure. The non-LTE computation is too CPU time-consuming to be fully incorporated into climate models, and hence it is parameterized. The most used parameterization of the CO2 15 µm cooling for Earth's middle and upper atmosphere was developed by Fomichev et al. (1998). The valid range of this parameterization with respect to CO2 volume mixing ratios (VMRs) is, however, exceeded by the CO2 of several scenarios considered in the Coupled Climate Model Intercomparison Projects, in particular the abrupt-4×CO2 experiment. Therefore, an extension, as well as an update, of that parameterization is both needed and timely. In this work, we present an update of that parameterization that now covers CO2 volume mixing ratios in the lower atmosphere from ∼0.5 to over 10 times the CO2 pre-industrial value of 284 ppmv (i.e. 150 to 3000 ppmv). Furthermore, it is improved by using a more contemporary CO2 line list and the collisional rates that affect the CO2 cooling rates. Overall, its accuracy is improved when tested for the reference temperature profiles as well as for measured temperature fields covering all expected conditions (latitude and season) of the middle atmosphere. The errors obtained for the reference temperature profiles are below 0.5 K d−1 for the present-day and lower CO2 VMRs. Those errors increase to ∼1–2K d−1 at altitudes between 110 and 120 km for CO2 concentrations of 2 to 3 times the pre-industrial values. For very high CO2 concentrations (4 to 10 times the pre-industrial abundances), those errors are below ∼1 K d−1 for most regions and conditions, except at 107–135 km, where the parameterization overestimates them by ∼1.2 %. These errors are comparable to the deviation of the non-LTE cooling rates with respect to LTE at about 70 km and below, but they are negligible (several times smaller) above that altitude. When applied to a large dataset of global (pole to pole and four seasons) temperature profiles measured by MIPAS (Michelson Interferometer for Passive Atmospheric Spectroscopy) (middle- and upper-atmosphere mode), the errors of the parameterization for the mean cooling rate (bias) are generally below 0.5 K d−1, except between 5×10-3 and 3×10-4 hPa (∼85–98 km), where they can reach biases of 1–2 K d−1. For single-temperature profiles, the cooling rate error (estimated by the root mean square – rms – of a statistically significant sample) is about 1–2 K d−1 below 5×10-3 hPa (∼85 km) and above 2×10-4 hPa (∼102 km). In the intermediate region, however, it is between 2 and 7 K d−1. For elevated stratopause events, the parameterization underestimates the mean cooling rates by 3–7 K d−1 (∼10 %) at altitudes of 85–95 km and the individual cooling rates show a significant rms (5–15 K d−1). Further, we have also tested the parameterization for the temperature obtained by a high-resolution version of the Whole Atmosphere Community Climate Model (WACCM-X), which shows a large temperature variability and wave structure in the middle atmosphere. In this case, the mean (bias) error of the parameterization is very small, smaller than 0.5 K d−1 for most atmospheric layers, reaching only maximum values of 2 K d−1 near 5×10-4 hPa (∼ 96 km). The rms has values of 1–2 K d−1 (∼20 %) below ∼2×10-2 hPa (∼80 km) and values smaller than 4 K d−1 (∼2 %) above 10−4 hPa (∼105 km). In the intermediate region between ∼5×10-3 and ∼2×10-4 hPa (85–102 km), the rms is in the range of 5–12 K d−1. While these values are significant in percentage at ∼5×10-3–5×10-4 hPa, they are very small above ∼5×10-4 hPa (96 km). The routine is very fast, taking (1.5–7.5) ×10-5 s, depending on the extension of the atmospheric profile, the processor and the Fortran compiler.