BMRMIA:A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System

Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology.The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis.Background:In recent years,deep learning has been widely used in academia,industry,andmedicine.An increasing number of companies are starting to recruit a large number of professionals in the field of deep learning.Increasing numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis techniques.For now,however,there is no practical training platform that can help radiologists learn automated medical image analysis systematically.ApplicationDesign:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this platform.The platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.

Precise Design and Deliberate Tuning of Turn-On Fluorescence in Tetraphenylpyrazine-Based Metal-Organic Frameworks

The manipulation on turn-on fluorescence in solid state materials attracts increasing interests owing to their widespread applications.Herein we report how the nonradiative pathways of tetraphenylpyrazine(TPP)units in metal-organic frameworks(MOFs)systems could be hindered through a topological design approach.Two MOFs single crystals of different topology were constructed via the solvothermal reaction of a TPP-based 4,4',4",4"-(pyrazine-2,3,5,6-tetrayl)tetrabenzoic acid(H,TCPP)ligand and metal cations,and their mechanisms of formation have been explored.Compared with the innate lowfrequency vibrational modes of flu net Tb-TCPP-1,such as phenyl ring torsions and pyrazine twists,Tb-TCPP-2 adopts a shp net,so the dihedral angle of pyrazine ring and phenyl arms is larger,and the center pyrazine ring in TPP unit is coplanar,which hinders the radiationless decay of TPP moieties in Tb-TCPP-2.Thereby Tb-TCPP-2 exhibits a larger blue-shifted fluorescence and a higher fluorescence quantum yield than Tb-TCPP-1,which is consistent with the reduced nonradiative pathways.Furthermore,Density functional theory(DFT)studies also confirmed aforementioned tunable turn-on fluorescence mechanism.Our work constructed TPP-type MOFs based on a deliberately topological design approach,and the precise design of turn-on fluorescence holds promise as a strategy for controlling nonradiative pathways.

Structural and magnetic properties of CeZnAl3 single crystals

We have synthesized single crystals of CeZnAl_3,which is a new member of family of Ce-based intermetallics CeTX_3(T=transition metal, X=Si, Ge, Al), crystallizing in the non-centrosymmetric tetragonal BaNiSn_3-type structure. Magnetization,specific heat and resistivity measurements all show that CeZnAl_3 orders magnetically below around 4.4 K. Furthermore,magnetization measurements exhibit a hysteresis loop at low temperatures and fields, indicating the presence of a ferromagnetic component in the magnetic state. This points to a different nature of the magnetism in CeZnAl_3 compared to the other isostructural CeTAl_3 compounds. Resistivity measurements under pressures up to 1.8 GPa show a moderate suppression of the ordering temperature with pressure, suggesting that measurements to higher pressures are required to look for quantum critical behavior.