Predictive Accuracy of the Nelson Equation via BodPod Compared to Commonly Used Equations to Estimate Resting Metabolic Rate in Adults

International Journal of Exercise Science 14(2): 1166-1177, 2021. Indirect calorimetry (IC) is considered the gold standard for assessing resting metabolic rate (RMR). However, many people do not have access to IC devices and use prediction equations for RMR estimation. Equations using fat free mass (FFM) as a predictor have been developed to estimate RMR, as a strong relationship exists between FFM and RMR. One such equation is the Nelson equation which is used by the BodPod (BP). Yet, there is limited evidence whether the Nelson equation is superior to other common equations to predict RMR. To examine the agreement between predicted RMR from common RMR equations and the BP, and RMR measured via IC. Data from 48 healthy volunteers who completed both the BP and IC were collected. Agreement between RMR measured by BP, common regression equations, and indirect caloriometry was evaluated using repeated measures ANOVA, Bland-Altman analysis and root mean square error (RMSE). Predicted RMR values from common equations and BP were significantly different from IC with the exception of the World Health Organization (WHO) equation. Large limits of agreement and RMSE values demonstrate a large amount of error at the individual level. Despite the use of FFM, the Nelson equation does not appear to be superior to other common RMR equations. Although the WHO equation presented the best option within our sample, all equations performed poorly at the individual level. Clinicians should be aware that prediction equations may significantly under- or overestimate RMR compared to IC and when an accurate value of RMR is required, IC is recommended

Improving Syntactic Relationships Between Language and Objects

This paper presents the integration of natural language processing and computer vision to improve the syntax of the language generated when describing objects in images. The goal was to not only understand the objects in an image, but the interactions and activities occurring between the objects. We implemented a multi-modal neural network combining convolutional and recurrent neural network architectures to create a model that can maximize the likelihood of word combinations given a training image. The outcome was an image captioning model that leveraged transfer learning techniques for architecture components. Our novelty was to quantify the effectiveness of transfer learning schemes for encoders and decoders to qualify which were the best for improving syntactic relationships. Our work found the combination of ResNet feature extraction and fine-tuned BERT word embeddings to be the best performing architecture across two datasets - a valuable discovery for those continuing this work considering the cost of compute for these complex models

Accounting for food availability reveals contaminant-induced breeding impairment, food-modulated contaminant effects, and endpoint-specificity of exposure indicators in free ranging avian populations

It remains unclear how sub-lethal effects of contaminants play out in relation to other stressors encountered by free-ranging populations. Effects may be masked or influenced by interactions with field stressors such as food availability. We predicted that (1) including food availability, and particularly its interaction with Hg, would reveal or enhance associations between Hg and breeding endpoints. We further predicted that (2) breeding impairment associated with Hg would be higher under food stress conditions. We monitored Hg and nest success of great egrets (Ardea alba) in eight breeding colonies in the Florida Everglades over 11 years. We characterized variation in local food availability among colonies and years using fish biomass and recession range -a proxy to fish vulnerability. We used two Hg exposure indicators (egg albumen Hg and nestling feather Hg) and six breeding endpoints (clutch-size, brood-size, fledged-size, hatching success, post-hatching success and fledglings per egg) to assess whether variation in food availability influenced associations between Hg and these endpoints. Accounting for interactions between Hg and food availability, we identified statistically significant associations in all 12 indicator-endpoint combinations, while only three were detectable without food. Further, 10 combinations showed interactions between Hg and components of food availability. Our results also indicated an endpoint-specific affinity, with albumen [Hg] explaining more variation in hatching success while nestling feather [Hg] explained more variation in post-hatching survival. Both Hg indicators accounted for relevant (6-10%) amounts of variation in fledglings produced per egg laid, an integrative endpoint. Increased Hg exposure resulted in overall reduced reproductive success when food availability was low, but our models predicted low or no effects of increasing Hg exposure when food availability was high. Our results indicate that Hg induced impairment is strongly driven by food availability, providing a framework that accommodates previously contradictory results in the literature.This work was supported by the U.S. Army Corps of Engineers (Cooperative Agreement W912HZ-12-02-0007) and by grants of equipment from the University of Florida Institute of Food and Agricultural Sciences. Egret monitoring was performed under University of Florida IACUC permit 201708650. Fish samplingwas supported by Cooperative Agreements W912HZ-11-2-0048 and W912HZ-16-2-0008 between Florida International University (FIU) and the U.S. Army Corps of Engineers and Contract 4600001083 between FIU and the South Florida Water Management District. Fish monitoring was performed under FIU IACUC permits including IACUC-08-004, -09-029, -10-026, 12-020, -13-060, and -16-033

GOES-17 Advanced Baseline Imager Performance Recovery Summary

The 17th Geostationary Operational Environmental Satellite (GOES-17) was launched on 1 March 2018. The Advanced Baseline Imager (ABI) is the primary instrument on the GOES-R series for weather and environmental monitoring. The GOES-17 ABI (flight model 2) experienced a degradation in its thermal system that limits ABI's ability to shed solar heat load. This limitation resulted in significant reduction in performance after initial turn on with only 3 of 16 spectral channels expected to be available for much of the year. A combined government/vendor team was tasked with optimizing the operation of ABI to recapture as much performance as possible. By modifying the operational configuration and sensor parameters, the team was able to regain over 97% imaging capability.This was accomplished by taking advantage of the considerably flexible nature of ABI's design to adapt its configuration to the new reality and improve capabilities for many of ABI's subsystems. The significant differences in operational configuration, sensor parameter optimization, and algorithm optimization will be discussed as well as their impact on performance and data availability

Factors Influencing Sleep Difficulty and Sleep Quantity in the Citizen Pscientist Psoriatic Cohort.

IntroductionSleep is essential for overall health and well-being, yet more than one-third of adults report inadequate sleep. The prevalence is higher among people with psoriasis, with up to 85.4% of the psoriatic population reporting sleep disruption. Poor sleep among psoriasis patients is particularly concerning because psoriasis is independently associated with many of the same comorbidities as sleep dysfunction, including cardiovascular disease, obesity, and depression. Given the high prevalence and serious consequences of disordered sleep in psoriasis, it is vital to understand the nature of sleep disturbance in this population. This study was designed to help meet this need by using survey data from Citizen Pscientist, an online patient portal developed by the National Psoriasis Foundation.MethodsOur analysis included 3118 participants who identified as having a diagnosis by a physician of psoriasis alone or psoriasis with psoriatic arthritis. Demographic information, psoriasis severity and duration, sleep apnea status, smoking and alcohol consumption, itch timing, and sleep characteristics were included. Two separate multivariate logistic regression models in STATA were used to determine whether the presence of psoriatic arthritis, age, gender, body mass index, comorbid sleep apnea, psoriasis severity, timing of worst itch, smoking status, or high-risk alcohol consumption were associated with sleep difficulty or low sleep quantity, defined by the American Academy of Sleep Medicine as less than 7 h of sleep per night on average.ResultsResults from the multivariate logistic regressions found that sleep difficulty was associated with psoriatic arthritis (OR 2.15, 95% CI [1.79-2.58]), female gender (2.03 [1.67-2.46]), obese body mass index (BMI ≥ 30) (1.25 [1.00-1.56]), sleep apnea (1.41 [1.07-1.86]), psoriasis severity of moderate (1.59 [1.30-1.94]) or severe (2.40 [1.87-3.08]), and smoking (1.60 [1.26-2.02]). Low sleep quantity was associated with obese BMI (1.62 [1.29-2.03]), sleep apnea (1.30 [1.01-1.68]), psoriasis severity of moderate (1.41 [1.16-1.72]) or severe (1.40 [1.11-1.76]), and smoking (1.62 [1.31-2.00]). Sleep difficulty and low sleep quantity were not associated with age, alcohol consumption, or timing of worst itch.ConclusionThese results are potentially meaningful in several aspects. We identify an important distinction between sleep difficulty and sleep quantity in psoriatic disease, whereby having psoriatic arthritis and being female are each associated with sleep difficulty despite no association with low sleep quantity. Furthermore, there is conflicting evidence from prior studies as to whether psoriasis severity is associated with sleep difficulty, but this well-powered, large study revealed a strong, graded relationship between psoriasis severity and both sleep difficulty and low sleep quantity. Overall, our results show that both sleep difficulty and low sleep quantity were associated with multiple factors in this analysis of a large psoriatic cohort. These findings suggest that dermatologists may gather clinically useful information by screening psoriatic patients for trouble sleeping and low sleep quantity to identify potential comorbidities and to more effectively guide disease management

Simulated Tibiofemoral Joint Reaction Forces for Three Previously Studied Gait Modifications in Healthy Controls

Gait modifications, such as lateral trunk lean (LTL), medial knee thrust (MKT), and toe-in gait (TIG), are frequently investigated interventions used to slow the progression of knee osteoarthritis. The Lerner knee model was developed to estimate the tibiofemoral joint reaction forces (JRF) in the medial and lateral compartments during gait. These models may be useful for estimating the effects on the JRF in the knee as a result of gait modifications. We hypothesized that all gait modifications would decrease the JRF compared to normal gait. Twenty healthy individuals volunteered for this study (26.7 ± 4.7 years, 1.75 ± 0.1 m, 73.4 ± 12.4 kg). Ten trials were collected for normal gait as well as for the three gait modifications: LTL, MKT, and TIG. The data were used to estimate the JRF in the first and second peaks for the medial and lateral compartments of the knee via opensim using the Lerner knee model. No significant difference from baseline was found for the first peak in the medial compartment. There was a decrease in JRF in the medial compartment during the loading phase of gait for TIG (6.6%) and LTL (4.9%) and an increasing JRF for MKT (2.6%). but none was statistically significant. A significant increase from baseline was found for TIG (5.8%) in the medial second peak. We found a large variation in individual responses to gait interventions, which may help explain the lack of statistically significant results. Possible factors influencing these wide ranges of responses to gait modifications include static alignment and the impacts of variation in muscle coordination strategies used, by participants, to implement gait modifications

JWST/MIRI Spectroscopy of the Disk of the Young Eruptive Star EX Lup in Quiescence

EX Lup is a low-mass pre-main sequence star that occasionally shows accretion-related outbursts. Here, we present JWST/MIRI medium resolution spectroscopy obtained for EX Lup fourteen years after its powerful outburst. EX Lup is now in quiescence and displays a Class II spectrum. We detect a forest of emission lines from molecules previously identified in infrared spectra of classical T Tauri disks: H2O, OH, H2, HCN, C2H2, and CO2. The detection of organic molecules demonstrates that they are back after disappearing during the large outburst. Spectral lines from water and OH are for the first time de-blended and will provide a much improved characterization of their distribution and density in the inner disk. The spectrum also shows broad emission bands from warm, sub-micron size amorphous silicate grains at 10 and 18 um. During the outburst, in 2008, crystalline forsterite grains were annealed in the inner disk within 1 au, but their spectral signatures in the 10 um silicate band later disappeared. With JWST we re-discovered these crystals via their 19.0, 20.0, and 23.5 um emission, whose strength implies that the particles are at ~3 au from the star. This suggests that crystalline grains formed in 2008 were transported outwards and now approach the water snowline, where they may be incorporated into planetesimals. Containing several key tracers of planetesimal and planet formation, EX Lup is an ideal laboratory to study the effects of variable luminosity on the planet-forming material and may provide explanation for the observed high crystalline fraction in solar system comets.Comment: 9 pages, 4 figures, accepted for publication in ApJL. JWST/MIRI spectrum is available for download at https://tinyurl.com/spexodisksJWS

Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection

Purpose: To develop a new three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) data analysis method using a machine learning technique based on variable-size super pixel segmentation that efficiently utilizes full 3D dataset to improve the discrimination between early glaucomatous and healthy eyes. Methods: 192 eyes of 96 subjects (44 healthy, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with SD-OCT. Each SD-OCT cube dataset was first converted into 2D feature map based on retinal nerve fiber layer (RNFL) segmentation and then divided into various number of super pixels. Unlike the conventional super pixel having a fixed number of points, this newly developed variable-size super pixel is defined as a cluster of homogeneous adjacent pixels with variable size, shape and number. Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes. For discriminating performance assessment, area under the curve (AUC) of the receiver operating characteristics of the machine classifier outputs were compared with the conventional circumpapillary RNFL (cpRNFL) thickness measurements. Results: The super pixel analysis showed statistically significantly higher AUC than the cpRNFL (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminated from healthy, while no significant difference was found when confirmed glaucoma eyes were discriminated from healthy eyes. Conclusions: A novel 3D OCT analysis technique performed at least as well as the cpRNFL in glaucoma discrimination and even better at glaucoma suspect discrimination. This new method has the potential to improve early detection of glaucomatous damage. © 2013 Xu et al

Host-linked soil viral ecology along a permafrost thaw gradient

Climate change threatens to release abundant carbon that is sequestered at high latitudes, but the constraints on microbial metabolisms that mediate the release of methane and carbon dioxide are poorly understood1,2,3,4,5,6,7. The role of viruses, which are known to affect microbial dynamics, metabolism and biogeochemistry in the oceans8,9,10, remains largely unexplored in soil. Here, we aimed to investigate how viruses influence microbial ecology and carbon metabolism in peatland soils along a permafrost thaw gradient in Sweden. We recovered 1,907 viral populations (genomes and large genome fragments) from 197 bulk soil and size-fractionated metagenomes, 58% of which were detected in metatranscriptomes and presumed to be active. In silico predictions linked 35% of the viruses to microbial host populations, highlighting likely viral predators of key carbon-cycling microorganisms, including methanogens and methanotrophs. Lineage-specific virus/host ratios varied, suggesting that viral infection dynamics may differentially impact microbial responses to a changing climate. Virus-encoded glycoside hydrolases, including an endomannanase with confirmed functional activity, indicated that viruses influence complex carbon degradation and that viral abundances were significant predictors of methane dynamics. These findings suggest that viruses may impact ecosystem function in climate-critical, terrestrial habitats and identify multiple potential viral contributions to soil carbon cycling