Launching,tracking,and controlling picosecond acoustic(PA)pulses are fundamentally important for the construction of ultrafast hypersonic wave sources,ultrafast manipulation of matter,and spatiotemporal imaging of int...Launching,tracking,and controlling picosecond acoustic(PA)pulses are fundamentally important for the construction of ultrafast hypersonic wave sources,ultrafast manipulation of matter,and spatiotemporal imaging of interfaces.Here,we show that GHz PA pulses can be all-optically generated,detected,and manipulated in a 2D layered MoS_(2)∕glass heterostructure using femtosecond laser pump-probe.Based on an interferometric model,PA pulse signals in glass are successfully decoupled from the coexisting temperature and photocarrier relaxation and coherent acoustic phonon(CAP)oscillation signals of MoS_(2)lattice in both time and frequency domains.Under selective interface excitations,temperature-mediated interfacial phonon scatterings can compress PA pulse widths by about 50%.By increasing the pump fluences,anharmonic CAP oscillations of MoS_(2)lattice are initiated.As a result,the increased interatomic distance at the MoS_(2)∕glass interface that reduces interfacial energy couplings can markedly broaden the PA pulse widths by about 150%.Our results open new avenues to obtain controllable PA pulses in 2D semiconductor/dielectric heterostructures with femtosecond laser pump-probe,which will enable many investigations and applications.展开更多
Objective The study aimed to develop a machine learning(ML)-coupled interpretable radiomics signature to predict the pathological status of non-palpable suspicious breast microcalcifications(MCs).Methods We enrolled 4...Objective The study aimed to develop a machine learning(ML)-coupled interpretable radiomics signature to predict the pathological status of non-palpable suspicious breast microcalcifications(MCs).Methods We enrolled 463 digital mammographical view images from 260 consecutive patients detected with non-palpable MCs and BI-RADS scored at 4(training cohort,n=428;independent testing cohort,n=35)in the First Affiliated Hospital of Nanjing Medical University between September 2010 and January 2019.Subsequently,837 textures and 9 shape features were subsequently extracted from each view and finally selected by an XGBoostembedded recursive feature elimination technique(RFE),followed by four machine learning-based classifiers to build the radiomics signature.Results Ten radiomic features constituted a malignancy-related signature for breast MCs as logistic regression(LR)and support vector machine(SVM)yielded better positive predictive value(PPV)/sensitivity(SE),0.904(95%CI,0.865–0.949)/0.946(95%CI,0.929–0.977)and 0.891(95%CI,0.822–0.939)/0.939(95%CI,0.907–0.973)respectively,outperforming their negative predictive value(NPV)/specificity(SP)from 10-fold crossvalidation(10FCV)of the training cohort.The optimal prognostic model was obtained by SVM with an area under the curve(AUC)of 0.906(95%CI,0.834–0.969)and accuracy(ACC)0.787(95%CI,0.680–0.855)from 10FCV against AUC 0.810(95%CI,0.760–0.960)and ACC 0.800 from the testing cohort.Conclusion The proposed radiomics signature dependens on a set of ML-based advanced computational algorithms and is expected to identify pathologically cancerous cases from mammographically undecipherable MCs and thus offer prospective clinical diagnostic guidance.展开更多
基金National Key Research and Development Program of China(2017YFA0700503)Natural Science Foundation of Jiangsu Province(BK20190765,BK20222007)+1 种基金National Natural Science Foundation of China(62335003,11734005,61704024,61821002,61875089,61904082,62075041,62175114)Social Development Program Fund of Jiangsu Province(BE2022827)。
文摘Launching,tracking,and controlling picosecond acoustic(PA)pulses are fundamentally important for the construction of ultrafast hypersonic wave sources,ultrafast manipulation of matter,and spatiotemporal imaging of interfaces.Here,we show that GHz PA pulses can be all-optically generated,detected,and manipulated in a 2D layered MoS_(2)∕glass heterostructure using femtosecond laser pump-probe.Based on an interferometric model,PA pulse signals in glass are successfully decoupled from the coexisting temperature and photocarrier relaxation and coherent acoustic phonon(CAP)oscillation signals of MoS_(2)lattice in both time and frequency domains.Under selective interface excitations,temperature-mediated interfacial phonon scatterings can compress PA pulse widths by about 50%.By increasing the pump fluences,anharmonic CAP oscillations of MoS_(2)lattice are initiated.As a result,the increased interatomic distance at the MoS_(2)∕glass interface that reduces interfacial energy couplings can markedly broaden the PA pulse widths by about 150%.Our results open new avenues to obtain controllable PA pulses in 2D semiconductor/dielectric heterostructures with femtosecond laser pump-probe,which will enable many investigations and applications.
基金supported in part by the State’s Key Project of Research and Development Plan(Grant Nos.2017YFC0109202 and 2017YFA0104302)in part by the National Natural Science Foundation(Grant No.61871117)in part by Science and Technology Program of Guangdong(Grant No.2018B030333001).
文摘Objective The study aimed to develop a machine learning(ML)-coupled interpretable radiomics signature to predict the pathological status of non-palpable suspicious breast microcalcifications(MCs).Methods We enrolled 463 digital mammographical view images from 260 consecutive patients detected with non-palpable MCs and BI-RADS scored at 4(training cohort,n=428;independent testing cohort,n=35)in the First Affiliated Hospital of Nanjing Medical University between September 2010 and January 2019.Subsequently,837 textures and 9 shape features were subsequently extracted from each view and finally selected by an XGBoostembedded recursive feature elimination technique(RFE),followed by four machine learning-based classifiers to build the radiomics signature.Results Ten radiomic features constituted a malignancy-related signature for breast MCs as logistic regression(LR)and support vector machine(SVM)yielded better positive predictive value(PPV)/sensitivity(SE),0.904(95%CI,0.865–0.949)/0.946(95%CI,0.929–0.977)and 0.891(95%CI,0.822–0.939)/0.939(95%CI,0.907–0.973)respectively,outperforming their negative predictive value(NPV)/specificity(SP)from 10-fold crossvalidation(10FCV)of the training cohort.The optimal prognostic model was obtained by SVM with an area under the curve(AUC)of 0.906(95%CI,0.834–0.969)and accuracy(ACC)0.787(95%CI,0.680–0.855)from 10FCV against AUC 0.810(95%CI,0.760–0.960)and ACC 0.800 from the testing cohort.Conclusion The proposed radiomics signature dependens on a set of ML-based advanced computational algorithms and is expected to identify pathologically cancerous cases from mammographically undecipherable MCs and thus offer prospective clinical diagnostic guidance.
