Objective: While age has been recognized as a noteworthy factor in preoperative graft selection, the correlation between age and hamstring autograft diameter has been the subject of continued debate within the scientific community. This study aimed to explore the correlation between the diameter of autologous tendon grafts and age in anterior cruciate ligament reconstruction (ACLR). Methods: A retrospective review of 388 patients who underwent arthroscopic ACLR with hamstring autografts was performed. Patients were grouped by age to analyze differences in hamstring autograft diameter and tendon cross-sectional area (CSA). We explored the correlations between age and graft diameter and between age and the CSA of the popliteal tendon while controlling for the influence of other pertinent variables. Results: Compared with female patients, male patients presented significantly greater autograft diameters and hamstring tendon CSAs (P<0.05). Notably, graft diameter and hamstring tendon CSA varied significantly across different age groups (P<0.05); patients aged >32 years were substantially more likely to have a graft diameter exceeding 8 mm and a CSA surpassing 18.5 mm² than their ≤32-year-old counterparts were (P<0.05). Conclusion: This study revealed that graft diameter varies across different age groups, with age independently influencing graft diameter.

The assembly of a protein complex is very important for its biological function, which can be investigated by determining the order of assembly/disassembly of its protein subunits. Although static structures of many protein complexes are available in the protein data bank, their assembly/disassembly orders of subunits are largely unknown. In addition to experimental techniques for studying subcomplexes in the assembly/disassembly of a protein complex, computational methods can be used to predict the assembly/disassembly order. Since sampling is a nontrivial issue in simulating the assembly/disassembly process, coarse-grained simulations are more efficient than atomic simulations are. In this work, we developed computational protocols for predicting the assembly/disassembly orders of protein complexes via coarse-grained simulations. The protocols were illustrated via two protein complexes, and the predicted assembly/disassembly orders were consistent with the available experimental data.

In the interdisciplinary realm of statistics, genetics, and epidemiology, longitudinal sibling pair data offers a unique perspective for investigating complex diseases and traits, allowing the exploration of the dynamic processes of gene expression over time by controlling numerous confounding factors. Missing-not-at-random (MNAR) data are commonly used in such types of studies, but no statistical methods specifically tailored have been developed to handle MNAR data in complex longitudinal data in the literature. Here, we propose a new statistical method by jointly modeling longitudinal data from sib-pairs and MNAR data. Extensive simulations demonstrate the excellent finite sample properties of the proposed method.

Mendelian randomization (MR) is widely used in causal mediation analysis to control unmeasured confounding effects, which is valid under some strong assumptions. It is thus of great interest to assess the impact of violations of these MR assumptions through sensitivity analysis. Sensitivity analyses have been conducted for simple MR-based causal average effect analyses, but they are not available for MR-based mediation analysis studies, and we aim to fill this gap in this paper. We propose to use two sensitivity parameters to quantify the effect due to the deviation of the IV assumptions. With these two sensitivity parameters, we derive consistent indirect causal effect estimators and establish their asymptotic propersties. Our theoretical results can be used in MR-based mediation analysis to study the impact of violations of MR assumptions. The finite sample performance of the proposed method is illustrated through simulation studies, sensitivity analysis, and application to a real genome-wide association study.

The three common genetic models (or modes of inheritance) in association analysis are the dominant, additive, and recessive models. It is known that the Cochran-Armitage trend test (CATT) which correctly incorporates information from genetic models, is more powerful than the commonly used Pearson’s chi-square test. However, the true genetic model is usually unknown in practice, and the power of the CAT test could be substantially reduced with a wrongly specified genetic model. To achieve a power that is close to that of a correctly specified CAT test, it is natural to apply trend tests under different possible genetic models and to report the most significant test result. This results in a MAX-type testing procedure, and it was found that this test is usually more powerful than the Pearson’s chi-square test. Although the significance (i.e., p value) of the MAX-type test can be accessed by either large sample approximation or permutation methods, requirements for sample size or simulation replicates are demanding with respect to accuracy and efficiency. This paper proposes an approach to calculate the exact p values of MAX-type tests based on the combinatorial counting method. The simulation results show that the exact method is more accurate than the large sample approximation methods and more computationally efficient than the permutation method, and our method can be readily applied to genome-wide association studies (GWASs). The proposed method is built in an R package, MaXact, which is available at the https://github.com/Myuan2019/MaXact/.

Finding the optimal dose combination in two-agent dose-finding trials is challenging due to limited sample sizes and the extensive range of potential doses. Unlike traditional chemotherapy or radiotherapy, which primarily focuses on identifying the maximum tolerated dose (MTD), therapies involving targeted and immune agents facilitate the identification of an optimal biological dose combination (OBDC) by simultaneously evaluating both toxicity and efficacy. Currently, most approaches to determining the OBDC in the literature are model-based and require complex model fittings, making them cumbersome and challenging to implement. To address these challenges, we developed a novel model-assisted approach called uTPI-Comb. This approach refines the established utility-based toxicity probability interval design by integrating a strategically devised zone-based local and global candidate set searching strategy, which can effectively optimize the decision-making process for two-agent dose escalation or de-escalation in drug combination trials. Extensive simulation studies demonstrate that the uTPI-Comb design speeds up the dose-searching process and provides substantial improvements over existing model-based methods in determining the optimal biological dose combinations.

Forest fire accidents caused by distribution line faults occur frequently, resulting in heavy impacts on people’s safety and social and economic development. Currently, there are few risk assessments for forest fires induced by overhead distribution lines, and existing assessment methods may have difficulties in data acquisition. On this basis, a novel assessment framework based on an analytic hierarchy process, a Bayesian network and a Fussel-Vesely importance metric is proposed in this paper. The framework combines field research and historical operation and maintenance data to assess the regional-scale risk of forest fires induced by overhead distribution lines to derive the probability of forest fires and to identify high-risk lines and key hazard events in the assessment region. Finally, taking the southern Anhui region as an example, the annual fire probability of forest fires induced by overhead distribution lines in the southern Anhui region is 5.88%, and rectification measures are proposed. This study provides management with a complete assessment framework that optimizes the difficulty of data collection and allows for additional targeted corrective measures to be proposed for the entire region and route on the basis of the assessment results.