Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach

Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide.Therapeutic failure in lung cancer(LUAD)is heavily influenced by drug resistance.This challenge stems from the diverse cell populations within the tumor,each having unique genetic,epigenetic,and phenotypic profiles.Such variations lead to varied therapeutic responses,thereby contributing to tumor relapse and disease progression.Methods:The Genomics of Drug Sensitivity in Cancer(GDSC)database was used in this investigation to obtain the mRNA expression dataset,genomic mutation profile,and drug sensitivity information of NSCLS.Machine Learning(ML)methods,including Random Forest(RF),Artificial Neurol Network(ANN),and Support Vector Machine(SVM),were used to predict the response status of each compound based on the mRNA and mutation characteristics determined using statistical methods.The most suitable method for each drug was proposed by comparing the prediction accuracy of different ML methods,and the selected mRNA and mutation characteristics were identified as molecular features for the drug-responsive cancer subtype.Finally,the prognostic influence of molecular features on the mutational subtype of LUAD in publicly available datasets.Results:Our analyses yielded 1,564 gene features and 45 mutational features for 46 drugs.Applying the ML approach to predict the drug response for each medication revealed an upstanding performance for SVM in predicting Afuresertib drug response(area under the curve[AUC]0.875)using CIT,GAS2L3,STAG3L3,ATP2B4-mut,and IL15RA-mut as molecular features.Furthermore,the ANN algorithm using 9 mRNA characteristics demonstrated the highest prediction performance(AUC 0.780)in Gefitinib with CCL23-mut.Conclusion:This work extensively investigated the mRNA and mutation signatures associated with drug response in LUAD using a machine-learning approach and proposed a priority algorithm to predict drug response for different drugs.

Design of a Flexible Capture Mechanism Inspired by Sea Anemone for Non-cooperative Targets

Robotic grippers have been used in industry as end-effectors but are usually limited to operations in pre-defined workspace.However,few devices can capture irregularly shaped dynamic targets in space,underwater and other unstructured environments.In this paper,a novel continuum arm group mechanism inspired by the morphology and motions of sea anemones is proposed.It is able to dissipate and absorb the kinetic energy of a fast moving target in omni-direction and utilize multiple arms to wrap and lock the target without accurate positioning control.Wire-driven actuation systems are implemented in the individual continuum arms,achieving both bending motion and stiffness regulation.Through finite element method,the influence of different configurations of the continuum arm group on the capture performance is analyzed.A robotic prototype is constructed and tested,showing the presented arm group mechanism has high adaptability to capture targets with different sizes,shapes,and incident angles.

Testing and modeling of rockfill materials:A review

The research development of rockfill materials （RFM） was investigated by a series of large-scale triaxial tests. It is observed that confining pressure and particle breakage play important roles in the mechanical property, dilatancy relation and constitutive model of RFM. In addition, it is observed that the conven- tional dilatancy relation and constitutive model are not suitable for RFM due to the complex mechanical behavior. Hence, it needs to propose a unified constitutive model of RFM, considering the statedependent and particle breakage behavior.

Strength test of 3D printed artificial rock mass with pre-existing fracture

Discontinuities or structural planes are widely distributed in natural rock masses and significantly influence their geo-mechanical and geometric properties.Herein,a batch of rock samples,each with a single structural plane,is created using a 3D printer equipped with two robotic manipulators.One of the manipulators is connected via a nozzle to a concrete pumping truck,which can extrude brittle rock-like material to form layered intact rock masses;the rock-like material is mainly composed of cement,silica fume,sand and water.The other manipulator features a knife,which can carve discontinuities onto each layer of the printed model.By means of this system,rock masses with discontinuous joints are formed,and the failure strengths of rock masses with different joints are demonstrated via uniaxial compression tests and direct shear tests.The results thereof obtained via digital image correlation technology show that discontinuities lower the strength of the rock mass models significantly.With the increase of the angle between the fracture and horizontal plane,the uniaxial compressive strength first decreases,and then increases.During shear testing,the shear strength of the rock mass models increases with the surface roughness of the preset joint.These test results indicate that the influence of artificial joints on the mechanical properties of the models is consistent with that of natural rock mass joints.Using digital modeling and 3D printing technology,cracks hidden in a rock mass can be reproduced.