Fitness Metrics Research Articles

Electrical parameters identification for three diode photovoltaic based on the manta ray foraging optimization with dynamic fitness distance balance

This study focuses on the critical task of accurately simulating and operating solar cells and photovoltaic modules by acquiring essential model parameters through experimental data. These parameters play a pivotal role in assessing system performance under varying environmental conditions. However, due to the inherent nonlinearity of solar photovoltaic systems, a dependable algorithm is imperative for precise modeling. Introducing the MRFO-dFDB algorithm (Manta ray foraging optimization with Dynamic fitness distance balance), designed to address the intricate challenges associated with modeling solar photovoltaic systems. This algorithm harnesses fitness distance balance to maintain a harmonious balance between exploration and exploitation within the search space. Its adaptability to shifts in the search landscape, achieved through dynamic adjustments to distance and fitness metrics, enhances its versatility and effectiveness. Our extensive evaluation encompasses a comparative analysis of the MRFO-dFDB algorithm against six other optimization methods across diverse solar module types, including STP6–120/36, Photowatt-PWP201, XKD_50W, and XHYG-10W. The outcomes underscore the MRFO-dFDB algorithm's remarkable equilibrium between precision and computational efficiency. Numerical findings provide concrete evidence of the MRFO-dFDB algorithm's capabilities. For example, it achieves an RMSE of 0.002087 A and an NRMSE of 0.0015639 for the Photowatt-PWP201 module, underscoring its proficiency in parameter extraction. Similarly, for the STP6–120/36 module, it records an RMSE of 0.015194 A, highlighting its effectiveness in modeling.

Modeling personalized heart rate response to exercise and environmental factors with wearables data

Heart rate (HR) response to workout intensity reflects fitness and cardiorespiratory health. Physiological models have been developed to describe such heart rate dynamics and characterize cardiorespiratory fitness. However, these models have been limited to small studies in controlled lab environments and are challenging to apply to noisy—but ubiquitous—data from wearables. We propose a hybrid approach that combines a physiological model with flexible neural network components to learn a personalized, multidimensional representation of fitness. The physiological model describes the evolution of heart rate during exercise using ordinary differential equations (ODEs). ODE parameters are dynamically derived via a neural network connecting personalized representations to external environmental factors, from area topography to weather and instantaneous workout intensity. Our approach efficiently fits the hybrid model to a large set of 270,707 workouts collected from wearables of 7465 users from the Apple Heart and Movement Study. The resulting model produces fitness representations that accurately predict full HR response to exercise intensity in future workouts, with a per-workout median error of 6.1 BPM [4.4–8.8 IQR]. We further demonstrate that the learned representations correlate with traditional metrics of cardiorespiratory fitness, such as VO2 max (explained variance 0.81 ± 0.003). Lastly, we illustrate how our model is naturally interpretable and explicitly describes the effects of environmental factors such as temperature and humidity on heart rate, e.g., high temperatures can increase heart rate by 10%. Combining physiological ODEs with flexible neural networks can yield interpretable, robust, and expressive models for health applications.

Optimizing environmental monitoring in IoT: integrating DBSCAN with genetic algorithms for enhanced clustering

In our study, we introduce an advanced clustering method designed for IoT-based environmental monitoring. We’ve combined two powerful techniques, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Genetic Algorithms (GA), to create a specialized approach called EC-GAD (Enhanced-Clustering using Genetic Algorithms and DBSCAN). This integrated system model relies on DBSCAN, a robust clustering algorithm capable of handling irregular shapes and varying data densities, to group sensor nodes based on their physical proximity. To improve clustering performance, we’ve harnessed Genetic Algorithms to optimize the parameters of DBSCAN. Through a repetitive process involving selection, crossover, and mutation, GA refines parameter settings based on the quality of environmental clustering as assessed by fitness metrics. Our approach is tailored specifically for IoT deployments in environmental monitoring, which involve collecting data from sensor nodes and integrating DBSCAN and GA. We’ve paid special attention to choosing an appropriate distance metric and fine-tuning DBSCAN parameters such as epsilon (ε) and minPts to match the unique needs of environmental monitoring applications. Furthermore, we’ve taken energy efficiency into account by implementing energy-aware node selection and optimizing cluster formation to minimize energy consumption.

Is tourism impacting pumas in the Torres del Paine UNESCO Biosphere Reserve in southern Chile?

Ecotourism generates $7.6 trillion annually and supports 292 million jobs around the world, and because of these benefits, it has been emphasized as a conservation strategy. Ecotourism, however, is not the solution for every community, and there is growing evidence that tourism can have unintended consequences for local people and wildlife, unless well managed. Here, we assessed the relative impacts of anthropogenic activities related to tourism (e.g. road density, distance to ranches and camping areas) and prey distributions on puma occupancy and abundance in the open steppe habitat of the southeast portions of the Torres del Paine UNESCO Biosphere Reserve, where pumas are protected. In winter, puma abundance and occupancy were explained by both prey and anthropogenic metrics. In summer, puma abundance was explained by both prey and anthropogenic metrics, but occupancy only by anthropogenic metrics. Across both periods, puma abundance was only explained by anthropogenic metrics, and occupancy was explained by both prey and anthropogenic metrics. Guanacos, the most important prey species for local pumas, never appeared in any top model, except in summer, during which puma abundance was explained by lower not higher guanaco abundance. Not unexpectedly, puma abundance and occupancy were negatively correlated with trail and road density across all analyses. In the high tourism season of summer, roads had 2–3 times the impact on puma abundance than prey metrics in top models. During our study, the average number of monthly visitors per month in summer (November-January) was 892 % higher than winter (May- July). As a whole, we believe our analyses suggest that tourism may be negatively impacting pumas, but the severity of this impact remains questionable. Future work should include studying stress in puma populations experiencing heavy tourism, simultaneous with the work needed to understand relative thresholds of impact on puma fitness metrics (e.g. survival, hunting success). We believe our work also highlights the need for standards and guidelines to be created and adopted among those involved in puma tourism in Torres del Paine and beyond.

Molecular Biomarkers of Aging to Predict Clinical Frailty in Newly Diagnosed Hematologic Malignancy

Introduction: Older adults with hematologic malignancy (HM) are a growing demographic with wide-ranging therapeutic options. With aging, patient health and treatment decision-making are heterogenous. Treatment decisions are impacted by chronological age, performance status, and comorbidities. An approach to treating older adults based on objective measures of health may better personalize therapy by focusing on physiologic parameters. Here we examined the relationship of a blood-based aging biomarker, p16 INK4a (p16), with clinical frailty. Methods: Newly diagnosed patients with HM were enrolled in a prospective study capturing fitness measures and peripheral blood T lymphocyte (PBTL) p16 levels pre-treatment. Clinical phenotypes of fitness included 11 measures: ECOG performance status, Patient-Reported Outcome Measurement Information System (PROMIS) Global Health Scale Short Form v1, Older Americans Resources and Services (OARS) instrumental activities of daily living (IADL), Medical Outcomes Study (MOS) physical function, patient-reported Karnofsky Performance Status (KPS), social activity and social support (MOS) scores, Mental Health Index (MHI), and cognition (Blessed Orientation-Memory-Concentration). Physical function was measured using the Short Physical Performance Battery (SPPB). PBTL p16 was measured using the OSU_Senescence Nanostring CodeSet, which includes 74 additional T cell markers and five controls. PBTL were also examined for DNA methylation. PBTL were extracted, bisulfite converted, and methylation was quantified using the Illumina 850K EPIC methylation array at the TruDiagnostic core laboratory. Raw data was processed using the minfi pipeline, followed by normalization, batch correction, and KNN-based imputation using the GMQN and impute R packages. Principal-component (PC) PhenoAge measures were calculated using the processed methylation values. Results: Patients newly diagnosed with HM (n=33) (acute leukemia n=13, 39.5%, plasma cell dyscrasia n=13, 39.5%, lymphoma n=4, 12.1%, chronic lymphocytic leukemia=3, 9.1%) and complete p16 analysis with geriatric deficits were included. The mean p16 score was 32.6 (range 1.7-259.7). We quantified a composite deficit index defined as a frailty score higher than the third quartile using the aggregate score of the above fitness measures (range 0 to 11). Using a threshold for p16 ≥31.9, the model had 80% sensitivity and 79% specificity to identify frailty. The positive predictive value was 0.40, the negative predictive value was 0.96, and the accuracy was 0.76. The area under the ROC curve (AUC) for frailty versus p16 was 0.725. PBTL p16 was also explored in relation to other biomarkers of interest including epigenetic age (n=50). PBTL p16 correlated with PhenoAge r=0.38, p=0.0067. Conclusion: Aging biomarkers provide a novel means to quantify physiologic age in newly diagnosed HM. PBTL p16 levels showed a significant relationship with a composite metric of clinical fitness and accelerated aging in patients with different HM. PBTL p16 was both a sensitive and specific blood-based biomarker to identify clinical frailty in this preliminary analysis.

30 Exploring the Differential Importance of Modifiable Fitness Variables on Cognitive Performance in Older Adults

Objective:To identify the relative contributions and importance of modifiable fitness and demographic variables to cognitive performance in a cohort of healthy older adults.Participants and Methods:Metrics of modifiable fitness (gait speed, respiratory function, grip strength, and body mass index (BMI)) and cognition (executive function, episodic memory, and processing speed) were assessed in 619 older adults from the Health and Retirement Study 2016 wave (mean age = 74.9, sd = 6.9; mean education = 13.4 years, sd = 2.6; 42% female). General linear models were employed to assess the contribution of modifiable fitness variables in predicting three domains of cognition: executive function, episodic memory, and processing speed. Demographics (age, sex, education, time between appointments, and a chronic disease score) were entered as covariates for each model. Relative importance metrics were computed for all variables in each model using Lindeman, Merenda, and Gold (lmg) analysis, a technique which decomposes a given model’s explained variance to describe the average contribution of each predictor variable, independent of its position in the linear model.Results:When all variables were entered into the general linear model, demographic and modifiable fitness variables explained 35%, 24%, and 26% of the variance in executive function, episodic memory, and processing speed, respectively (all three models were significant, p <0.001). Age, education, respiratory function, and walking speed had higher relative importance values (all lmgs > 1.8) compared to BMI, grip strength, and other covariates in all three models (all lmgs < 1.3). Gender was also relatively important in the executive function (lmg = 4.2) and episodic memory models (lmg = 5.0). Of the modifiable fitness variables, walking speed and respiratory function had the greatest lmg values (5.8 and 6.4 respectively) in the executive function model, similar to demographic variables age (lmg = 6.0) and education (lmg = 8.9). When demographic variables were entered as covariates, modifiable fitness variables collectively accounted for an additional 9.7%, 6.3%, and 6.0% variance in the executive function, episodic memory, and processing speed models respectively (all three models were significant, p <0.001).Conclusions:Our findings indicate that walking speed and respiratory function are of similar importance compared to “traditional” demographic variables such as age and education in predicting cognitive performance in a cohort of healthy older adults. Moreover, modifiable fitness variables accounted for unique variance in executive function, episodic memory, and processing speed after accounting for age and education. Modifiable fitness variables explained the most unique variance in executive function. These results extend the current literature by demonstrating that modifiable fitness variables, even when assessed with brief and relatively coarse measures of physical performance, may be useful in predicting cognitive function. Moreover, the results highlight the need to assess metrics of cognitive reserve, such as education, as well as modifiable fitness variables and their respective roles in accounting for cognitive performance. The data further suggest that relative contributions of physical performance metrics may vary by cognitive domain in healthy older adults.

Evaluation of runoff estimation from GRACE coupled with different meteorological gridded products over the Upper Blue Nile Basin

Study RegionThe Upper Blue Nile (UBN) basin, Ethiopia. Study FocusIn efforts to accurately close the water balance equation for the UBN basin using remote sensing products, river runoff is calculated as a residual from the water balance equation by incorporating Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and remote sensing products for precipitation (P) and evapotranspiration (ET). The calculated river runoff is then compared to the gauge records located at the basin’s outlet. The best performing combination among the various combinations is chosen by aggregating rankings attributed to both error and linear fit metrics. The errors associated with each satellite product were assessed by forcing the In-Situ runoff to estimate the P, ET, and TWS. This methodology helps in addressing the uncertainty linked with each hydrological component. New Hydrological Insights for the RegionThe best P, ET, and TWS combination performance products to estimate runoff are SM2RAIN-CCI, GLEAM, and GRACE Spherical Harmonic products, respectively. The statistical results for the six metrics are R2 = 0.7, slope = 1.6, y-intercept = - 0.5 cm, RMSE = 3 cm, MAE = 2.8 cm, and PBIAS = 36%. The uncertainty from each hydrological component was quantified and showed that improving the accuracy of P and ET estimation is a crucial step to successfully close the water balance.

A hybrid fuzzy logic/genetic algorithm model based on experimental data for estimation of cuttings concentration during drilling

Adequate hole cleaning is a key to economic drilling operations. However, cuttings transport during drilling is affected by different operational parameters, geometric parameters and, cuttings and drilling fluid related parameters which makes the cuttings transport phenomenon a complicated modeling exercise. This complexity has led to the use of computational intelligence (CI) methods such as fuzzy logic and genetic algorithm (GA). A total of 11 parameters are considered in the current work while developing Mamdani type fuzzy logic model using 702 experimental observations. The developed FL model (FL2) is further combined with GA to optimize fuzzy rule weights which has led to the development of a hybrid CI model termed FL3 in this paper. The performance of FL3 over two test datasets is compared with FL2 and a previously published FL model (Chowdhury and Hovda, 2022) termed FL1 (built on 509 experimental observations and 195 fuzzy rules). Model comparison using three goodness of fit metrics (R2, RMSE and MAE) shows that the hybrid CI model, FL3, outperforms both FL1 and FL2 over both test datasets. R2 value of FL3 over both test datasets is ‘very good’ following (Moriasi et al., 2007). The difference of FL3 cuttings concentration (Cc) estimates and the measured Cc values is less than 1% for more than two thirds of the experimental data for both test datasets collected at two different geographical locations by two different research groups – TUDRP and SINTEF. The reasons behind FL3 outperforming FL1 and FL2 are data randomization, modified fuzzy sets for eccentricity and drillstring rotation based on the exploratory data analysis published earlier (Chowdhury et al., 2023), use of more experimental data and the optimization of fuzzy rule weights by GA.

Surviving & Thriving; a healthy lifestyle app for new US firefighters: usability and pilot study protocol.

In the United States (US), new firefighters' fitness and health behaviors deteriorate rapidly after fire academy graduation. Over the long-term, this increases their risks for chronic diseases. This study protocol describes the proposed usability testing and pilot study of a newly designed and developed healthy lifestyle smartphone app, "Surviving & Thriving", tailored towards young US firefighters. "Surviving & Thriving" will provide interactive educational content on four lifestyle factors; nutrition, sleep, physical activity, and resilience, and include a personalized journey, habit tracker, and elements of gamification to promote engagement and long-term healthy behavior change. The first phase of the app development entails alpha testing by the research team and pre-beta testing by a fire service expert panel which will help refine the app into a pre-consumer version. Upon completion of the full app prototype, beta 'usability' testing will be conducted among new fire academy graduates from two New England fire academies to collect qualitative and quantitative feedback via focus groups and satisfaction surveys, respectively. A last phase of piloting the app will evaluate the app's efficacy at maintaining/improving healthy lifestyle behaviors, mental health metrics, and physical fitness metrics. We will also evaluate whether firefighters' perceived "health cultures" scores (ratings of each fire station's/fire department's environments as to encouraging/discouraging healthy behaviors) modify the changes in health metrics after utilizing the app for three to six months. This novel user-friendly app seeks to help new firefighters maintain/improve their health and fitness more effectively, reducing their risk of lifestyle-related chronic disease. Firefighters who can establish healthy habits early in their careers are more likely to sustain them throughout their lives.

Curiosity Creates Diversity in Policy Search

When searching for policies, reward-sparse environments often lack sufficient information about which behaviors to improve upon or avoid. In such environments, the policy search process is bound to blindly search for reward-yielding transitions and no early reward can bias this search in one direction or another. A way to overcome this is to use intrinsic motivation in order to explore new transitions until a reward is found. In this work, we use a recently proposed definition of intrinsic motivation, Curiosity, in an evolutionary policy search method. We propose Curiosity-ES, 1 an evolutionary strategy adapted to use Curiosity as a fitness metric. We compare Curiosity-ES with other evolutionary algorithms intended for exploration, as well as with Curiosity-based reinforcement learning, and find that Curiosity-ES can generate higher diversity without the need for an explicit diversity criterion and leads to more policies which find reward.

Comparison of child and ATD belt fit and posture on belt-positioning boosters during self-selected, holding device, and nominal conditions

ObjectivePediatric anthropomorphic test devices (ATDs) are important tools for the assessment of child occupant protection and should represent realistic child belt fit and posture on belt-positioning boosters. Previous comparisons have been made to children in either self-selected or nominal postural conditions. This study compares belt fit and postural measurements between pediatric ATDs and a single cohort of children assuming different postures on boosters: self-selected, holding a portable electronic device, and nominal. MethodsA cohort of children (n = 25) were evaluated in a stationary vehicle on five boosters and in three postural conditions: nominal, self-selected, and a representative holding electronic device position. The Hybrid III 6- and 10-year-old and Q-Series 6- and 10-year-old ATDs were evaluated in the same five boosters and in two postural conditions: nominal and a representative holding electronic device position. A 3D coordinate measurement device was used to quantify belt fit (shoulder belt score, lap belt score, maximum gap size, and gap length) and anatomic landmark positions (head, suprasternale, ASIS, and patella). Landmark positions and belt fit were compared between ATDs and children for each booster and postural condition, and Pearson correlations (r) were assessed across boosters. ResultsATDs generally represented Nominal child postures across boosters. In the Device condition, ATDs were seldom able to be positioned to represent both the torso and head position of children, due to limited ATD spinal flexibility. When the torso position was matched, the ATD head was more rear by 63 mm. Correlations between Nominal child and ATD belt fit and belt gap metrics were generally weak and not significant, with the exception of lap belt score (all ATDs p < 0.07, r = 0.8549–0.9857). DiscussionATDs were generally able to represent realistic child postures and lap belt fit in Nominal and short duration Self-selected postures in a laboratory setting. However, these results display the potential difficulty of utilizing ATDs to represent more naturalistic child postures, especially the more forward head positions and flexed spinal posture associated with utilizing a portable electronic device.

Total Force Fitness Standardization: Leveraging Policy and Metrics to Inform and Accelerate Implementation.

Total Force Fitness (TFF) metrics inform leaders at every level as they develop and evaluate policies, practices, and programs that enable soldiers, airmen, sailors, marines, guardians, and operators to achieve human performance optimization in all eight domains as defined by the TFF framework. The Standardization Workgroup was a facilitated discussion breakout session among a group of uniquely informed and engaged attendees who were invited to be part of the TFF summit that was hosted in Bethesda, Maryland, on September 14 and 15, 2021. The Standardization Workgroup explored the 2018 TFF shift in perspective to consider human performance optimization in the context of a military unit's fitness for missions and identified several challenges, opportunities, and action items for improving the implementation and sustainment of TFF throughout the services. Key opportunities and recommendations synthesized from the workgroup discussion include the following: Creating true ownership of TFF by an entity with authority and engagement across the services; creating divisions of labor and assigning champion organizations with distinct areas of responsibility related to metric development, data accessibility, line-leader decision support, and data-driven policy and program review; leveraging and expanding the use of data repositories, secure data clouds, and artificial intelligence platforms to support a robust and integrated learning health and performance system; and prioritizing the operational line-leader stakeholder engagement at every stage of metric development and refinement as they are ultimately responsible for and have direct influence over the fitness of the individual service member and readiness to deploy of the military unit.

A new climate externalities food knowledge test validated by item response theory and behavioural data prediction (Validación de una nueva prueba de conocimientos sobre la externalidad climática de los alimentos mediante la teoría de respuesta al ítem y la predicción a partir de datos conductuales)

ABSTRACT Research underscores the imperative for global dietary shifts to curb global temperature rise within the Paris Agreement’s 2°C limit. Understanding human dietary behaviour is crucial for effective environmental policies promoting pro-environmental actions. A significant obstacle is the lack of awareness regarding the CO2 implications of meat and dairy consumption, hindering sustainable diet adoption. Thus, evaluating knowledge-based interventions requires reliable tools to quantify climate impact knowledge related to food consumption. Unfortunately, existing pro-environmental knowledge measurement instruments lack domain specificity, leaving a void in assessing climate-conscious food choice knowledge. Addressing this void, our climate externalities food knowledge test was developed. It underwent calibration via item response theory with a German university student sample and validation across one German and two Chilean student samples. The resulting scale exhibited a strong approximate model fit, acceptable item fit metrics, good reliability and the ability to predict climate-relevant food-choice patterns and expert group performance. However, notable differential item functioning was observed based on cultural context. Our instrument provides a valuable contribution to understanding and promoting sustainable food choices on a global scale.

Metrics Of Strength, Fitness, And Rotc Year Associate With Army Combat Fitness Test Performance

Metrics Of Strength, Fitness, And Rotc Year Associate With Army Combat Fitness Test Performance

An Unsupervised Machine Learning Approach for Monitoring Data Fusion and Health Indicator Construction.

The prediction of system degradation is very important as it serves as an important basis for the formulation of condition-based maintenance strategies. An effective health indicator (HI) plays a key role in the prediction of system degradation as it enables vital information for critical tasks ranging from fault diagnosis to remaining useful life prediction. To address this issue, a method for monitoring data fusion and health indicator construction based on an autoencoder (AE) and a long short-term memory (LSTM) network is proposed in this study to improve the predictability and effectiveness of health indicators. Firstly, an unsupervised method and overall framework for HI construction is built based on a deep autoencoder and an LSTM neural network. The neural network is trained fully based on the normal operating monitoring data and then the construction error of the AE model is adopted as the health indicator of the system. Secondly, we propose related machine learning techniques for monitoring data processing to overcome the issue of data fusion, such as mutual information for sensor selection and t-distributed stochastic neighbor embedding (T-SNE) for operating condition identification. Thirdly, in order to verify the performance of the proposed method, experiments are conducted based on the CMAPSS dataset and results are compared with algorithms of principal component analysis (PCA) and a vanilla autoencoder model. Result shows that the LSTM-AE model outperforms the PCA and Vanilla-AE model in the metrics of monotonicity, trendability, prognosability, and fitness. Fourthly, in order to analyze the impact of the time step of the LSMT-AE model on HI construction, we construct and analyze the system HI curve under different time steps of 5, 10, 15, 20, and 25 cycles. Finally, the results demonstrate that the proposed method for HI construction can effectively characterize the health state of a system, which is helpful for the development of further failure prognostics and converting the scheduled maintenance into condition-based maintenance.

Climate change outpaces adaptive potential via hybridization in nesting female Saltmarsh and Nelson’s Sparrows

Abstract Hybridization and introgression can promote adaptive potential and evolutionary resilience in response to increased pressures of climate change; they can also disrupt local adaptation and lead to outbreeding depression. We investigated female fitness consequences of hybridization in two sister species that are endemic to a threatened tidal marsh ecosystem: Saltmarsh (Ammospiza caudacutus) and Nelson’s (Ammospiza nelsoni) sparrows. We found increasing nest flooding rates due to rising sea levels are outpacing potential adaptive benefits of hybridization due to very low overall nesting success in both the Nelson’s and Saltmarsh sparrows. In the center of the hybrid zone across two years, we determined the success of 201 nests of 104 pure and admixed Saltmarsh and Nelson’s Sparrow females, genotyped using a panel of single nucleotide polymorphisms (SNPs) from double digest restriction-site associated DNA (ddRAD) sequencing. We evaluated 5 metrics of female fitness and modeled nesting success in relation to genotypic, environmental, and nesting characteristics. We found differential fitness among Saltmarsh, Nelson’s, and hybrid females, such that birds with predominantly Saltmarsh Sparrow alleles had higher reproductive success than birds with predominantly Nelson’s Sparrows alleles, and hybrids were intermediate. Fledging success increased with two known tidal marsh nesting adaptations: nest height and nesting synchrony with tidal cycles. We found a positive relationship between hybrid index and fitness in daily nest survival in 2016, but not in 2017, likely due to differing levels of precipitation and nest flooding between years. The strongest and most consistent predictors of daily nest survival were nesting synchrony with lunar tidal flooding cycles and daily maximum tide height. Fitness patterns suggest that there may be an adaptive benefit of interspecific geneflow for the Nelson’s Sparrow at the detriment of the Saltmarsh Sparrow; however, flooding rates are so high in many years they mask any fitness differences between the species, and all females had poor nesting success, regardless of genetic makeup.

Intraspecific variation in leaf litter alters fitness metrics and the gut microbiome of consumers.

Biodiversity can have cascading effects throughout ecosystems. While these effects are better understood at coarser taxonomic scales of biodiversity, there has been a resurgence in investigating how biodiversity within species may have cascading effects on communities and ecosystems. We investigate the broader trophic implications of intraspecific variation in the riparian tree, Alnus rubra, where immediately local or 'home' litter decomposes faster than 'away' litter in aquatic and terrestrial systems. With climate change shifting the distributions of plants across the globe, it is essential to understand how shifts in the intraspecific traits of leaf litter may have reverberating effects throughout ecosystems. Here, we find that intraspecific variation in leaf litter has fitness implications for invertebrate consumers, including the algivorous Dicosmoecus and detrivorous Psychoglypha caddisflies, which exhibited increased body size and muscle nitrogen content when incubated within in-situ river mesocosms supplied with local A. rubra litter. Litter source altered caddisfly gut microbiomes by increasing relative abundance of methanogens and methanotrophs among the non-local treatment group. Additionally, Dicosmoecus supplied with non-local litter may have shifted their diet towards a higher proportion of algae, as inferred from shifts in gut microbiome composition and isotopic ratios of muscle tissue. Overall, our study demonstrates that shifting distributions of plant genotypes across the globe may cause plant-microbe mismatches that will disrupt patterns of decomposition and may have consequences on the fitness and foraging behavior of consumers.

Examining cross-sectional and longitudinal relationships between multidomain physical fitness metrics, education, and cognition in Black older adults

ABSTRACT A limited number of studies examine cognitive aging in Black or African American older adults. The purpose of this study was to explore the relationship between health-related fitness metrics, education, and cognition at baseline and over a 4-year follow-up in a sample of 321 Black or African American older adults in the Health and Retirement Study (HRS). Physical fitness was assessed with measures of gait speed, peak expiratory flow, grip strength, and body mass index. Global cognition was assessed with an adapted version of the Telephone Interview for Cognitive Status (TICS). Analyses of relative importance and hierarchical multiple regression were used to examine baseline cross-sectional relationships. Multiple logistic regression was used to examine prospective relationships with longitudinal cognitive status. Education was the strongest predictor of global cognition at baseline and follow-up. More years of education significantly increased the odds of maintaining cognitive status at 4-year follow-up. After accounting for education, gait speed was independently associated with baseline cognitive performance and accounted for additional variance. Grip strength, peak expiratory flow, and body mass index were not significantly associated with cognition. The results indicated that modifiable variables, including years of educational attainment and gait speed, were more strongly associated with global cognition than other modifiable variables including body mass index, grip strength, and peak expiratory flow. The lack of observed associations between other fitness variables and cognition may be attributable to the brief assessment methods implemented, which was necessitated by the large-scale, epidemiological approach of the HRS.

Evaluating the interactive effects of artificial light at night and background color on tadpole crypsis, background adaptation efficacy, and growth

Artificial light at night (ALAN) is a global pollutant of rising concern. While alterations to natural day-night cycles caused by ALAN can affect a variety of traits, the broader fitness and ecological implications of these ALAN-induced shifts remain unclear. This study evaluated the interactive effects of ALAN and background color on traits that have important implications for predator-prey interactions and fitness: crypsis, background adaptation efficacy, and growth. Using three amphibian species as our models, we discovered that: (1) Exposure to ALAN reduced the ability for some species to match their backgrounds (background adaptation efficacy), (2) Crypsis and background adaptation efficacy were enhanced when tadpoles were exposed to dark backgrounds only, emphasizing the importance of environmental context when evaluating the effects of ALAN, (3) ALAN and background color have a combined effect on a common metric of fitness (growth), and (4) Effects of ALAN were not generalizable across amphibian species, supporting calls for more studies that utilize a diversity of species. Notably, to our knowledge, we found the first evidence that ALAN can diminish background adaptation efficacy in an amphibian species (American toad tadpoles). Collectively, our study joins others in highlighting the complex effects of ALAN on wildlife and underscores the challenges of generalizing ALAN's effect across species, emphasizing the need for a greater diversity of species and approaches used in ALAN research.

Effect of naturalistic seating postures and seatbelt routing on booster-seated Q6 ATD kinematics and kinetics in frontal impacts

Research questions / objectiveTest protocols evaluate restraint performance with pediatric ATDs placed in an ideal seating posture. However, real-world evidence suggests that ideal test conditions do not always reflect actual occupant positions. Prior studies have also shown that booster seat designs affect the position of the seatbelt around the child. Occupants in naturalistic seating postures, coupled with potentially unfavorable seatbelt positions, could result in adverse kinematics and kinetics in a crash. Therefore, the aim of this study was to quantify the effect of different naturalistic seating postures on the response of the Q6 ATD restrained on boosters with varying initial static belt fit in a frontal impact. Methods/data sourcesThe Q6 ATD was positioned on two booster seats of similar design but varying static belt fit metrics in three seating postures: reference, leaning forward, and leaning inboard. These booster seats were chosen from extensive belt fit studies on human volunteers and ATDs, and were defined as follows:•Booster A: more inboard shoulder belt position, more inferior lap belt position, larger gap size.•Booster B: more outboard shoulder belt position, more superior lap belt position, smaller gap size.The booster-seated ATD was restrained on the simulated Consumer Reports test buck (2010 Ford Flex 2nd row seat) with a front blocker plate using a 3-point lap-shoulder belt with a retractor and pretensioner. The sled environment was subjected to the FMVSS 213 frontal impact pulse, and each booster and seating posture was evaluated twice (n = 12 sled tests). Kinematic and kinetic measures were recorded. A linear regression analysis was conducted across postures on each booster. Further, a paired t-test analysis was conducted across booster seats for each seating posture. ResultsAcross seating postures, the reference posture exhibited similar or higher kinematic and kinetic metric values compared to the leaning forward and leaning inboard postures on both boosters. However, both leaning forward (Booster A = 279.5 ± 21.6 mm; Booster B = 298.8 ± 1.5 mm) and leaning inboard (Booster A = 308.7 ± 1.1 mm; Booster B = 331.4 ± 8.5 mm) postures generally resulted in greater head excursion than the reference posture (Booster A = 285.0 ± 16.9 mm; Booster B = 288.1 ± 1.5 mm), indicating greater potential for head contact.Between boosters, Booster A resulted in significantly lower head 3 ms clip acceleration (p = 0.0026), HIC15 (p = 0.0008), upper neck tensile force (Fz)(p = 0.0057), chest 3 ms clip acceleration (p = 0.0013), and right abdominal pressure (p = 0.0163), and significantly higher left ASIS force (Fx)(p = 0.0150) and left (p = 0.0489) and right (p = 0.0088) ASIS moment (My) than Booster B. Upper neck tensile forces on Booster B crossed the 20% and 50% thresholds for AIS3 + injury. Lower abdominal pressure and higher ASIS forces and moments on Booster A suggest that the lap belt loaded the ASIS appropriately, and hence, relatively better kinematics than Booster B. Significance of resultsThis study shows that booster design affects static belt fit which can have an effect on dynamic crash performance and assessment criteria. By connecting static belt fit to dynamic performance, these effects may have the potential to help guide booster seat design.