Global latitudinal gradients and the evolution of body size in dinosaurs and mammals.

Global climate patterns fundamentally shape the distribution of species and ecosystems. For example, Bergmann's rule predicts that homeothermic animals, including birds and mammals, inhabiting cooler climates are generally larger than close relatives from warmer climates. The modern world, however, lacks the comparative data needed to evaluate such macroecological rules rigorously. Here, we test for Bergmann's rule in Mesozoic dinosaurs and mammaliaforms that radiated within relatively temperate global climate regimes. We develop a phylogenetic model that accounts for biases in the fossil record and allows for variable evolutionary dispersal rates. Our analysis also includes new fossil data from the extreme high-latitude Late Cretaceous Arctic Prince Creek Formation. We find no evidence for Bergmann's rule in Mesozoic dinosaurs or mammaliaforms, the ancestors of extant homeothermic birds and mammals. When our model is applied to thousands of extant dinosaur (bird) and mammal species, we find that body size evolution remains independent of latitude. A modest temperature effect is found in extant, but not in Mesozoic, birds, suggesting that body size evolution in modern birds was influenced by Bergmann's rule during Cenozoic climatic change. Our study provides a general approach for studying macroecological rules, highlighting the fossil record's power to address longstanding ecological principles.

The evolution and ecology of gigantism in terror birds (Aves, Phorusrhacidae).

Terror birds (Aves, Phorusrhacidae) were large flightless apex predators in South America during the Cenozoic. Here, we estimate a new phylogeny for phorusrhacids using Bayesian inference. We demonstrate phylogenetic evidence for a monophyletic Patagornithinae and find significant support for a distinct crown group associated with the quintessential 'terror bird' characteristics. We use this phylogeny to analyse the evolution of body size and cursoriality. Our results reveal that size overlap was rare between co-occurring subfamilies, supporting the hypothesis that these traits were important for niche partitioning. We observe that gigantism evolved in a single clade, containing Phorusrhacinae and Physornithinae. The members of this lineage were consistently larger than all other phorusrhacids. Phorusrhacinae emerged following the extinction of Physornithinae, suggesting the ecological succession of the apex predator niche. The first known phorusrhacine, Phorusrhacos longissimus, was gigantic but significantly smaller and more cursorial than any physornithine. These traits likely evolved in response to the expansion of open environments. Following the Santacrucian SALMA, phorusrhacines increased in size, further converging on the morphology of Physornithinae. These findings suggest that the evolution and displacement of body size drove terror bird niche partitioning and competitive exclusion controlled phorusrhacid diversity.

Active Mutual Conjoint Estimation of Multiple Contrast Sensitivity Functions.

Recent advances in nonparametric Contrast Sensitivity Function (CSF) estimation have yielded a new tradeoff between accuracy and efficiency not available to classical parametric estimators. An additional advantage of this new framework is the ability to independently tune multiple aspects of the estimator to seek further improvements. Machine Learning CSF (MLCSF) estimation with Gaussian processes allows for design optimization in the kernel, acquisition function and underlying task representation, to name a few. This paper describes a novel kernel for CSF estimation that is more flexible than a kernel based on strictly functional forms. Despite being more flexible, it can result in a more efficient estimator. Further, trial selection for data acquisition that is generalized beyond pure information gain can also improve estimator quality. Finally, introducing latent variable representations underlying general CSF shapes can enable simultaneous estimation of multiple CSFs, such as from different eyes, eccentricities or luminances. The conditions under which the new procedures perform better than previous nonparametric estimation procedures are presented and quantified.

Contrast response function estimation with nonparametric Bayesian active learning.

Multidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning tools can be leveraged to provide an adjustable balance between accuracy and efficiency. Contrast sensitivity functions (CSFs) are behaviorally estimated curves that provide insight into both peripheral and central visual function. Because estimation can be impractically long, current clinical workflows must make compromises such as limited sampling across spatial frequency or strong assumptions on CSF shape. This article describes the development of the machine learning contrast response function (MLCRF) estimator, which quantifies the expected probability of success in performing a contrast detection or discrimination task. A machine learning CSF can then be derived from the MLCRF. Using simulated eyes created from canonical CSF curves and actual human contrast response data, the accuracy and efficiency of the machine learning contrast sensitivity function (MLCSF) was evaluated to determine its potential utility for research and clinical applications. With stimuli selected randomly, the MLCSF estimator converged slowly toward ground truth. With optimal stimulus selection via Bayesian active learning, convergence was nearly an order of magnitude faster, requiring only tens of stimuli to achieve reasonable estimates. Inclusion of an informative prior provided no consistent advantage to the estimator as configured. MLCSF achieved efficiencies on par with quickCSF, a conventional parametric estimator, but with systematically higher accuracy. Because MLCSF design allows accuracy to be traded off against efficiency, it should be explored further to uncover its full potential.

The coevolution of rostral keratin and tooth distribution in dinosaurs.

Teeth evolved early in vertebrate evolution, and their morphology reflects important specializations in diet and ecology among species. The toothless jaws (edentulism) in extant birds likely coevolved with beak keratin, which functionally replaced teeth. However, extinct dinosaurs lost teeth multiple times independently and exhibited great variation in toothrow distribution and rhamphotheca-like keratin structures. Here, we use rostral jawbone surface texture as a proxy for rostral keratin covering and phylogenetic comparative models to test for the influence of rostral keratin on toothrow distribution in Mesozoic dinosaurs. We find that the evolution of rostral keratin covering explains partial toothrow reduction but not jaw toothlessness. Toothrow reduction preceded the evolution of rostral keratin cover in theropods. Non-theropod dinosaurs evolved continuous toothrows despite evolving rostral keratin covers (e.g. some ornithischians and sauropodomorphs). We also show that rostral keratin covers did not significantly increase the evolutionary rate of tooth loss, which further delineates the antagonistic relationship between these structures. Our results suggest that the evolution of rostral keratin had a limited effect on suppressing tooth development. Independent changes in jaw development may have facilitated further tooth loss. Furthermore, the evolution of strong chemical digestion, a gizzard, and a dietary shift to omnivory or herbivory likely alleviated selective pressures for tooth development.

Filling in the white space: Spatial interpolation with Gaussian processes and social media data.

Full national coverage below the state level is difficult to attain through survey-based data collection. Even the largest survey-based data collections, such as the CDC's Behavioral Risk Factor Surveillance System or the Gallup-Healthways Well-being Index (both with more than 300,000 responses p.a.) only allow for the estimation of annual averages for about 260 out of roughly U.S. 3,000 counties when a threshold of 300 responses per county is used. Using a relatively high threshold of 300 responses gives substantially higher convergent validity-higher correlations with health variables-than lower thresholds but covers a reduced and biased sample of the population. We present principled methods to interpolate spatial estimates and show that including large-scale geotagged social media data can increase interpolation accuracy. In this work, we focus on Gallup-reported life satisfaction, a widely-used measure of subjective well-being. We use Gaussian Processes (GP), a formal Bayesian model, to interpolate life satisfaction, which we optimally combine with estimates from low-count data. We interpolate over several spaces (geographic and socioeconomic) and extend these evaluations to the space created by variables encoding language frequencies of approximately 6 million geotagged Twitter users. We find that Twitter language use can serve as a rough aggregate measure of socioeconomic and cultural similarity, and improves upon estimates derived from a wide variety of socioeconomic, demographic, and geographic similarity measures. We show that applying Gaussian Processes to the limited Gallup data allows us to generate estimates for a much larger number of counties while maintaining the same level of convergent validity with external criteria (i.e., N = 1,133 vs. 2,954 counties). This work suggests that spatial coverage of psychological variables can be reliably extended through Bayesian techniques while maintaining out-of-sample prediction accuracy and that Twitter language adds important information about cultural similarity over and above traditional socio-demographic and geographic similarity measures. Finally, to facilitate the adoption of these methods, we have also open-sourced an online tool that researchers can freely use to interpolate their data across geographies.

Contrast Response Function Estimation with Nonparametric Bayesian Active Learning.

Multidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning tools can be leveraged to provide an adjustable balance between accuracy and efficiency. Contrast Sensitivity Functions (CSFs) are behaviorally estimated curves that provide insight into both peripheral and central visual function. Because estimation can be impractically long, current clinical workflows must make compromises such as limited sampling across spatial frequency or strong assumptions on CSF shape. This paper describes the development of the Machine Learning Contrast Response Function (MLCRF) estimator, which quantifies the expected probability of success in performing a contrast detection or discrimination task. A machine learning CSF can then be derived from the MLCRF. Using simulated eyes created from canonical CSF curves and actual human contrast response data, the accuracy and efficiency of the MLCSF was evaluated in order to determine its potential utility for research and clinical applications. With stimuli selected randomly, the MLCSF estimator converged slowly toward ground truth. With optimal stimulus selection via Bayesian active learning, convergence was nearly an order of magnitude faster, requiring only tens of stimuli to achieve reasonable estimates. Inclusion of an informative prior provided no consistent advantage to the estimator as configured. MLCSF achieved efficiencies on par with quickCSF, a conventional parametric estimator, but with systematically higher accuracy. Because MLCSF design allows accuracy to be traded off against efficiency, it should be explored further to uncover its full potential. Precis: Machine learning classifiers enable accurate and efficient contrast sensitivity function estimation with item-level prediction for individual eyes.

wenda_gpu: fast domain adaptation for genomic data.

MOTIVATION: Domain adaptation allows for the development of predictive models even in cases with limited sample data. Weighted elastic net domain adaptation specifically leverages features of genomic data to maximize transferability but the method is too computationally demanding to apply to many genome-sized datasets. RESULTS: We developed wenda_gpu, which uses GPyTorch to train models on genomic data within hours on a single GPU-enabled machine. We show that wenda_gpu returns comparable results to the original wenda implementation, and that it can be used for improved prediction of cancer mutation status on small sample sizes than regular elastic net. AVAILABILITY AND IMPLEMENTATION: wenda_gpu is available on GitHub at https://github.com/greenelab/wenda_gpu/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ancient Mitogenomes Reveal the Origins and Genetic Structure of the Neolithic Shimao Population in Northern China.

Shimao City is considered an important political and religious center during the Late Neolithic Longshan period of the Middle Yellow River basin. The genetic history and population dynamics among the Shimao and other ancient populations, especially the Taosi-related populations, remain unknown. Here, we sequenced 172 complete mitochondrial genomes, ranging from the Yangshao to Longshan period, from individuals related to the Shimao culture in northern Shaanxi Province and Taosi culture in southern Shanxi Province, Middle Yellow River basin. Our results show that the populations inhabiting Shimao City had close genetic connections with an earlier population in the Middle Neolithic Yangshao period of northern Shaanxi Province, revealing a mostly local origin for the Shimao Society. In addition, among the populations in other regions of the Yellow River basin, the Shimao-related populations had the closest maternal affinity with the contemporaneous Taosi populations from the Longshan period. The Shimao-related populations also shared more affinity with present-day northern Han populations than with the minorities and southern Han in China. Our study provides a new perspective on the genetic origins and structure of the Shimao people and the population dynamics in the Middle Yellow River basin during the Neolithic period.

Ancient DNA reveals the maternal genetic history of East Asian domestic pigs.

Zoo-archaeological and genetic evidence suggest that pigs were domesticated independently in Central China and Eastern Anatolia along with the development of agricultural communities and civilizations. However, the genetic history of domestic pigs, especially in China, has not been fully explored. In this study, we generate 42 complete mitochondrial DNA sequences from ∼7500- to 2750-year-old individuals from the Yellow River basin. Our results show that the maternal genetic continuity of East Asian domestic pigs dates back to at least the Early to Middle Neolithic. In contrast, the Near Eastern ancestry in European domestic pigs saw a near-complete genomic replacement by the European wild boar. The majority of East Asian domestic pigs share close haplotypes, and the most recent common ancestor of most branches dates back to less than 20,000 years before present, inferred using new substitution rates of whole mitogenomes or combined protein-coding regions. Two major population expansion events of East Asian domestic pigs coincided with changes in climate, widespread adoption of introduced crops, and the development of agrarian societies. These findings add to our understanding of the maternal genetic composition and help to complete the picture of domestic pig evolutionary history in East Asia.

Determining subpopulation methylation profiles from bisulfite sequencing data of heterogeneous samples using DXM.

Epigenetic changes, such as aberrant DNA methylation, contribute to cancer clonal expansion and disease progression. However, identifying subpopulation-level changes in a heterogeneous sample remains challenging. Thus, we have developed a computational approach, DXM, to deconvolve the methylation profiles of major allelic subpopulations from the bisulfite sequencing data of a heterogeneous sample. DXM does not require prior knowledge of the number of subpopulations or types of cells to expect. We benchmark DXM's performance and demonstrate improvement over existing methods. We further experimentally validate DXM predicted allelic subpopulation-methylation profiles in four Diffuse Large B-Cell Lymphomas (DLBCLs). Lastly, as proof-of-concept, we apply DXM to a cohort of 31 DLBCLs and relate allelic subpopulation methylation profiles to relapse. We thus demonstrate that DXM can robustly find allelic subpopulation methylation profiles that may contribute to disease progression using bisulfite sequencing data of any heterogeneous sample.

Ancient Xinjiang mitogenomes reveal intense admixture with high genetic diversity.

Xinjiang is a key region in northwestern China, connecting East and West Eurasian populations and cultures for thousands of years. To understand the genetic history of Xinjiang, we sequenced 237 complete ancient human mitochondrial genomes from the Bronze Age through Historical Era (41 archaeological sites). Overall, the Bronze Age Xinjiang populations show high diversity and regional genetic affinities with Steppe and northeastern Asian populations along with a deep ancient Siberian connection for the Tarim Basin Xiaohe individuals. In the Iron Age, in general, Steppe-related and northeastern Asian admixture intensified, with North and East Xinjiang populations showing more affinity with northeastern Asians and South Xinjiang populations showing more affinity with Central Asians. The genetic structure observed in the Historical Era of Xinjiang is similar to that in the Iron Age, demonstrating genetic continuity since the Iron Age with some additional genetic admixture with populations surrounding the Xinjiang region.

Evolutionary Sample Size and Consilience in Phylogenetic Comparative Analysis.

Phylogenetic comparative methods (PCMs) are commonly used to study evolution and adaptation. However, frequently used PCMs for discrete traits mishandle single evolutionary transitions. They erroneously detect correlated evolution in these situations. For example, hair and mammary glands cannot be said to have evolved in a correlated fashion because each evolved only once in mammals, but a commonly used model (Pagel's Discrete) statistically supports correlated (dependent) evolution. Using simulations, we find that rate parameter estimation, which is central for model selection, is poor in these scenarios due to small effective (evolutionary) sample sizes of independent character state change. Pagel's Discrete model also tends to favor dependent evolution in these scenarios, in part, because it forces evolution through state combinations unobserved in the tip data. This model prohibits simultaneous dual transitions along branches. Models with underlying continuous data distributions (e.g., Threshold and GLMM) are less prone to favor correlated evolution but are still susceptible when evolutionary sample sizes are small. We provide three general recommendations for researchers who encounter these common situations: i) create study designs that evaluate a priori hypotheses and maximize evolutionary sample sizes; ii) assess the suitability of evolutionary models-for discrete traits, we introduce the phylogenetic imbalance ratio; and iii) evaluate evolutionary hypotheses with a consilience of evidence from disparate fields, like biogeography and developmental biology. Consilience plays a central role in hypothesis testing within the historical sciences where experiments are difficult or impossible to conduct, such as many hypotheses about correlated evolution. These recommendations are useful for investigations that employ any type of PCM. [Class imbalance; consilience; correlated evolution; evolutionary sample size; phylogenetic comparative methods.].

The relationship between genome size and metabolic rate in extant vertebrates.

Genome size has long been hypothesized to affect the metabolic rate in various groups of animals. The mechanism behind this proposed association is the nucleotypic effect, in which large nucleus and cell sizes influence cellular metabolism through surface area-to-volume ratios. Here, we provide a review of the recent literature on the relationship between genome size and metabolic rate. We also conduct an analysis using phylogenetic comparative methods and a large sample of extant vertebrates. We find no evidence that the effect of genome size improves upon models in explaining metabolic rate variation. Not surprisingly, our results show a strong positive relationship between metabolic rate and body mass, as well as a substantial difference in metabolic rate between endothermic and ectothermic vertebrates, controlling for body mass. The presence of endothermy can also explain elevated rate shifts in metabolic rate whereas genome size cannot. We further find no evidence for a punctuated model of evolution for metabolic rate. Our results do not rule out the possibility that genome size affects cellular physiology in some tissues, but they are consistent with previous research suggesting little support for a direct functional connection between genome size and basal metabolic rate in extant vertebrates. This article is part of the theme issue 'Vertebrate palaeophysiology'.

Lower-limb joint reaction forces and moments during modified cycling in healthy controls and individuals with knee osteoarthritis.

BACKGROUND: Osteoarthritis (OA) is a clinical problem affecting an estimated 27 million adults in the United States, with the only clear treatment options being pain management. Cycling is an integral component of exercise for individuals with knee osteoarthritis, while the joint reaction forces during cycling remain unknown. METHODS: Thirteen subjects with medial compartment knee osteoarthritis and eleven healthy subjects performed a cycling protocol with a neutral pedal and four pedal modifications. Six hundred muscle-actuated inverse-dynamic simulations (24 subjects, 5 trials in each of 5 conditions) were performed to estimate joint reaction force differences between conditions. FINDINGS: Subjects with knee osteoarthritis had many significant changes among them was a reduction in knee adduction-abduction moment by 45% (5° lateral wedge), 77% (10° lateral wedge), 54% (5° toe-in) and 58% (10° toe-in). Conversely the healthy subjects had no significant changes in the knee adduction-abduction moment for the lateral wedge conditions and the 5° toe-in but did decrease by 18% for the 10° toe-in condition. When comparing the cohorts across the different pedal conditions, the data showed many significant differences among the groups. INTERPRETATION: This study showed that while cycling in different pedal modifications, the knee osteoarthritis subjects had more beneficial changes in their knee adduction-abduction moment compared to the healthy subjects with the lateral-wedge modification resulting in the greatest impact on the subjects with knee osteoarthritis. Both groups had greater contact forces at the hip and ankle across pedal modifications compared to neutral. For the knee, subjects with osteoarthritis mostly decreased their knee contact forces but the healthy subjects mostly increased these forces with all pedal modifications.

Is sarcopenia and sarcopenic obesity associated with clinical and pathological outcomes in patients undergoing radical nephroureterectomy?

PURPOSE: To investigate the association between sarcopenia and sarcopenic obesity on clinical, perioperative, and oncologic outcomes in patients with upper-tract urothelial carcinoma (UTUC) undergoing radical nephroureterectomy (RNU). METHODS: Retrospective review of our institutional UTUC database was performed to identify all patients who underwent radical nephroureterectomy from 2002-2016. Skeletal Muscle Index (SMI) was measured at the L3 vertebral level and standardized according to patient height (cm2/m2). Sarcopenia was defined as<55cm2/m2 for men and<39cm2/m2 for women. Sarcopenic obesity was also assessed in patients with BMI>30kg/m2. Unadjusted logistic regression and Wilcoxon rank sum tests examined the relationship between sarcopenia and variables. RESULTS: A total of 100 patients (66 men and 34 women) with a mean age of 68 years, BMI of 30, Charlson comorbidity index of 4.0, tumor size of 3.5, and SMI of 50.8cm2/m2 were included. Furthermore, 42 patients (42%) were sarcopenic, and 18 patients (18%) had sarcopenic obesity. Median EBL was 150ml, OR duration was 322 minutes, and length of stay was 5.0 days. Sarcopenia was associated with several clinical factors including decreasing BMI, male sex, and coronary artery disease, albeit without association with any perioperative or oncologic outcomes. Sarcopenic obesity was similarly associated with several clinical variables including male sex, diabetes mellitus, hyperlipidemia, as well as increased EBL (P = 0.047) and non-bladder cancer disease relapse (P = 0.049). CONCLUSIONS: This contemporary cohort of patients undergoing RNU highlights the association of nonmodifiable risk factors with sarcopenia and disease relapse with sarcopenic obesity. Larger studies are necessary to further validate these observations.

Intraoperative Frontal Alpha-Band Power Correlates with Preoperative Neurocognitive Function in Older Adults.

Each year over 16 million older Americans undergo general anesthesia for surgery, and up to 40% develop postoperative delirium and/or cognitive dysfunction (POCD). Delirium and POCD are each associated with decreased quality of life, early retirement, increased 1-year mortality, and long-term cognitive decline. Multiple investigators have thus suggested that anesthesia and surgery place severe stress on the aging brain, and that patients with less ability to withstand this stress will be at increased risk for developing postoperative delirium and POCD. Delirium and POCD risk are increased in patients with lower preoperative cognitive function, yet preoperative cognitive function is not routinely assessed, and no intraoperative physiological predictors have been found that correlate with lower preoperative cognitive function. Since general anesthesia causes alpha-band (8-12 Hz) electroencephalogram (EEG) power to decrease occipitally and increase frontally (known as "anteriorization"), and anesthetic-induced frontal alpha power is reduced in older adults, we hypothesized that lower intraoperative frontal alpha power might correlate with lower preoperative cognitive function. Here, we provide evidence that such a correlation exists, suggesting that lower intraoperative frontal alpha power could be used as a physiological marker to identify older adults with lower preoperative cognitive function. Lower intraoperative frontal alpha power could thus be used to target these at-risk patients for possible therapeutic interventions to help prevent postoperative delirium and POCD, or for increased postoperative monitoring and follow-up. More generally, these results suggest that understanding interindividual differences in how the brain responds to anesthetic drugs can be used as a probe of neurocognitive function (and dysfunction), and might be a useful measure of neurocognitive function in older adults.

Role of proteasome-dependent protein degradation in long-term operant memory in Aplysia.

We investigated the in vivo role of protein degradation during intermediate (ITM) and long-term memory (LTM) in Aplysia using an operant learning paradigm. The proteasome inhibitor MG-132 inhibited the induction and molecular consolidation of LTM with no effect on ITM. Remarkably, maintenance of steady-state protein levels through inhibition of protein synthesis using either anisomycin or rapamycin in conjunction with proteasome inhibition permitted the formation of robust 24 h LTM. Our studies suggest a primary role for proteasomal activity in facilitation of gene transcription for LTM and raise the possibility that synaptic mechanisms are sufficient to sustain 24 h memory.

Differential role of calpain-dependent protein cleavage in intermediate and long-term operant memory in Aplysia.

In addition to protein synthesis, protein degradation or protein cleavage may be necessary for intermediate (ITM) and long-term memory (LTM) to remove molecular constraints, facilitate persistent kinase activity and modulate synaptic plasticity. Calpains, a family of conserved calcium dependent cysteine proteases, modulate synaptic function through protein cleavage. We used the marine mollusk Aplysia californica to investigate the in vivo role of calpains during intermediate and long-term operant memory formation using the learning that food is inedible (LFI) paradigm. A single LFI training session, in which the animal associates a specific netted seaweed with the failure to swallow, generates short (30min), intermediate (4-6h) and long-term (24h) memory. Using the calpain inhibitors calpeptin and MDL-28170, we found that ITM requires calpain activity for induction and consolidation similar to the previously reported requirements for persistent protein kinase C activity in intermediate-term LFI memory. The induction of LTM also required calpain activity. In contrast to ITM, calpain activity was not necessary for the molecular consolidation of LTM. Surprisingly, six hours after LFI training we found that calpain activity was necessary for LTM, although this is a time at which neither persistent PKC activity nor protein synthesis is required for the maintenance of long-term LFI memory. These results demonstrate that calpains function in multiple roles in vivo during associative memory formation.

Acute effects of lateral shoe wedges on joint biomechanics of patients with medial compartment knee osteoarthritis during stationary cycling.

Cycling is commonly prescribed for individuals with knee osteoarthritis (OA) but very little biomechanical research exists on the topic. Individuals with OA may be at greater risk of OA progression or other knee injuries because of their altered knee kinematics. This study investigated the effects of lateral wedges on knee joint biomechanics and pain in patients with medial compartment knee OA during stationary cycling. Thirteen participants with OA and 11 paired healthy participants volunteered for this study. A motion analysis system and a customized instrumented pedal were used to collect 5 pedal cycles of kinematics and kinetics, respectively, during 2 minutes of cycling in 1 neutral and 2 lateral wedge (5° and 10°) conditions. Participants pedaled at 60 RPM and an 80W workrate and rated their knee pain on a visual analog scale during each minute of each condition. There was a 22% decrease in the internal knee abduction moment with the 10° wedge. However, this finding was not accompanied by a decrease in knee adduction angle or subjective pain. Additionally, there was an increase in vertical and horizontal pedal reaction force which may negate the advantages of the decreased internal knee abduction moment. For people with medial knee OA, cycling with 10° lateral wedges may not be sufficient to slow the progression of OA beyond the neutral riding condition.