Joint Model Research Articles

Verification of the Finite Element Model of a Moving Load Passing Over a Single Irregular Suspended Load in the Dynamic Analysis of a Beam System

This article compares the variants of dynamic models of mobile load to describe the joint oscillations of span structures and vehicles on road bridges, taking into account the irregularities of the road surface. Using a known solution to the beam system oscillation problem, when the sprung load moves through a single unevenness, the joint modeling application of an inert mobile load and a span structure in the LS-Dyna FE complex using contacts is considered. The proposed method eliminates the need to use special plugins to describe the car dynamics and allows considering the the separation of the wheel from the road surface. At the same time, the use of contacts to create dynamic models of vehicles in the FEM is complicated by the lack of a verified way to account for road surface irregularities. In bridge calculations, spatial modeling of an elastic pavement layer with irregularities leads to the fact that the rigidity of the span structure varies in length depending on the micro profile. An effective way to solve this problem is to use solids with orthotropic material properties to describe the geometry of irregularities. Due to the unequal mechanical properties of the material along and across the beam, the layer with irregularities adequately transfers the load from the vehicle model to the supporting structures while not affecting the rigidity of the span structure. A good coincidence of the results of solving the dynamic problem by the proposed method in LS-Dyna with the results obtained by other authors in the SAP2000 program shows the possibility of using contacts for the dynamic calculation of bridge structures considering the irregularities of the road surface.

TransformerLSR: Attentive joint model of longitudinal data, survival, and recurrent events with concurrent latent structure.

In applications such as biomedical studies, epidemiology, and social sciences, recurrent events often co-occur with longitudinal measurements and a terminal event, such as death. Therefore, jointly modeling longitudinal measurements, recurrent events, and survival data while accounting for their dependencies is critical. While joint models for the three components exist in statistical literature, many of these approaches are limited by heavy parametric assumptions and scalability issues. Recently, incorporating deep learning techniques into joint modeling has shown promising results. However, current methods only address joint modeling of longitudinal measurements at regularly-spaced observation times and survival events, neglecting recurrent events. In this paper, we develop TransformerLSR, a flexible transformer-based deep modeling and inference framework to jointly model all three components simultaneously. TransformerLSR integrates deep temporal point processes into the joint modeling framework, treating recurrent and terminal events as two competing processes dependent on past longitudinal measurements and recurrent event times. Additionally, TransformerLSR introduces a novel trajectory representation and model architecture to potentially incorporate a priori knowledge of known latent structures among concurrent longitudinal variables. We demonstrate the effectiveness and necessity of TransformerLSR through simulation studies and analyzing a real-world medical dataset on patients after kidney transplantation.

Additive Value of Polygenic Risk Score to Family History for Type 2 Diabetes Prediction: Results From the All of Us Research Database.

The goal of this study was to assess the additive value of considering type 2 diabetes (T2D) polygenic risk score (PRS) in addition to family history for T2D prediction. Data were obtained from the All of Us (AoU) research database. First-degree T2D family history was self-reported on the personal family history health questionnaire. A PRS was constructed from 1,289 variants identified from a large multiancestry genome-wide association study meta-analysis for T2D. Logistic regression models were run to generate odds ratios (ORs) and 95% CIs for T2D. All models were adjusted for age, sex, and BMI. A total of 109,958 AoU research participants were included in the analysis. The odds of T2D increased with 1 SD PRS (OR 1.75; 95% CI 1.71-1.79) and positive T2D family history (OR 2.32; 95% CI 2.20-2.43). In the joint model, both 1 SD PRS (OR 1.69; 95% CI 1.65-1.72) and family history (OR 2.06; 95% CI 1.98-2.15) were significantly associated with T2D, although the ORs were slightly attenuated. Predictive models that included both the PRS and family history (area under the curve [AUC] 0.794) performed better than models including only family history (AUC 0.763) or the PRS (AUC 0.785). In predicting T2D, inclusion of a T2D PRS in addition to family history of T2D (first-degree relatives) added statistical value. Further study is needed to determine whether consideration of both family history and a PRS would be useful for clinical T2D prediction.

Impact of tislelizumab + chemotherapy versus placebo + chemotherapy on patient-reported symptoms and disease progression by programmed death-ligand 1 expression in gastroesophageal adenocarcinoma: A post hoc analysis of the RATIONALE-305 trial.

365 Background: The relationship between patient-reported outcome (PRO)-based symptom scores, recurrent PRO-based symptomatic deterioration events (RDEs), and terminal events such as progression-free survival (PFS) are rarely examined in the oncology therapeutic domain. Thus, we applied a 3-component joint model (JM) framework aiming to illuminate more clinically interpretable associations between PRO-based treatment effects, RDEs, and PFS among subgroups of patients with programmed death-ligand 1 (PD-L1) expression of ≥1%, ≥5%, and ≥10%. Methods: The final analytic sample included 378 patients in the tislelizumab + chemotherapy arm (T+C) vs 401 in the placebo + chemotherapy arm (P+C) for the PD-L1 ≥1% subgroup, 238 in the T+C arm vs 237 in the P+C for the PD-L1 ≥5% subgroup, and 118 in the T+C arm vs 125 in the P+C arm for the PD-L1 ≥10% subgroup. Symptom domain scores from the EORTC QLQ-C30 and QLQ-STO22 symptom were modeled. Change from baseline (CFBL) in each domain was analyzed every cycle up to cycle 6, then every other cycle thereafter (up to cycle 25). The joint model was applied to all PD-L1 subgroups and comprised three components: 1) linear mixed model (LMM) predicting CFBL symptom scores, 2) Cox proportional hazard (CPH) model for disease progression (PFS as terminal event), and 3) frailty (random effects for RDEs) CPH model for time to PRO-based RDEs. Osoba’s 10-point threshold was used to define RDEs. Results: In the LMM, significant treatment efficacy for the T+C arm compared with the P+C arm was observed for the GHS/QoL ( P =0.0080) and dietary restriction scores ( P =0.0475) in the PD-L1 ≥5% subgroup. In the PD-L1 ≥1% subgroup, compared with the P+C arm, the T+C arm was associated with less worsening in pain/discomfort ( P =0.0376), upper gastrointestinal symptoms ( P =0.0088), and dietary restrictions ( P =0.0352). In the CPH model, the average hazard ratio indicated that compared with the P+C arm, the T+C arm was associated with a reduction in risk of disease progression across all PRO domains and PD-L1 subgroups. Lastly, the PRO-based RDE frailty predictions were strongly associated with PFS risk in the PD-L1 ≥1% and ≥5% subgroups for GHS/QoL, and in the PD-L1 ≥1% subgroup for physical functioning, fatigue, dysphagia-odynophagia, pain/discomfort, and dietary restrictions, as well as for pain/discomfort and dietary restrictions in the PD-L1 ≥5% subgroup. Conclusions: Through the 3-component JM, PRO-based effects were detected and demonstrated strong associations between PRO-based RDEs, and investigator-assessed PFS among varying PD-L1 expression levels. This framework may be a promising alternative for analyzing and interpreting PRO data in the context of PFS.

Impact of tislelizumab + chemotherapy versus placebo + chemotherapy on patient-reported symptoms and overall survival (OS) by programmed death-ligand 1 (PD-L1) expression in advanced or metastatic esophageal squamous cell carcinoma (ESCC): A post hoc analysis of the RATIONALE-306 trial.

366 Background: While improved survival has been previously demonstrated, the impact of immunotherapy on HRQoL in ESCC has not been well examined. Traditional PRO-based analyses in oncology trials, such as time to deterioration (TTD) and mixed models for repeated measures (MMRMs), are limited by discounting recurrent PRO events. Thus, we applied a 3-component joint model (JM) framework to define more clinically interpretable associations between patient-reported symptoms, treatment effects, and OS among subgroups of patients with ESCC from RATIONALE-306, which met its primary endpoint, with PD-L1 expression of ≥1%, ≥5%, and ≥10%. Methods: The final analytic sample included 226 patients in the tislelizumab + chemotherapy arm (T+C), 242 in the placebo + chemotherapy arm (P+C) for PD-L1 ≥1%, 113 in the T+C arm and 103 in the P+C arm for PD-L1 ≥5%, and 168 in the T+C arm and 178 in the P+C arm for PD-L1 ≥10%.From EORTC QLQ-C30 and OES18, 7 keysymptom domains were modeled (GHS, physical functioning, fatigue, dysphagia, pain, reflux, dietary restrictions). PRO data were collected at baseline and at every treatment cycle (up to 6 cycles), then every other cycle, and at safety follow-up, and change from baseline (CFBL) was analyzed. The joint model comprised three components: 1) linear mixed model predicting CFBL symptom scores; 2) Cox proportional hazard model (CPH) for time to OS; and 3) frailty (random effects for recurrent deterioration events [RDEs]) CPH model for time to PRO-based RDEs. Osoba (1998) 10-point threshold was used to define RDEs. Results: Adjusted completion rates were >90% in the ITT population for both arms. Significant T+C treatment effects wereobserved for physical functioning in PD-L1 ≥5% ( P =0.0476), and pain in PD-L1 ≥1% ( P =0.0028) and PD-L1 ≥5% ( P =0.0149) subgroups, but not in PD-L1 ≥10%. For other 5 symptoms (ie, GHS, fatigue, reflux, dysphagia, dietary restrictions), there were no statistically significant differences between treatment arms. However, T+C was associated with significant reductions in the risk of death across all 7 key symptoms and PD-L1 subgroups. As one example, with respect to interaction between pain and OS, T+C was associated with a 22% (HR, 0.78 [95% CI, 0.652-0.931]), 33% (HR, 0.67 [95% CI, 0.515-0.860]), and 47% (HR, 0.50 [95% CI, 0.344-0.720]) reduction in the risk of death in PD-L1 ≥1%, ≥5%, and ≥10%, respectively compared with P+C. Conclusions: In this analysis, the addition of tislelizumab to chemotherapy was associated with significantly less deterioration in multiple PRO symptoms including physical functioning and pain, alongside a lower risk of death, after adjusting for recurring deterioration events using a frailty model across multiple PD-L1 expression subgroups.

Modeling of CFRP hybrid lap joints via energy-based 2D framework

Modeling of CFRP hybrid lap joints via energy-based 2D framework

Nomogram to screen older adult patients attending the radiology department for sarcopenia.

Sarcopenia is an age-related syndrome that can impact the physical and mental health of older adults. However, it is often overlooked in clinical practice. Therefore, we aim to construct a nomogram based on simplified discriminant parameters for screening older adult patients for sarcopenia risk. This cross-sectional study included 654 patients aged ≥ 60 years who underwent an examination in the radiology department between October 2023 and June 2024. Patients were diagnosed with sarcopenia according to the method and cutoff value criteria proposed the Asian Working Group on Sarcopenia (AWGS) 2019 criteria. Calf circumference (CC), SARC-F score, mid-upper arm circumference (MUAC), and SARC-CalF score were used as simplified discriminant parameters for sarcopenia. The discriminative ability of these parameters for sarcopenia was assessed using receiver operating characteristic analysis. Additionally, we included each screening parameter and evaluated it's important for screening for the presence of sarcopenia via univariate and multivariate logistic regression analysis to develop a new screening nomogram model. The performance of the nomogram was evaluated using receiver operating characteristic curves, and the performance of the nomogram model was compared to that of CC, SARC-F, MUAC, and the SARC-CalF using the Delong test. Of the 654 subjects, 120 (18.3%) were diagnosed with sarcopenia, and the areas under the curve (AUCs) of the CC, SARC-F, MUAC, and SARC-CalF were 0.73, 0.61, 0.66, and 0.70, respectively. The multivariate analysis results revealed that older age, male sex, low CC, low MUAC, and low strength were related to sarcopenia. A nomogram model constructed with these five variables had an AUC of 0.84. The DeLong test showed that the diagnostic efficacy of the joint model was significantly higher than that of CC, SARC-F, MUAC, and SARC-CalF. Our simple nomogram based on simplified discriminant parameters offers personalized sarcopenia screening for older adult patients attending the radiology department.

Bayesian identification of differentially expressed isoforms using a novel joint model of RNA-seq data.

We develop a Bayesian approach, BayesIso, to identify differentially expressed isoforms from RNA-seq data. The approach features a novel joint model of the sample variability and the deferential state of isoforms. Specifically, the within-sample variability and the between-sample variability of each isoform are modeled by a Poisson-Lognormal model and a Gamma-Gamma model, respectively. Using a Bayesian framework, the differential state of each isoform and the model parameters are jointly estimated by a Markov Chain Monte Carlo (MCMC) method. Extensive studies using simulation and real data demonstrate that BayesIso can effectively detect isoforms of less differentially expressed and differential transcripts for genes with multiple isoforms. We applied the approach to breast cancer RNA-seq data and uncovered a unique set of isoforms that form key pathways associated with breast cancer recurrence. First, PI3K/AKT/mTOR signaling and PTEN signaling pathways are identified as being involved in breast cancer development. Further integrated with protein-protein interaction data, pathways of Jak-STAT, mTOR, MAPK and Wnt signaling are revealed in association with breast cancer recurrence. Finally, several pathways are activated in the early recurrence of breast cancer. In tumors that occur early, members of pathways of cellular metabolism and cell cycle (such as CD36 and TOP2A) are upregulated, while immune response genes such as NFATC1 are downregulated.

Jointly modeling responses and omitted items by a competing risk model: A survival analysis approach.

Item response theory models are commonly adopted in educational assessment and psychological measurement. Such models need to be modified to accommodate practical situations when statistical sampling assumptions are violated. Omission is a common phenomenon in educational testing. In modern computer-based testing, we have not only examinees' responses but also their response times. This paper utilizes response time and develops a joint model of responses and response times. The new approach is analogous to those developed in survival analysis for dealing with right-censored data. In particular, a key ingredient is the introduction of the omission time (OT), which corresponds to the censoring time in survival analysis. By competing risk formulation, the proposed method provides an alternative narrative to how an item becomes answered versus omitted, depending on the competing relationship of response time and OT, so that the likelihood function can be constructed properly. The maximum likelihood estimator can be computed via the expectation-maximization algorithm. Simulation studies were conducted to evaluate the performance of the proposed method and its robustness against various mis-specifications. The method was applied to a dataset from the PISA 2015 Science Test.

P-575. Evaluation of Weight Gain on Incident Hypertension among People Living with HIV-1 Receiving Dolutegravir (DTG)-Based Regimens or Comparator Antiretroviral Therapy (cART) in Randomized Clinical Trials through 96 Weeks

Abstract Background Weight gain has been observed with integrase strand transfer inhibitor (INSTI)-based treatment with an association between INSTI use and hypertension (HTN) in some studies. We observed no difference in odds of incident HTN and minimal weight increase in participants receiving DTG-based or comparator antiretroviral therapy (cART) through Week (W)96. Here, we evaluated the relationship between ART use, weight gain, and HTN risk among participants without baseline (BL) HTN receiving DTG or cART in pooled phase 2/3 studies. Methods Data from ART-naive participants randomized to either DTG-based ART (+ ABC/3TC or TDF/FTC), or cART (EFV/TDF/FTC, RAL + 2 NRTIs, or DRV/r + 2 NRTIs), were pooled from the SPRING-1 and -2, SINGLE, and FLAMINGO trials. HTN at BL, W24, W48, and W96 was defined as a single systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg, HTN history or HTN adverse event, and/or antihypertensive use. Mixed-models repeated-measures (MMRM) analyses evaluated adjusted change from BL in weight. Bayesian Joint Models (JM) evaluated the associations between treatment, weight change, and treatment and weight change interaction with HTN risk. Models were adjusted for key BL variables and pooled treatment. Results Among 2345 participants, 23% (n=530) were excluded for BL HTN; of the remaining, 927 received DTG-based ART and 888 received cART. At W96, adjusted mean (SE) change in weight was 1.98 (0.242) kg for DTG-based ART vs 1.59 (0.212) kg for cART (treatment difference: 0.39 kg; 95% CI: −0.25, 1.04; P=0.230). From JM, treatment (DTG-based ART vs cART) was not independently associated with HTN risk through W96 (mean, M [hazard ratio, HR]: 1.099; 95% credible interval [CrI]: 0.876, 1.370). On average, a 1-kg increase in weight was estimated to increase HTN risk (HR) by 1.012 (CrI: 1.004, 1.020). No evidence of treatment separation was observed through the treatment-weight interaction (DTG-based ART HR: 1.013 [1.003, 1.022]; cART HR: 1.009 [0.998, 1.020]). Conclusion Weight gain was moderately associated with HTN risk through W96. ART was not an independent risk factor for HTN. Weight gain attributed to treatment effect impacted HTN risk similarly. Disclosures Clifford B. Jones, BSc MSc MB ChB, GSK: Stocks/Bonds (Public Company)|ViiV Healthcare: Employee Sofia Pozsonyiova, MS, GSK: Employee|GSK: Stocks/Bonds (Public Company) Richard Grove, MSc, GSK: Employee|GSK: Stocks/Bonds (Public Company) Michael McKenna, MBChB, GSK: Employee|GSK: Stocks/Bonds (Public Company) Parul Patel, MSW, GSK: Stocks/Bonds (Public Company)|ViiV Healthcare: Employee

Development of a Finite Element Model of the Human Wrist Joint With Radial and Ulnar Axial Force Distribution and Radiocarpal Contact Validation.

This study presents a comprehensive finite element (FE) model for the human wrist, constructed from a CT scan of a 68-year-old male (type 1 wrist). This model intricately captures the bone and soft tissue geometries to study the biomechanics of wrist axial loading through tendon-driven simulations and grasping biomechanics using metacarpal loads. Validation is carried out by assessing the radial and ulnar axial loading distribution, radiocarpal articulation contact patterns, and other standard finite element metrics. The results show radial transmission of the load, consistent with results from wrist finite element models conducted in the last decade and other experimental studies. Our results confirm the model's efficacy in reproducing key known biomechanical aspects, laying the groundwork for future investigations into ongoing wrist biomechanics challenges and pathology mechanism studies.

The Impact of the Yeoh Model’s Variability in Contact on Knee Joint Mechanics

The aim of this study was to assess the impact of the variability of the Yeoh model when modeling the contact of bones through cartilage in the knee in compression and flexion–extension within a hybrid knee model. Firstly, a Sobol sequence of 64 samples and four variables representing the Yeoh parameters of the cartilage of the femur and tibia was generated. Based on these samples, 2 × 64 finite element contact models of the geometry of the sphere plane were generated and solved for healthy tissue affected by osteoarthritis. The resulting indentation curves were incorporated into a multibody knee joint model. The obtained results suggested that cartilage variability severely affected the knee in compression by up to 32%. However, the same variability also affected the flexion–extension motion, although to a lesser extent, with a relative change to the range of angular displacements of almost 7%. Osteoarthritic tissue was consistently more affected by this variability, suggesting that when modeling degenerated tissue, complex joint models are necessary.

A Bayesian Joint Model of Multiple Nonlinear Longitudinal and Competing Risks Outcomes for Dynamic Prediction in Multiple Myeloma: Joint Estimation and Corrected Two-Stage Approaches.

Predicting cancer-associated clinical events is challenging in oncology. In Multiple Myeloma (MM), a cancer of plasma cells, disease progression is determined by changes in biomarkers, such as serum concentration of the paraprotein secreted by plasma cells (M-protein). Therefore, the time-dependent behavior of M-protein and the transition across lines of therapy (LoT), which may be a consequence of disease progression, should be accounted for in statistical models to predict relevant clinical outcomes. Furthermore, it is important to understand the contribution of the patterns of longitudinal biomarkers, upon each LoT initiation, to time-to-death or time-to-next-LoT. Motivated by these challenges, we propose a Bayesian joint model for trajectories of multiple longitudinal biomarkers, such as M-protein, and the competing risks of death and transition to the next LoT. Additionally, we explore two estimation approaches for our joint model: simultaneous estimation of all parameters (joint estimation) and sequential estimation of parameters using a corrected two-stage strategy aiming to reduce computational time. Our proposed model and estimation methods are applied to a retrospective cohort study from a real-world database of patients diagnosed with MM in the US from January 2015 to February 2022. We split the data into training and test sets in order to validate the joint model using both estimation approaches and make dynamic predictions of times until clinical events of interest, informed by longitudinally measured biomarkers and baseline variables available up to the time of prediction.

Finite element modelling approaches for sheet metal joining: A review of techniques and application

Joining for sheet metal is a widely used process in product manufacturing in diverse industries such as automotive, aerospace, electronics, constructions and others. Finite element has become a vital tool when it comes to simulating the structural integrity and performance of many types of joining that are being used such as mechanical fastener, welding, adhesives and many more. This review paper provides a comprehensive overview of finite element modelling techniques applied to sheet metal joining based on existing studies from other researchers. The paper discusses the methods, advantages and limitations for each modelling approaches and highlights their key applications. This paper also discusses the role of finite element analysis when studying the performance of these models for joints. This will enable researchers to predict failure behaviours and enhance design efficiency. The strengths and limitations of various modelling approaches such as simplified and detailed methods and identified key areas for future research and development were also examined. By summarising the current knowledge and methodologies, this paper aims to contribute to the advancement of joint modelling techniques, which can lead to more accurate and reliable modelling method for joining in sheet metal.

Joint Modelling of Astrophysical Systematics for Cosmology with LSST Cosmic Shear

Abstract We present a novel framework for jointly modelling the weak lensing source galaxy redshift distribution and the intrinsic alignment (IA) of galaxies through a shared luminosity function (LF). In the context of a Rubin Observatory’s Legacy Survey of Space and Time (LSST) Year 1 and Year 10 cosmic shear analysis, we show that our novel approach produces cosmological parameter constraints which are comparable to standard methods, while offering more physical insight into IA and selection effects. We clarify the relationship between individual parameters of a Schechter luminosity function and the redshift distribution of a magnitude-limited sample, showing the consequences of marginalizing over these parameters when modelling intrinsic alignments in standard cosmic shear analyses. We explore the impact of the shape of the luminosity function on the cosmic shear data vector, and we outline the potential of this method to naturally model selection functions in redshift distribution estimation. Although this work focuses on LSST cosmic shear, the proposed joint modelling framework is broadly applicable to weak lensing surveys.

Multi-sequence magnetic resonance imaging radiomics combined with imaging features predicts the difficulty of HIFU treatment of uterine fibroids

To establish a multivariate linear regression model for predicting the difficulty of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids based on multi-sequence magnetic resonance imaging radiomics features. A retrospective analysis was conducted on 218 patients with uterine fibroids who underwent HIFU treatment, including 178 cases from Yongchuan Hospital of Chongqing Medical University and 40 cases from the Second Affiliated Hospital of Chongqing Medical University (external validation set). Radiomics features were extracted and selected from magnetic resonance images, and potentially related imaging features were collected. The energy efficiency factor (EEF) was used as the dependent variable. Imaging models, radiomics models, and joint models were established using a stepwise approach. The model with the highest R2 value was selected for external validation. The R2 value of the combined model was 0.642, higher than that of other models. Spearman correlation analysis showed a correlation coefficient of R = 0.824 (P < 0.001) between predicted EEF and actual EEF. External validation yielded a correlation coefficient of R = 0.645 (P < 0.001). A model for predicting EEF has been developed, which is clinically important for predicting the difficulty of HIFU treatment of uterine fibroids.

Derivation of a multivariate longitudinal causal effects model

This paper presents a causal inference estimation method for longitudinal observational studies with multiple outcomes. The method uses marginal structural models with inverse probability treatment weights (MSM-IPTWs). In developing the proposed method, we re-define the weights as a product of inverse weights at each time point, accounting for time-varying confounders and treatment exposures and possible correlation between and within (serial) the multiple outcomes. The proposed method is evaluated by simulation studies and with an application to estimate the effect of HIV positivity awareness on condom use and multiple sexual partners using the Malawi Longitudinal Study of Families and Health (MLSFH) data. The simulation study shows that the joint MSM-IPTW performs well with coverage within the expected 95% level for a large sample size (n = 1000) and moderate to strong between and within outcome correlation strength ( ρ j = 0.3 , 0.75, ρ k = 0.4 , 0.8) when the effects are similar. The joint MSM-IPTW performed relatively the same as the adjusted standard joint model when the treatment effect estimate was the same for the outcomes. In the application, HIV positivity awareness increased the usage of condoms and did not affect the number of sexual partners. We recommend using the proposed MSM-IPTWs to correctly control for time-varying treatment and confounders when estimating causal effects for longitudinal observational studies with multiple outcomes.

Research on the Dynamic Response Patterns of Layered Slopes Considering Non-Homogeneity Under Blast-Induced Vibration Effects

To investigate the dynamic wave propagation characteristics and dynamic response of heterogeneous layered slopes under a blasting vibration, a modeling method considering the slope’s layered dip angle and heterogeneity was proposed. Different dip jointed slope models were established using the Weibull random distribution function introduced to realize the stochastic distribution of rock mechanics parameters, representing heterogeneity. Taking the background project of the Sijiaying Yanshan Open-Pit Iron Mine as an example, through numerical simulation, the effects of different joint dip angles and rock hardness on the slope’s dynamic response were analyzed in detail. The sensitivity of the elastic modulus, cohesion, and friction angle to the slope dynamic response was also investigated. A comparative analysis of the amplification effects between a jointed slope and heterogeneous slope was conducted. Finally, the dynamic stability of the jointed slope and heterogeneous slope under a blasting load was analyzed. The results indicate that the Peak Ground Acceleration (PGA) of jointed slopes with dip angles of 45° and 60° is generally higher than that of slopes with a 0° dip angle and without joints. The smaller the rock mass heterogeneity, the smaller the PGA at the measuring points, and the less sensitive the PGA is to variations in the three quantities. Under the same physical and mechanical parameters of the rock, the amplification factor of jointed slopes is generally greater than that of heterogeneous slopes. Under the blasting load, the overall dynamic time-series safety factors of both slopes decrease first and then increase, with the safety factor reaching its lowest value at the location of the strongest blasting vibration wave. This study can provide guidance for the blasting design and safety protection of layered dip slopes and serve as a reference for the analysis of blasting impact laws in similar mines.

Using feedback in pooled experiments augmented with imputation for high genotyping accuracy at reduced cost.

Conducting genomic selection in plant breeding programs can substantially speed up the development of new varieties. Genomic selection provides more reliable insights when it is based on dense marker data, in which the rare variants can be particularly informative. Despite the availability of new technologies, the cost of large-scale genotyping remains a major limitation to the implementation of genomic selection. We suggest to combine pooled genotyping with population-based imputation as a cost-effective computational strategy for genotyping SNPs. Pooling saves genotyping tests and has proven to accurately capture the rare variants that are usually missed by imputation. In this study, we investigate adding iterative coupling to a joint model of pooling and imputation that we have previously proposed. In each iteration, the imputed genotype probabilities serve as feedback input for adjusting the per-sample prior genotype probabilities, before running a new imputation based on these adjusted data. This flexible setup indirectly imposes consistency between the imputed genotypes and the pooled observations. We demonstrate that repeated cycles of feedback can take advantage of the strengths in both pooling and imputation when an appropriate set of reference haplotypes is available for imputation. The iterations improve greatly upon the initial genotype predictions, achieving very high genotype accuracy for both low and high frequency variants. We enhance the average concordance from 94.5% to 98.4% at limited computational cost and without requiring any additional genotype testing.

Theoretical considerations on stiffness characteristics of a 3-dimensional tensegrity joint model for the use in dynamic hand orthoses

Theoretical considerations on stiffness characteristics of a 3-dimensional tensegrity joint model for the use in dynamic hand orthoses