Histopathological imaging of tissues often requires extensive sample preparation including fixation and staining in order to highlight characteristic alterations associated with diseases. Herein, we report an integrated spectro-microscopy approach based on a combination of multi-modal multi-photon imaging and Raman micro-spectroscopy and demonstrate label-free characterization of the structure-constituent correlation of porcine skin. The multi-modal imaging allows the visualization of dermatological features whereas Raman micro-spectroscopy enables the identification of their 'molecular fingerprints'. By obtaining both structural and molecular-level information of tissue constituents, this integrated approach can offer new insight into the patho-physiological status of tissues.
A general method for synthesizing
optically active, primary, secondary,
and tertiary organofluorides was developed. This chiral pool synthesis
utilized the skeleton of arabinose to generate diastereomerically
pure 2-oxazolidinone-fused aziridines, which underwent ring opening
with a fluoride anion. The adducts, polyoxygenated organofluorides,
were useful precursors to various fluorinated compounds, such as fluorinated
amino acids.
A major difficulty in a search-based problem-solving process is the task of searching the potentially huge search space resulting from the exponential growth of states. State explosion rapidly occupies memory and increases computation time. Although various heuristic search algorithms have been developed to solve problems in a reasonable time, there is no efficient method to construct heuristic functions. In this work, we propose a method by which a neural network can be iteratively trained to form an efficient heuristic function. An adaptive heuristic search procedure is involved in the training iterations. This procedure reduces the evaluation values of the states that are involved in the currently known best solution paths. By doing so, the promising states are continuously moved forward. The adapted heuristic values are fed back to neural networks; thus, a well-trained network function can find the near-best solutions quickly. To demonstrate this method, we solved the fifteen-puzzle problem. Experimental results showed that the solutions obtained by our method were very close to the shortest path, and both the number of explored nodes and the search time were significantly reduced.
The design of the proposed readout circuit provides benefits of detection speed, portability, low-cost and less human operational errors compared with the measurement by traditional instruments. Thus the added value is brought for biosensors and applied in home care. A novel readout circuit for a gas sensor based on an organic diode with vertical nano-junctions (VNJ) is proposed in this study. There are seven parts included in the readout system. First part is a preamplifier, second part is a peak-detect-and-hold circuit, third part is a divider, fourth part is the saturation detector, fifth part is the auto-reset circuit, sixth part is a logic gate and a buffer, seventh part is a micro-processor control unit (MCU). STM32 is the CPU of proposed MCU by ALIENTEK. The ADC of MCU is used to transform the output data of readout circuit. The designed circuit is accomplished by Taiwan Semiconductor Manufacturing Company (TSMC) 0.35 μm 2P4M 3.3 V mixed signal CMOS process, the area of chip is 0.74×0.75 mm2. Finally, the differences between experimental results with post-simulation results in 10 ppb ∼ 3 ppm of ammonia, the differences are within 7.24%. The sensing system is able to detect minimum ammonia concentration of 10 ppb, while the maximum one reaches around 3 ppm.
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