Addition of rituximab (R) to fludarabine and cyclophosphamide (FC) has significantly improved patient outcomes in chronic lymphocytic leukemia (CLL). Whether baseline gene expression can identify patients who will benefit from immunochemotherapy over chemotherapy alone has not been determined. We assessed genome-wide expression of 300 pretreatment specimens from a subset of 552 patients in REACH, a study of FC or R-FC in relapsed CLL. An independent test set was derived from 282 pretreatment specimens from CLL8, a study of FC or R-FC in treatment-naïve patients. Genes specific for benefit from R-FC were determined by assessing treatment-gene interactions in Cox proportional hazards models. REACH patients with higher pretreatment protein tyrosine kinase 2 (PTK2) messenger RNA levels derived greater benefit from R-FC, with significant improvements in progression-free survival, independent of known prognostic factors in a multivariate model. Examination of PTK2 gene expression in CLL8 patients yielded similar results. Furthermore, PTK2 inhibition blunted R-dependent cell death in vitro. This retrospective analysis from 2 independent trials revealed that increased PTK2 expression is associated with improved outcomes for CLL patients treated with R-FC vs FC. PTK2 expression may be a useful biomarker for patient selection in future trials. These trials were registered at www.clinicaltrials.gov as #NCT00090051 (REACH) and #NCT00281918 (CLL8). Key Points Increased PTK2 expression is associated with improved outcomes in patients with CLL treated with R-FC immunochemotherapy. PTK2 expression represents a useful, novel biomarker for selection of patients who will benefit from R-FC immunochemotherapy.

Robotic manipulators are widely used in various industries for complex and repetitive tasks. However, they remain vulnerable to unexpected hardware failures. In this study, we address the challenge of enabling a robotic manipulator to complete tasks despite joint malfunctions. Specifically, we develop a reinforcement learning (RL) framework to adaptively compensate for a nonfunctional joint during task execution. Our experimental platform is the Franka robot with seven degrees of freedom (DOFs). We formulate the problem as a partially observable Markov decision process (POMDP), where the robot is trained under various joint failure conditions and tested in both seen and unseen scenarios. We consider scenarios where a joint is permanently broken and where it functions intermittently. Additionally, we demonstrate the effectiveness of our approach by comparing it with traditional inverse kinematics-based control methods. The results show that the RL algorithm enables the robot to successfully complete tasks even with joint failures, achieving a high success rate with an average rate of 93.6%. This showcases its robustness and adaptability. Our findings highlight the potential of RL to enhance the resilience and reliability of robotic systems, making them better suited for unpredictable environments.

This study aims to investigate the effects of the draw beads on the crashworthiness of the aluminum tubes under axial quasi-static loading. Based on this design philosophy, a total of 12 beading tube designs with various configurations were developed. Within each design, the effect of arrangement bead form on the crashworthiness performance was also analyzed. A finite element model, validated using experimental tests, was used to study the crashworthiness performance and progressive deformation of the tubes. Based on the results, a multi-criteria decision-making method known as Technique of Order Preference by Similarity to Ideal Solution was employed to determine the most suitable tube that features high energy absorption and low impact force. The best tube with a high score was selected to investigate the effect of bead formed direction on aluminum tubes. Consequently, the study identified a bead shape tubes configuration that exhibits superior crashworthiness and low impact force. The beading tube design methodology presented in this study allows the exploitation of variable shapes geometries for the development of high-efficiency energy-absorbing structures and their crushing behaviors.

This paper conducts an extensive investigation to address the optimization challenge of designing planetary gear trains (PG Ts) with one or two stages. The Hippopotamus Optimization (HO) algorithm, Puma Optimizer (PO) algorithm, and Mountain Gazelle Optimizer (MGO) algorithm are utilized to optimize the volume of the planetary gearboxes. The determination of the appropriate mathematical model and constraint conditions for each independent variable is undertaken after considering all design factors. The optimal volume values obtained with the three distinct optimizers are better than those reported in the literature. Two examples demonstrate great agreement between the present and available results.

This study focuses on developing a dynamic model of the rider–folding bicycle system equipped with a rear suspension, based on the geometric parameters of the commercial Fornix F160 bicycle and pivot point locations referenced from the work of Good and McPhee. The equations of motion, derived from the second kind of Lagrange equations, are numerically solved using the Runge–Kutta method to determine the rider’s acceleration response, which serves as a key indicator for evaluating ride comfort. The reliability of the dynamic model is validated by comparing its results with simulations conducted in Altair Inspire software. Following model validation, the defined dynamic model is employed to quantify rider comfort through vertical acceleration responses, in accordance with the ISO 2631 standard. The analysis is conducted under two excitation conditions: irregular road surfaces defined by ISO 8608 and cluster-type speed bumps specified in TCCS 34:2020. Based on this setup, the study investigates the influence of rear suspension parameters ( c; k ), aiming to identify the optimal configuration that enhances rider comfort. The results demonstrate that the proposed model significantly improves ride comfort, with enhancements of up to 88.57% compared to a rigid-frame bicycle. These findings have informed notable structural modifications to the frame design relative to the original folding bicycle configuration.

This study investigates the fatigue performance of 3D-printed metal shafts fabricated via Wire Arc Additive Manufacturing (WAAM) with stacked steel ring substrates under rotating bending (ISO 1143:2021). A Taguchi L25 orthogonal array was used to analyze five process parameters: ring diameter, current intensity, torch speed, ring thickness, and contact tip to workpiece distance (CTWD). Analysis of Variance (ANOVA) identified ring diameter as the dominant factor, significantly enhancing fatigue life at 14.0 mm by reducing stress concentrations. Current intensity (125 A) and torch speed (550 mm/min) further improve weld quality and microstructure, while ring thickness (1.0 mm) and CTWD (1.5 mm) have minor effects. A linear regression model (R² = 0.9603) accurately predicts fatigue life, with optimal settings yielding 299,730 cycles. The stacked-ring configuration enables intricate structures like cooling channels, ideal for aerospace and automotive applications. The 3.5% unexplained variance suggests parameter interactions, warranting further investigation into shielding gas effects and multiaxial loading to broaden material and loading applicability.