Single-shot optical imaging with spectrum circuit bridging timescales in high-speed photography.

Single-shot optical imaging based on ultrashort lasers has revealed nonrepetitive processes in subnanosecond timescales beyond the recording range of conventional high-speed cameras. However, nanosecond photography without sacrificing short exposure time and image quality is still missing because of the gap in recordable timescales between ultrafast optical imaging and high-speed electronic cameras. Here, we demonstrate nanosecond photography and ultrawide time-range high-speed photography using a spectrum circuit that produces interval-tunable pulse trains while keeping short pulse durations. We capture a shock wave propagating through a biological cell with a 1.5-ns frame interval and 44-ps exposure time while suppressing image blur. Furthermore, we observe femtosecond laser processing over multiple timescales (25-ps, 2.0-ns, and 1-ms frame intervals), showing that the plasma generated at the picosecond timescale affects subsequent shock wave formation at the nanosecond timescale. Our technique contributes to accumulating data of various fast processes for analysis and to analyzing multi-timescale phenomena as a series of physical processes.

Super-resolution vibrational imaging based on photoswitchable Raman probe.

Super-resolution vibrational microscopy is promising to increase the degree of multiplexing of nanometer-scale biological imaging because of the narrower spectral linewidth of molecular vibration compared to fluorescence. However, current techniques of super-resolution vibrational microscopy suffer from various limitations including the need for cell fixation, high power loading, or complicated detection schemes. Here, we present reversible saturable optical Raman transitions (RESORT) microscopy, which overcomes these limitations by using photoswitchable stimulated Raman scattering (SRS). We first describe a bright photoswitchable Raman probe (DAE620) and validate its signal activation and depletion characteristics when exposed to low-power (microwatt level) continuous-wave laser light. By harnessing the SRS signal depletion of DAE620 through a donut-shaped beam, we demonstrate super-resolution vibrational imaging of mammalian cells with excellent chemical specificity and spatial resolution beyond the optical diffraction limit. Our results indicate RESORT microscopy to be an effective tool with high potential for multiplexed super-resolution imaging of live cells.

Design and fabrication of an electrochemical chip for liquid-phase transmission electron microscopy.

Liquid-phase transmission electron microscopy (LP-TEM) can be used with an electrochemical chip (e-chip) to observe electrochemical reactions in a liquid in situ. The design of electrodes on an e-chip fabricated using microelectromechanical system technology cannot be easily changed. Here, we report a newly designed e-chip and its fabrication process. Electrodes with a desired shape were fabricated with various metals via an additional step of vacuum deposition onto our e-chip with a shadow mask. For precise control of the electrochemical reactions in LP-TEM, optimization of the electrode shape and material is critical.

Increasing cell-device adherence using cultured insect cells for receptor-based biosensors.

Field-effect transistor (FET)-based biosensors have a wide range of applications, and a bio-FET odorant sensor, based on insect (Sf21) cells expressing insect odorant receptors (ORs) with sensitivity and selectivity, has emerged. To fully realize the practical application of bio-FET odorant sensors, knowledge of the cell-device interface for efficient signal transfer, and a reliable and low-cost measurement system using the commercial complementary metal-oxide semiconductor (CMOS) foundry process, will be indispensable. However, the interfaces between Sf21 cells and sensor devices are largely unknown, and electrode materials used in the commercial CMOS foundry process are generally limited to aluminium, which is reportedly toxic to cells. In this study, we investigated Sf21 cell-device interfaces by developing cross-sectional specimens. Calcium imaging of Sf21 cells expressing insect ORs was used to verify the functions of Sf21 cells as odorant sensor elements on the electrode materials. We found that the cell-device interface was approximately 10 nm wide on average, suggesting that the adhesion mechanism of Sf21 cells may differ from that of other cells. These results will help to construct accurate signal detection from expressed insect ORs using FETs.

A Nearest Neighbor Classifier Employing Critical Boundary Vectors for Efficient On-Chip Template Reduction.

Aiming at efficient data condensation and improving accuracy, this paper presents a hardware-friendly template reduction (TR) method for the nearest neighbor (NN) classifiers by introducing the concept of critical boundary vectors. A hardware system is also implemented to demonstrate the feasibility of using an field-programmable gate array (FPGA) to accelerate the proposed method. Initially, k -means centers are used as substitutes for the entire template set. Then, to enhance the classification performance, critical boundary vectors are selected by a novel learning algorithm, which is completed within a single iteration. Moreover, to remove noisy boundary vectors that can mislead the classification in a generalized manner, a global categorization scheme has been explored and applied to the algorithm. The global characterization automatically categorizes each classification problem and rapidly selects the boundary vectors according to the nature of the problem. Finally, only critical boundary vectors and k -means centers are used as the new template set for classification. Experimental results for 24 data sets show that the proposed algorithm can effectively reduce the number of template vectors for classification with a high learning speed. At the same time, it improves the accuracy by an average of 2.17% compared with the traditional NN classifiers and also shows greater accuracy than seven other TR methods. We have shown the feasibility of using a proof-of-concept FPGA system of 256 64-D vectors to accelerate the proposed method on hardware. At a 50-MHz clock frequency, the proposed system achieves a 3.86 times higher learning speed than on a 3.4-GHz PC, while consuming only 1% of the power of that used by the PC.