Large Variations Research Articles

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 < 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 < 2, EK is contributed mainly by IW, and EP by QG, while, for Bu > 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.

Role of gastrointestinal appetite hormones in the success of body mass loss during interventions based on exercise combined with restricted energy intake diets with different fat and carbohydrate content

Restriction of energy intake is an effective strategy for achieving body mass loss(1). However, it leads to gastrointestinal appetite hormonal changes, which are expected to enhance hunger, reduce satiety, and possibly impair the ability to comply with prescribed intervention(2). Exercise interventions, on the other hand, did not modify gastrointestinal appetite hormones(3) and there is some evidence that postprandial appetite hormone responses after a low carbohydrate-high fat meal (LCHF) are more favorable than after high carbohydrate-low fat meal (HCLF) meal (4). This study investigated how energy-restricted diets based on LCHF and HCLF meals and combined with exercise affect body weight, and gastrointestinal appetite hormones and explored the relationship between body mass changes and changes in gastrointestinal appetite hormones.Twenty-seven overweight and obese females (age: 35 ± 9.35 years, BMI: 31.64 ± 5.08 kg/m2) completed a randomized parallel design study and underwent a 4-week intervention consisting of brisk walking combined with either consumption of calorie-restricted HCLF or LCHF diet. Participants underwent a 5-hour experimental trial before and after the interventions, which included body weight measurements and fasting and postprandial blood sample collection for gastrointestinal appetite hormone measurements.Both interventions significantly reduced body mass (HCLF group, −2.1 ± 1.7 Kg: LCHF group, -1.7 ± 1.7 Kg, P < 0.05 for both groups), but changes between the groups were not significant (P>0.05). Body mass changes were individual and ranged from -0.4 kg to −5.3 kg and from +1 kg to −4.4 kg in the HCLF and LCHF groups, respectively. The interventions had no impact on mean values of time- averaged postprandial concentrations of ghrelin, PYY, and GLP-1 but there was large individual variability in changes of these hormones. Results combined from both groups revealed that changes in ghrelin ranged from +549.3 to -407.7 pg/ml, in PYY from −132.5 to + 96.4 pg/ml and in GLP-1 from −6.6 to +19.5 pg/ml. Correlation, investigated on the data from both groups combined, between changes in body mass and changes in ghrelin (r = −0.18, P= 0.41), PYY (r = −0.04, P =0.87), and GLP-1 (r = 0.11, P = 0.64) were not significant.In conclusion, the extent of body mass loss during interventions based on restricted energy intake diets combined with exercise is not related to the carbohydrate and fat content of the diet. Our findings also suggest that the success of body mass loss cannot be predicted by changes in postprandial concentrations of gastrointestinal appetite hormones.

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.

Using crack width for shear, stiffness, and stirrup strain history predictions for reinforced concrete beams

Shear failures in reinforced concrete structures occur with little or no warning. Reinforced concrete members with shear cracks should be evaluated to ensure safety. Existing evaluation methods have large variability, require time-consuming modeling or expert opinion. This study uses machine learning to investigate correlations of crack width with shear loading, stiffness, and stirrup strain histories. Experimental literature enables the assembly of a database of rectangular reinforced concrete slender beams with crack width measurements for beams with shear reinforcement amounts smaller and larger than the minimum required by ACI 318-19. Measured data include crack widths from 122 beams, load–displacement relationship from 100 beams, and stirrup strains from 46 beams. Gaussian Process Regression is used to correlate crack width and geometric, material and design properties to shear loading, stiffness, and stirrup strain histories. Ten-fold cross validation training shows mean absolute percent errors of 18, 33 and 77% for shear, stiffness, and stirrup strain history predictions. The proposed algorithms can be used to accelerate the evaluation of in-service structures and can be updated upon availability of additional data.

Machine learning assisted improved desalination pilot system design and experimentation for the circular economy

Desalination is among the most feasible solutions to supply sustainable and clean drinking water in water scarcity areas. In this regard, Multi-Effect Desalination (MED) systems are particularly preferred for harsh feeds (high temperature and salinity) because of their robust mode of operation for water production. However, maintaining the efficient operation of the MED systems is challenging because of the large system design and variables' interdependencies that are sensitive to the distillate production. Therefore, this research leverages the power of machine learning and optimization to estimate the optimal operating conditions for the maximum distillate production from the MED system. In the first step, detailed experimentation is conducted for distillate production against hot water temperature (HWT) varying from 38 to 70 °C, and feed water temperature (FWT) is changed from 34 to 42 °C. Whereas, the feed flow rate (FFR) is investigated to be varied nearly from 3.6 to 8.7 LPM in the three stages, i.e., FFR-S1, FFR-S2 and FFR-S3. The compiled dataset is used to make the process models of the MED system by three ML-based algorithms, i.e., Artificial Neural Network (ANN), Support Vector Machine (SVM), and Gaussian Process Regression (GPR) under rigorous hyperparameters optimization. GPR exhibited superior predictive performance than those of ANN and SVM on R2 value of 0.99 and RMSE of 0.026 LPM. Monte Carlo technique-based variable significance analysis revealed that the HWT has the highest effect on distillate production with a percentage significance of 95.6 %. Then Genetic Algorithm is used to maximize the distillate production with the GPR model embedded in the optimization problem. The GPR-GA driven maximum distillate production is estimated on HWT = 70 ± 0.5 °C, FWT = 40 ± 2.5 °C, FFR-S1 = 6 ± 2.6 LPM, FFR-S2 = 7 ± 1 LPM and FFR-S3 = 7 ± 1. The ML-GA-based system analysis and optimization of the MED system can boost the distillate production that promotes operation excellence and circular economy from the desalination sector.

Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip

By mimicking the neurons and synapses of the human brain and employing spiking neural networks on neuromorphic chips, neuromorphic computing offers a promising energy-efficient machine intelligence. How to borrow high-level brain dynamic mechanisms to help neuromorphic computing achieve energy advantages is a fundamental issue. This work presents an application-oriented algorithm-software-hardware co-designed neuromorphic system for this issue. First, we design and fabricate an asynchronous chip called “Speck”, a sensing-computing neuromorphic system on chip. With the low processor resting power of 0.42mW, Speck can satisfy the hardware requirements of dynamic computing: no-input consumes no energy. Second, we uncover the “dynamic imbalance” in spiking neural networks and develop an attention-based framework for achieving the algorithmic requirements of dynamic computing: varied inputs consume energy with large variance. Together, we demonstrate a neuromorphic system with real-time power as low as 0.70mW. This work exhibits the promising potentials of neuromorphic computing with its asynchronous event-driven, sparse, and dynamic nature.

Illuminating Snow Droughts: The Future of Western United States Snowpack in the SPEAR Large Ensemble

AbstractSeasonal snowpack in the Western United States (WUS) is vital for meeting summer hydrological demands, reducing the intensity and frequency of wildfires, and supporting snow‐tourism economies. While the frequency and severity of snow droughts (SD), that is, anomalously low snowpacks, are expected to increase under continued global warming, the uncertainty from internal climate variability remains challenging to quantify with observations alone. Using a 30‐member large ensemble from a state‐of‐the‐art global climate model, the Seamless System for Prediction and EArth System Research (SPEAR), and an observations‐based data set, we find WUS SD changes are already significant. By 2100, SPEAR projects SDs to be nearly 9 times more frequent under shared socioeconomic pathway 5‐8.5 (SSP5‐8.5) and 5 times more frequent under SSP2‐4.5, compared to a 1921–2011 average. By investigating the influence of the two primary drivers of SD, temperature and precipitation amount, we find the average WUS SD will become warmer and wetter. To assess how these changes affect future summer water availability, we track late winter and spring snowpack across WUS watersheds, finding differences in the onset time of a “no‐snow” threshold between regions and large internal variability within the ensemble that are both on the order of decades. We attribute the inter‐regional variability to differences in the regions' mean winter temperature and the intra‐regional variability to irreducible internal climate variability which is not well‐explained by temperature variations alone. Despite strong scenario forcing, internal climate variability will continue to drive variations in SD and no‐snow conditions through 2100.

High variability in self-pay pricing for vasectomy and vasectomy reversal in the United States.

In the United States (US) men who undergo vasectomy and/or vasectomy reversal (vasovasotomy) are likely to pay out-of-pocket for these procedures. We characterized the publicly disclosed pricing of both procedures with a focus on variability in self-pay prices. We queried all US hospitals for publicly disclosed prices of vasectomy and vasovasotomy. We assessed interhospital variability in self-pay pricing and compared hospitals charging high (≥75th percentile) and low (≤25th percentile) self-pay prices for either procedure. We also examined trends in pricing after the 2022 US Supreme Court decision that allowed individual states to ban abortion. Of 6692 hospitals, 1375 (20.5%) and 281 (4.2%) disclosed self-pay prices for vasectomy and vasovasotomy, respectively. There was a 17-fold difference between the 10th and 90th percentile self-pay prices for vasectomy ($421-$7147) and a 39-fold difference for vasovasotomy ($446-$17,249). Compared with hospitals charging low (≤25th percentile) self-pay prices for vasectomy or vasovasotomy, hospitals charging high (≥75th percentile) prices were larger (median 150 vs. 59 beds, p < 0.001) and more likely to be for-profit (31.2% vs. 7.8%, p < 0.001), academic-affiliated (52.7% vs. 23.1%, p < 0.001), and located in an urban zip code (70.1% vs. 41.3%, p < 0.001). From October 2022 to April 2023, the median self-pay price of vasectomy increased by 10% (from $1667 to $1832) while the median self-pay price of vasovasotomy decreased by 16% (from $3309 to $2786). We found large variability in self-pay pricing for vasectomy and vasectomy reversal, which may serve as a barrier to the accessibility of male reproductive care.

Neural responses to oral administration of erythritol vs. sucrose and sucralose explain differences in subjective liking ratings

IntroductionHigh sugar intake is associated with many chronic diseases. However, non-caloric sweeteners (NCSs) might fail to successfully replace sucrose due to the mismatch between their rewarding sweet taste and lack of caloric content. The natural NCS erythritol has been proposed as a sugar substitute due to its satiating properties despite being non-caloric. We aimed to compare brain responses to erythritol vs. sucrose and the artificial NCS sucralose in a priori taste, homeostatic, and reward brain regions of interest (ROIs). MethodsWe performed a within-subject, single-blind, counterbalanced fMRI study in 30 healthy men (mean ± SEM age:24.3 ± 0.8 years, BMI:22.3 ± 0.3 kg/m2). Before scanning, we individually matched the concentrations of both NCSs to the perceived sweetness intensity of a 10% sucrose solution. During scanning, participants received 1 mL sips of the individually titrated equisweet solutions of sucrose, erythritol, and sucralose, as well as water. After each sip, they rated subjective sweetness liking. ResultsLiking ratings were significantly higher for sucrose and sucralose vs. erythritol (both pHolm = 0.0037); water ratings were neutral. General Linear Model (GLM) analyses of brain blood oxygen level-depended (BOLD) responses at qFDR<0.05 showed no differences between any of the sweeteners in a priori ROIs, but distinct differences were found between the individual sweeteners and water. These results were confirmed by Bayesian GLM and machine learning-based models. However, several brain response patterns mediating the differences in liking ratings between the sweeteners were found in whole-brain multivariate mediation analyses. Both subjective and neural responses showed large inter-subject variability. ConclusionWe found lower liking ratings in response to oral administration of erythritol vs. sucrose and sucralose, but no differences in neural responses between any of the sweeteners in a priori ROIs. However, differences in liking ratings between erythritol vs. sucrose or sucralose are mediated by multiple whole-brain response patterns.

Relationship between oscillatory volume and oscillatory pressure in neonatal high-frequency oscillatory ventilators: an in vitro study

ObjectiveWe analysed the relationship between oscillatory volume (VOSC) and pressure amplitude (ΔP) in six neonatal high-frequency oscillatory (HFO) ventilators and related it to (1) the accuracy of VOSC and ΔP...

A Method for Evaluating Systematic Risk in Dams with Random Field Theory

The parameters of gravity dams and foundation materials objectively exhibit spatial variability due to environmental and load influences, which significantly affect the safety status of dam structures. Therefore, a safety risk analysis method for a gravity dam–foundation system based on random field theory is proposed in this paper. Spatial variabilities in materials are particularly considered by using the finite element method. Then, composite response surface equations for the performance function (PF) of strength and stability failure are established, and then, the system failure risk is obtained using the Monte Carlo method. The proposed method solves the problem wherein the effect of spatial variability on failure risk cannot be reflected accurately by the performance function of multi-element sliding paths, and the difficulties in solving the failure risk of the series–parallel system due to multiple failure paths and their complex correlations. The application of a gravity dam shows that the developed method overcomes the disadvantages of the traditional method, such as the homogenization of the spatially random characteristics of parameters and the overestimation of failure risk in the system due to large variance estimation.

Drought in the middle growing season inhibited carbon uptake more critical in an anthropogenic shrub ecosystem of Northwest China

Large-scale ecological restoration efforts of the degraded desert steppe ecosystem in northwestern China have effectively combated desertification but have also resulted in the encroachment of shrubs. However, the function of the anthropogenic shrub ecosystem is still unknown under climate change, especially the response of carbon fluxes to drought, which hinders our further understanding of its sustainability in the future. Hence, based on the eddy covariance and environment sensors, continuous carbon and water fluxes and biophysical factors were monitored during 2019–2022 to investigate the characteristics of carbon fluxes and their response to seasonal drought in an anthropogenic shrub ecosystem dominated by Caragana liouana in the southern Mu Us sandy land, northwestern China. The results showed the shrub ecosystem was a stable carbon sink over the 4 years despite large interannual variability, with annual net ecosystem production (NEP) ranging from 54.37 to 150.35 g C·m−2·y−1. Interannual variability in NEP was mainly due to variation in gross primary productivity (GPP), which is driven by the fact that interannual variability in GPP was higher than interannual variability in ecosystem respiration (Reco). While drought inhibited GPP more than Reco, thus weakening the carbon sequestration ability of the ecosystem during the middle growing season, and thereby carbon fluxes in the shrub ecosystem were significantly suppressed by drought in the middle growing season but slightly in the early growing season. Furthermore, the magnitude and variation of carbon fluxes were determined by the maximum apparent photosynthetic capacity of the canopy (Pmax), leaf area index (LAI), and canopy conductance (gc), all of which are limited by water stress during drought. The work suggests that to keep the carbon sink of anthropogenic shrub ecosystems in arid and semi-arid areas, managers should focus on drought-relief measures in the middle growing season that are better adapted to future climate change.