Rapid in-situ calibration of computational micro-spectrometer with few-shot meta-learning.

Computational micro-spectrometers comprised of detector arrays and encoding structure arrays, such as on-chip Fabry-Perot (FP) cavity filters, have great potential in many in-situ applications owing to their compact size and snapshot imaging ability. Given manufacturing deviation and environmental influence are inevitable, easy and effective calibration for spectrometer is necessary, especially for in-situ applications. Currently calibration strategies based on iterative algorithms or neural networks require accurate measurements of pixel-level (spectral) encoding functions through monochromator or large amounts of standard samples. These procedures are time-consuming and expensive, thereby impeding in-situ applications. Meta-learning algorithms with few-shot learning ability can address this challenge by incorporating the prior knowledge in the simulated dataset. In this work, we propose a meta-learning algorithm free of measuring encoding function or large amounts of standard samples to calibrate a micro-spectrometer with manufacturing deviation effectively. Our micro-spectrometer comprises 16 types of FP filters covering a wavelength range of 550-720 nm. The center wavelength of each filter type deviates from the design up to 6 nm. After calibration with 15 different color data, the average reconstruction error on the test dataset decreased from 7.2 × 10-3 to 1.2 × 10-3, and further decreased to 9.4 × 10-4 when the calibration data increased to 24. The performance is comparable to algorithms trained with measured encoding function both in reconstruction error and generalization ability. We estimated that the cost of in-situ calibration through reflectance measurements of color chart decreased to one percent of the cost through monochromator measurements. By exploiting prior deviation information in simulation data with meta-learning, the efficiency and cost of calibration are significantly improved, thereby facilitating the large-scale production and in-situ application of micro-spectrometers.

The protective effects of Ninjin'yoeito against liver steatosis/fibrosis in a non-alcoholic steatohepatitis model mouse.

Non-alcoholic steatohepatitis (NASH) is a progressive fibrotic form of non-alcoholic fatty liver disease. Liver fibrosis leads to liver cancer and cirrhosis, and drug therapy for NASH remains lacking. Ninjin'yoeito (NYT) has shown antifibrotic effects in a model of liver fibrosis without steatosis but has not been studied for NASH. Therefore, we evaluated the efficacy of NYT in mice fed a choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) as a NASH model. Compared with the normal diet group, mice fed CDAHFD showed decreased body weight and increased white adipose tissue, liver weight, and triglyceride content in the liver. Furthermore, a substantial increase in the hepatic concentration of hydroxyproline, expression of α-smooth muscle actin (α-SMA), and transforming growth factor-ß was observed in CDAHFD-fed mice. Masson's trichrome and Picro-Sirius red staining revealed a remarkable increase in collagen fiber compared with the normal diet group. Compared with mice that received CDAHFD alone, those supplemented with NYT exhibited reduced hepatic triglyceride and hydroxyproline levels and α-SMA expression. Additionally, compared with the group fed CDAHFD alone, the stained liver tissues of NYT-treated mice exhibited a reduction in Masson's trichrome- and Picro-Sirius red-positive areas. Locomotor activity was significantly reduced in the CDAHFD-fed group compared with the normal diet group. In the NYT-treated group, the CDAHFD-induced decrease in locomotor activity was significantly suppressed. The findings indicate that NYT inhibited fatty and fibrotic changes in the livers of NASH mice and alleviated the decrease in locomotor activity. Therefore, NYT may serve as a novel therapeutic approach for NASH.