Influence of surface micro-beams with large deflection on the resonance frequency of a quartz crystal resonator in thickness-shear mode vibrations

We study the dynamic behavior of a quartz crystal resonator （QCR） in thickness-shear vibrations with the upper surface covered by an array of micro-beams （MBs） under large deflection. Through taking into account the continuous conditions of shear force and bending moment at the interface of MBs/resonator, dependences of frequency shift of the compound QCR system versus material parameter and geometrical parameter are illustrated in detail for nonlinear and linear vibrations. It is found that the frequency shift produces a little right （left） translation for increasing elastic modulus （length/radius ratio） of MBs. Moreover, the frequency right （left） translation distance caused by nonlinear deformation becomes more serious in the second-order mode than in the first-order one,

Electric admittance analysis of quartz crystal resonator in thickness-shear mode induced by array of surface viscoelastic micro-beams

The electric admittance of a compound system composed of a thickness-shear mode （TSM） quartz crystal resonator （QCR） and an array of surface viscoelastic micro-beams （MBs） is studied. The governing equations of the MBs are derived from the Timoshenko-beam theory in consideration of shear deformation. The electrical admittance is described directly in terms of the physical properties of the surface epoxy resin （SU-8） MBs from an electrically forced vibration analysis. It is found that both the inertia effect and the constraint effect of the MBs produce competitive influence on the resonant frequency and admittance of the compound QCR system. By further comparing the numerical results calculated from the Timoshenko-beam model with those from the Euler-beam model, the shear deformation is found to lead to some deviation of an admittance spectrum. The deviations are revealed to be evident around the admittance peak（s） and reach the maximum when a natural frequency of the MBs is identical to the fundamental frequency of the QCR. Besides, a higher order vibration mode of the MBs corresponds to a larger deviation at the resonance.

Hierarchical learning of gastric cancer molecular subtypes by integrating multi-modal DNA-level omics data and clinical stratification

Molecular subtyping of gastric cancer(GC)aims to comprehend its genetic landscape.However,the efficacy of current subtyping methods is hampered by their mixed use of molecular features,a lack of strategy optimization,and the limited availability of public GC datasets.There is a pressing need for a precise and easily adoptable subtyping approach for early DNA-based screening and treatment.Based on TCGA subtypes,we developed a novel DNA-based hierarchical classifier for gastric cancer molecular subtyping(HCG),which employs gene mutations,copy number aberrations,and methylation patterns as predictors.By incorporating the closely related esophageal adenocarcinomas dataset,we expanded the TCGA GC dataset for the training and testing of HCG(n=453).The optimization of HCG was achieved through three hierarchical strategies using Lasso-Logistic regression,evaluated by their overall the area under receiver operating characteristic curve(auROC),accuracy,F1 score,the area under precision-recall curve(auPRC)and their capability for clinical stratification using multivariate survival analysis.Subtype-specific DNA alteration biomarkers were discerned through difference tests based on HCG defined subtypes.Our HCG classifier demonstrated superior performance in terms of overall auROC(0.95),accuracy(0.88),F1 score(0.87)and auPRC(0.86),significantly improving the clinical stratification of patients(overall p-value=0.032).Difference tests identified 25 subtype-specific DNA alterations,including a high mutation rate in the SYNE1,ITGB4,and COL22A1 genes for the MSI subtype,and hypermethylation of ALS2CL,KIAA0406,and RPRD1B genes for the EBV subtype.HCG is an accurate and robust classifier for DNA-based GC molecular subtyping with highly predictive clinical stratification performance.The training and test datasets,along with the analysis programs of HCG,are accessible on the GitHub website(github.com/LabxSCUT).