scDecouple: decoupling cellular response from infected proportion bias in scCRISPR-seq.

Single-cell clustered regularly interspaced short palindromic repeats-sequencing (scCRISPR-seq) is an emerging high-throughput CRISPR screening technology where the true cellular response to perturbation is coupled with infected proportion bias of guide RNAs (gRNAs) across different cell clusters. The mixing of these effects introduces noise into scCRISPR-seq data analysis and thus obstacles to relevant studies. We developed scDecouple to decouple true cellular response of perturbation from the influence of infected proportion bias. scDecouple first models the distribution of gene expression profiles in perturbed cells and then iteratively finds the maximum likelihood of cell cluster proportions as well as the cellular response for each gRNA. We demonstrated its performance in a series of simulation experiments. By applying scDecouple to real scCRISPR-seq data, we found that scDecouple enhances the identification of biologically perturbation-related genes. scDecouple can benefit scCRISPR-seq data analysis, especially in the case of heterogeneous samples or complex gRNA libraries.

CoRegNet: unraveling gene co-regulation networks from public RNA-Seq repositories using a beta-binomial statistical model.

Millions of RNA sequencing samples have been deposited into public databases, providing a rich resource for biological research. These datasets encompass tens of thousands of experiments and offer comprehensive insights into human cellular regulation. However, a major challenge is how to integrate these experiments that acquired at different conditions. We propose a new statistical tool based on beta-binomial distributions that can construct robust gene co-regulation network (CoRegNet) across tens of thousands of experiments. Our analysis of over 12 000 experiments involving human tissues and cells shows that CoRegNet significantly outperforms existing gene co-expression-based methods. Although the majority of the genes are linearly co-regulated, we did discover an interesting set of genes that are non-linearly co-regulated; half of the time they change in the same direction and the other half they change in the opposite direction. Additionally, we identified a set of gene pairs that follows the Simpson's paradox. By utilizing public domain data, CoRegNet offers a powerful approach for identifying functionally related gene pairs, thereby revealing new biological insights.

m5c-iDeep: 5-Methylcytosine sites identification through deep learning.

5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventional laboratory methods do not provide quick reliable identification of m5c sites. However, the sequence data readiness has made it feasible to develop computationally intelligent models that optimize the identification process for accuracy and robustness. The present research focused on the development of in-silico methods built using deep learning models. The encoded data was then fed into deep learning models, which included gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM). After that, the models were subjected to a rigorous evaluation process that included both independent set testing and 10-fold cross validation. The results revealed that LSTM-based model, m5c-iDeep, outperformed revealing 99.9 % accuracy while comparing with existing m5c predictors. In order to facilitate researchers, m5c-iDeep was also deployed on a web-based server which is accessible at https://taseersuleman-m5c-ideep-m5c-ideep.streamlit.app/.

BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks.

BACKGROUND: Chemical bioproduction has attracted attention as a key technology in a decarbonized society. In computational design for chemical bioproduction, it is necessary to predict changes in metabolic fluxes when up-/down-regulating enzymatic reactions, that is, responses of the system to enzyme perturbations. Structural sensitivity analysis (SSA) was previously developed as a method to predict qualitative responses to enzyme perturbations on the basis of the structural information of the reaction network. However, the network structural information can sometimes be insufficient to predict qualitative responses unambiguously, which is a practical issue in bioproduction applications. To address this, in this study, we propose BayesianSSA, a Bayesian statistical model based on SSA. BayesianSSA extracts environmental information from perturbation datasets collected in environments of interest and integrates it into SSA predictions. RESULTS: We applied BayesianSSA to synthetic and real datasets of the central metabolic pathway of Escherichia coli. Our result demonstrates that BayesianSSA can successfully integrate environmental information extracted from perturbation data into SSA predictions. In addition, the posterior distribution estimated by BayesianSSA can be associated with the known pathway reported to enhance succinate export flux in previous studies. CONCLUSIONS: We believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field.

Analysis of Erythrocyte Parameters in Multiple and Long-Term Blood Donors from Northern Pomerania (Poland).

BACKGROUND/AIMS: Assessment of the levels of vital blood parameters in donors is essential to evaluate their health status, ensure their suitability for donation, preserve the integrity of the circulatory system, and facilitate comprehensive health monitoring. The aim of our study was to analyse the levels of haemoglobin, haematocrit, erythrocyte count, MCV, MCH, and MCHC in 12 groups of first-time donors and experienced donors of both sexes at the John Paul II Regional Blood Donation and Treatment Centre in Slupsk, northern Poland. The donors were divided into three age groups (18-30 years, 31-45 years, and 46-65 years). METHODS: Using MANOVA multivariate significance tests, we examined the main effects of donor-related factors (age, sex, donor stage) on morphological blood parameters to evaluate different haematological parameters, such as Hb, Ht, RBC, MCV, MCH, and MCHC, and identified statistically significant relationships between all variables. RESULTS: The multivariate analysis of these three main factors showed that the variation in haemoglobin (Hb) levels accounted for 46% of the explained dependence in this statistical model. In particular, approximately half of the variability in the multivariate statistical analysis was attributed to the role of Hb and haematocrit (Ht). In addition, the ß-coefficient values for Hb and Ht were statistically higher in relation to donor sex and donor type (single versus repeat). These ß-coefficient values from our data represent the strength and direction of the relationship between the haematological parameters (Hb and Ht) and the specific donor characteristics. A higher ß-coefficient indicates a stronger influence of donor sex and donor type on these parameters, suggesting that these factors contribute significantly to the variation in the Hb and Ht levels. Based on our results, the comprehensive analysis of the entire statistical model of metabolic biomarkers revealed the following hierarchy: Hb > Ht > MCHC > MCV > RBC > MCH. The results obtained showed strong statistical relationships, as indicated by the high values of the key statistical indicators in our analysis. The coefficient of determination (R²) showed that the model explained a significant proportion of the variance in the data, while the F-test statistic confirmed the significance of the predictors. CONCLUSION: These strong statistical dependencies provided a clear justification for selecting this model over others, as it effectively represented the underlying relationships within the data. These statistics help to assess how well the model matches the actual data, thereby helping to reduce the risks associated with blood donation, optimise donor safety, and maintain the quality and efficiency of blood transfusion services.

Full-dimensional coupled-channel statistical approach to atom-triatom systems and applications to H/D + O3 reaction.

The statistical quantum model (SQM), which assumes that the reactivity is controlled by entrance/exit channel quantum capture probabilities, is well suited for chemical reactions with a long-lived intermediate complex. In this work, a time-independent coupled-channel implementation of the SQM approach is developed for atom-triatom systems in full dimensionality. As SQM treats the capture dynamics quantum mechanically, it is capable of handling quantum effects such as tunneling. A detailed study of the H/D + O3 capture dynamics was performed by applying the newly developed SQM method on an accurate global potential energy surface. Agreement with previous ring polymer molecular dynamics (RPMD) results on the same potential energy surface is excellent except for very low temperatures. The SQM results are also in reasonably good agreement with available experimental rate coefficients. The strong H/D kinetic isotope effect underscores the dominant role of quantum tunneling under an entrance channel barrier at low temperatures.

Quantitative morphological analysis framework of infant cranial sutures and fontanelles based on CT images.

Characterizing the suture morphological variation is a crucial step to investigate the influence of sutures on infant head biomechanics. This study aimed to establish a comprehensive quantitative framework for accurately capturing the cranial suture and fontanelle morphologies in infants. A total of 69 CT scans of 2-4 month-old infant heads were segmented to identify semilandmarks at the borders of cranial sutures and fontanelles. Morphological characteristics, including length, width, sinuosity index (SI), and surface area, were measured. For this, an automatic method was developed to determine the junction points between sutures and fontanelles, and thin-plate-spline (TPS) was utilized for area calculation. Different dimensionality reduction methods were compared, including nonlinear and linear principal component analysis (PCA), as well as deep-learning-based variational autoencoder (VAE). Finally, the significance of various covariates was analyzed, and regression analysis was performed to establish a statistical model relating morphological parameters with global parameters. This study successfully developed a quantitative morphological framework and demonstrate its application in quantifying morphologies of infant sutures and fontanelles, which were shown to significantly relate to global parameters of cranial size, suture SI, and surface area for infants aged 2-4 months. The developed framework proved to be reliable and applicable in extracting infant suture morphology features from CT scans. The demonstrated application highlighted its potential to provide valuable insights into the morphologies of infant cranial sutures and fontanelles, aiding in the diagnosis of suture-related skull fractures. Infant suture, Infant fontanelle, Morphological variation, Morphology analysis framework, Statistical model.

"High-throughput screening of catalytically active inclusion bodies using laboratory automation and Bayesian optimization".

BACKGROUND: In recent years, the production of inclusion bodies that retain substantial catalytic activity was demonstrated. These catalytically active inclusion bodies (CatIBs) are formed by genetic fusion of an aggregation-inducing tag to a gene of interest via short linker polypeptides. The resulting CatIBs are known for their easy and cost-efficient production, recyclability as well as their improved stability. Recent studies have outlined the cooperative effects of linker and aggregation-inducing tag on CatIB activities. However, no a priori prediction is possible so far to indicate the best combination thereof. Consequently, extensive screening is required to find the best performing CatIB variant. RESULTS: In this work, a semi-automated cloning workflow was implemented and used for fast generation of 63 CatIB variants with glucose dehydrogenase of Bacillus subtilis (BsGDH). Furthermore, the variant BsGDH-PT-CBDCell was used to develop, optimize and validate an automated CatIB screening workflow, enhancing the analysis of many CatIB candidates in parallel. Compared to previous studies with CatIBs, important optimization steps include the exclusion of plate position effects in the BioLector by changing the cultivation temperature. For the overall workflow including strain construction, the manual workload could be reduced from 59 to 7 h for 48 variants (88%). After demonstration of high reproducibility with 1.9% relative standard deviation across 42 biological replicates, the workflow was performed in combination with a Bayesian process model and Thompson sampling. While the process model is crucial to derive key performance indicators of CatIBs, Thompson sampling serves as a strategy to balance exploitation and exploration in screening procedures. Our methodology allowed analysis of 63 BsGDH-CatIB variants within only three batch experiments. Because of the high likelihood of TDoT-PT-BsGDH being the best CatIB performer, it was selected in 50 biological replicates during the three screening rounds, much more than other, low-performing variants. CONCLUSIONS: At the current state of knowledge, every new enzyme requires screening for different linker/aggregation-inducing tag combinations. For this purpose, the presented CatIB toolbox facilitates fast and simplified construction and screening procedures. The methodology thus assists in finding the best CatIB producer from large libraries in short time, rendering possible automated Design-Build-Test-Learn cycles to generate structure/function learnings.

Mixed effects models but not t-tests or linear regression detect progression of apathy in Parkinson's disease over seven years in a cohort: a comparative analysis.

INTRODUCTION: While there is an interest in defining longitudinal change in people with chronic illness like Parkinson's disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researchers. Here, we aim to demonstrate how the choice of statistical method may influence research outcomes, (e.g., progression in apathy), specifically the size of longitudinal effect estimates, in a cohort. METHODS: In this retrospective longitudinal analysis of 802 people with typical Parkinson's disease in the Luxembourg Parkinson's study, we compared the mean apathy scores at visit 1 and visit 8 by means of the paired two-sided t-test. Additionally, we analysed the relationship between the visit numbers and the apathy score using linear regression and longitudinal two-level mixed effects models. RESULTS: Mixed effects models were the only method able to detect progression of apathy over time. While the effects estimated for the group comparison and the linear regression were smaller with high p-values (+ 1.016/ 7 years, p = 0.107, -0.056/ 7 years, p = 0.897, respectively), effect estimates for the mixed effects models were positive with a very small p-value, indicating a significant increase in apathy symptoms by + 2.345/ 7 years (p < 0.001). CONCLUSION: The inappropriate use of paired t-tests and linear regression to analyse longitudinal data can lead to underpowered analyses and an underestimation of longitudinal change. While mixed effects models are not without limitations and need to be altered to model the time sequence between the exposure and the outcome, they are worth considering for longitudinal data analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation.

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations.

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Implications of variability on medical device chemical equivalence assessment.

Chemical equivalence testing can be used to assess the biocompatibility implications of a materials or manufacturing change for a medical device. This testing can provide a relatively facile means to evaluate whether the change may result in additional or different toxicological concerns. However, one of the major challenges in the interpretation of chemical equivalence data is the lack established criteria for determining if two sets of extractables data are effectively equivalent. To address this gap, we propose a two-part approach based upon a relatively simple statistical model. First, the probability of a false positive conclusion, wherein there is an incorrectly perceived increase for a given analyte in the comparator relative to the baseline device, can be reduced to a prescribed level by establishing an appropriate acceptance criterion for the ratio of the observed means. Second, the probability of a false negative conclusion, where an actual increase in a given analyte cannot be discerned from the test results, can be minimized by specifying a limiting value of applicability based on the margin of safety (MoS) of the analyte. This approach provides a quantitative, statistically motivated method to interpret chemical equivalence data, despite the relatively high intrinsic variability and small number of replicates typically associated with a chemical characterization evaluation.

Understanding the Nonlinear Response of SiPMs.

A systematic study of the nonlinear response of Silicon Photomultipliers (SiPMs) was conducted through Monte Carlo (MC) simulations. The MC code was validated against experimental data for two different SiPMs. Nonlinearity mainly depends on the balance between the photon rate and the pixel recovery time. Additionally, nonlinearity has been found to depend on the light pulse shape, the correlated noise, the overvoltage dependence of the photon detection efficiency, and the impedance of the readout circuit. Correlated noise has been shown to have a minor impact on nonlinearity, but it can significantly affect the shape of the SiPM output current. Considering these dependencies and a previous statistical analysis of the nonlinear response of SiPMs, two phenomenological fitting models were proposed for exponential-like and finite light pulses, explaining the roles of their various terms and parameters. These models provide an accurate description of the nonlinear responses of SiPMs at the level of a few percentages for a wide range of situations.

Sequence-Based Protein Design: A Review of Using Statistical Models to Characterize Coevolutionary Traits for Developing Hybrid Proteins as Genetic Sensors.

Statistical analyses of homologous protein sequences can identify amino acid residue positions that co-evolve to generate family members with different properties. Based on the hypothesis that the coevolution of residue positions is necessary for maintaining protein structure, coevolutionary traits revealed by statistical models provide insight into residue-residue interactions that are important for understanding protein mechanisms at the molecular level. With the rapid expansion of genome sequencing databases that facilitate statistical analyses, this sequence-based approach has been used to study a broad range of protein families. An emerging application of this approach is to design hybrid transcriptional regulators as modular genetic sensors for novel wiring between input signals and genetic elements to control outputs. Among many allosterically regulated regulator families, the members contain structurally conserved and functionally independent protein domains, including a DNA-binding module (DBM) for interacting with a specific genetic element and a ligand-binding module (LBM) for sensing an input signal. By hybridizing a DBM and an LBM from two different family members, a hybrid regulator can be created with a new combination of signal-detection and DNA-recognition properties not present in natural systems. In this review, we present recent advances in the development of hybrid regulators and their applications in cellular engineering, especially focusing on the use of statistical analyses for characterizing DBM-LBM interactions and hybrid regulator design. Based on these studies, we then discuss the current limitations and potential directions for enhancing the impact of this sequence-based design approach.

Age-Dependent Risk of Respiratory Syncytial Virus Infection: A Systematic Review and Hazard Modeling From Serological Data.

BACKGROUND: There is no immunization campaign that currently exist for respiratory syncytial virus (RSV). Seroprevalence studies are critical for assessing epidemiological dynamics before and during an immunization program. A systematic literature review was conducted to summarize the evidence from seroprevalence studies on RSV. METHODS: A systematic search of age-dependent RSV seroprevalence was conducted using the PubMed database and EMBASE. Age-dependent force of infections (FoI) and the decay rate of immunity were estimated. A mixture finite model was used, estimating the age-dependent disease state and the antibody concentrations in susceptible and infected or recovered populations. RESULTS: Twenty-one studies were identified from 15 countries, with studies using enzyme-linked immunosorbent assay being the most represented. Using a catalytic model, the age-dependent force of infection was estimated to be the lowest in infants aged 6 months to 1 year and increased in older age groups. The proportion ever-infected/recovered was estimated to be above 90% by 3 years of age. CONCLUSIONS: The number of seroprevalence studies covering a broad range of ages are limited. The age-dependent FoI indicated that the risk of infection was greatest among those aged >5 years. Additional data using valid assays are required to describe the transmission dynamics of RSV infection.

Statistical modeling to quantify the uncertainty of FoldX-predicted protein folding and binding stability.

BACKGROUND: Computational methods of predicting protein stability changes upon missense mutations are invaluable tools in high-throughput studies involving a large number of protein variants. However, they are limited by a wide variation in accuracy and difficulty of assessing prediction uncertainty. Using a popular computational tool, FoldX, we develop a statistical framework that quantifies the uncertainty of predicted changes in protein stability. RESULTS: We show that multiple linear regression models can be used to quantify the uncertainty associated with FoldX prediction for individual mutations. Comparing the performance among models with varying degrees of complexity, we find that the model precision improves significantly when we utilize molecular dynamics simulation as part of the FoldX workflow. Based on the model that incorporates information from molecular dynamics, biochemical properties, as well as FoldX energy terms, we can generally expect upper bounds on the uncertainty of folding stability predictions of ± 2.9 kcal/mol and ± 3.5 kcal/mol for binding stability predictions. The uncertainty for individual mutations varies; our model estimates it using FoldX energy terms, biochemical properties of the mutated residue, as well as the variability among snapshots from molecular dynamics simulation. CONCLUSIONS: Using a linear regression framework, we construct models to predict the uncertainty associated with FoldX prediction of stability changes upon mutation. This technique is straightforward and can be extended to other computational methods as well.

Patterns of reported infection and reinfection of SARS-CoV-2 in England.

One of the key features of any infectious disease is whether infection generates long-lasting immunity or whether repeated reinfection is common. In the former, the long-term dynamics are driven by the birth of susceptible individuals while in the latter the dynamics are governed by the speed of waning immunity. Between these two extremes a range of scenarios is possible. During the early waves of SARS-CoV-2, the underlying paradigm was for long-lasting immunity, but more recent data and in particular the 2022 Omicron waves have shown that reinfection can be relatively common. Here we investigate reported SARS-CoV-2 cases in England, partitioning the data into four main waves, and consider the temporal distribution of first and second reports of infection. We show that a simple low-dimensional statistical model of random (but scaled) reinfection captures much of the observed dynamics, with the value of this scaling, k, providing information of underlying epidemiological patterns. We conclude that there is considerable heterogeneity in risk of reporting reinfection by wave, age-group and location. The high levels of reinfection in the Omicron wave (we estimate that 18% of all Omicron cases had been previously infected, although not necessarily previously reported infection) point to reinfection events dominating future COVID-19 dynamics. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Projecting the COVID-19 immune landscape in Japan in the presence of waning immunity and booster vaccination.

Coronavirus disease 2019 (COVID-19) booster vaccination has been implemented globally in the midst of surges in infection due to the Delta and Omicron variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The objective of the present study was to present a framework to estimate the proportion of the population that is immune to symptomatic SARS-CoV-2 infection with the Omicron variant (immune proportion) in Japan, considering the waning of immunity resulting from vaccination and naturally acquired infection. We quantified the decay rate of immunity against symptomatic infection with Omicron conferred by the second and third doses of COVID-19 vaccine. We estimated the current and future vaccination coverage for the second and third vaccine doses from February 17, 2021 to August 1, 2022 and used data on the confirmed COVID-19 incidence from February 17, 2021 to April 10, 2022. From this information, we estimated the age-specific immune proportion over the period from February 17, 2021 to August 1, 2022. Vaccine-induced immunity, conferred by the second vaccine dose in particular, was estimated to rapidly wane. There were substantial variations in the estimated immune proportion by age group because each age cohort experienced different vaccination rollout timing and speed as well as a different infection risk. Such variations collectively contributed to heterogeneous immune landscape trajectories over time and age. The resulting prediction of the proportion of the population that is immune to symptomatic SARS-CoV-2 infection could aid decision-making on when and for whom another round of booster vaccination should be considered. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Assessing the transmissibility of epidemics involving epidemic zoning.

BACKGROUND: Epidemic zoning is an important option in a series of measures for the prevention and control of infectious diseases. We aim to accurately assess the disease transmission process by considering the epidemic zoning, and we take two epidemics with distinct outbreak sizes as an example, i.e., the Xi'an epidemic in late 2021 and the Shanghai epidemic in early 2022. METHODS: For the two epidemics, the total cases were clearly distinguished by their reporting zone and the Bernoulli counting process was used to describe whether one infected case in society would be reported in control zones or not. Assuming the imperfect or perfect isolation policy in control zones, the transmission processes are respectively simulated by the adjusted renewal equation with case importation, which can be derived on the basis of the Bellman-Harris branching theory. The likelihood function containing unknown parameters is then constructed by assuming the daily number of new cases reported in control zones follows a Poisson distribution. All the unknown parameters were obtained by the maximum likelihood estimation. RESULTS: For both epidemics, the internal infections characterized by subcritical transmission within the control zones were verified, and the median control reproduction numbers were estimated as 0.403 (95% confidence interval (CI): 0.352, 0.459) in Xi'an epidemic and 0.727 (95% CI: 0.724, 0.730) in Shanghai epidemic, respectively. In addition, although the detection rate of social cases quickly increased to 100% during the decline period of daily new cases until the end of the epidemic, the detection rate in Xi'an was significantly higher than that in Shanghai in the previous period. CONCLUSIONS: The comparative analysis of the two epidemics with different consequences highlights the role of the higher detection rate of social cases since the beginning of the epidemic and the reduced transmission risk in control zones throughout the outbreak. Strengthening the detection of social infection and strictly implementing the isolation policy are of great significance to avoid a larger-scale epidemic.

Accurate estimation of peak vertical ground reaction force using the duty factor in level treadmill running.

This study aimed to (1) construct a statistical model (SMM) based on the duty factor (DF) to estimate the peak vertical ground reaction force ( F v , max ) and (2) to compare the estimated F v , max to force plate gold standard (GSM). One hundred and fifteen runners ran at 9, 11, and 13 km/h. Force (1000 Hz) and kinematic (200 Hz) data were acquired with an instrumented treadmill and an optoelectronic system, respectively, to assess force-plate and kinematic based DFs. SMM linearly relates F v , max to the inverse of DF because DF was analytically associated with the inverse of the average vertical force during ground contact time and the latter was very highly correlated to F v , max . No systematic bias and a 4% root mean square error (RMSE) were reported between GSM and SMM using force-plate based DF values when considering all running speeds together. Using kinematic based DF values, SMM reported a systematic but small bias (0.05BW) and a 5% RMSE when considering all running speeds together. These findings support the use of SMM to estimate F v , max during level treadmill runs at endurance speeds if underlying DF values are accurately measured.

Research on High Precision Stiffness Modeling Method of Redundant Over-Constrained Parallel Mechanism.

Traditional stiffness modeling methods do not consider all factors comprehensively, and the modeling methods are not unified, lacking a global stiffness model. Based on screw theory, strain energy and the virtual work principle, a static stiffness modeling method for redundant over-constrained parallel mechanisms (PMs) with clearance was proposed that considers the driving stiffness, branch deformation, redundant driving, joint clearance and joint contact deformation. First, the driving stiffness and branch deformation were considered. According to the strain energy and Castiliano's second theorem, the global stiffness matrix of the ideal joint mechanism was obtained. The offset of the branch was analyzed according to the restraint force of each branch. The mathematical relationship between the joint clearance and joint contact deformation and the end deformation was established. Based on the probability statistical model, the uncertainty of the offset value of the clearance joint and the contact area of the joint caused by the coupling of the branch constraint force was solved. Finally, taking a 2UPR-RR-2RPU redundant PM as an example, a stiffness simulation of the mechanism was carried out using the finite element method. The research results show that the high-precision stiffness modeling method proposed in this paper is correct, and provides an effective method for evaluating the stiffness performance of the PM.