Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction

Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images. Conventional SR methods typically gather the paired training data by synthesizing LR images from HR images using a predetermined degradation model, e.g., Bicubic down-sampling. However, the realistic degradation type of test images may mismatch with the training-time degradation type due to the dynamic changes of the real-world scenarios, resulting in inferior-quality SR images. To address this, existing methods attempt to estimate the degradation model and train an image-specific model, which, however, is quite time-consuming and impracticable to handle rapidly changing domain shifts. Moreover, these methods largely concentrate on the estimation of one degradation type (e.g., blur degradation), overlooking other degradation types like noise and JPEG in real-world test-time scenarios, thus limiting their practicality. To tackle these problems, we present an efficient test-time adaptation framework for SR, named SRTTA, which is able to quickly adapt SR models to test domains with different/unknown degradation types. Specifically, we design a second-order degradation scheme to construct paired data based on the degradation type of the test image, which is predicted by a pre-trained degradation classifier. Then, we adapt the SR model by implementing feature-level reconstruction learning from the initial test image to its second-order degraded counterparts, which helps the SR model generate plausible HR images. Extensive experiments are conducted on newly synthesized corrupted DIV2K datasets with 8 different degradations and several real-world datasets, demonstrating that our SRTTA framework achieves an impressive improvement over existing methods with satisfying speed. The source code is available at https://github.com/DengZeshuai/SRTTA.Comment: Accepted by 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Rapid genome editing by CRISPR-Cas9-POLD3 fusion

Precision CRISPR gene editing relies on the cellular homology-directed DNA repair (HDR) to introduce custom DNA sequences to target sites. The HDR editing efficiency varies between cell types and genomic sites, and the sources of this variation are incompletely understood. Here, we have studied the effect of 450 DNA repair protein-Cas9 fusions on CRISPR genome editing outcomes. We find the majority of fusions to improve precision genome editing only modestly in a locus- and cell-type specific manner. We identify Cas9-POLD3 fusion that enhances editing by speeding up the initiation of DNA repair. We conclude that while DNA repair protein fusions to Cas9 can improve HDR CRISPR editing, most need to be optimized to the cell type and genomic site, highlighting the diversity of factors contributing to locus-specific genome editing outcomes.Peer reviewe

Development of an Ultrasensitive Immunoassay for Detecting Tartrazine

We have developed an ultrasensitive indirect competitive enzyme-linked immunosorbent assay for the determination of tartrazine. Two carboxylated analogues of tartrazine with different spacer lengths, and one derivative from commercial tartrazine after a little chemical modification, were synthesized as haptens in order to produce antibodies specific to tartrazine. The effect of sulfonic acid groups on the hapten structure of tartrazine was also studied carefully for the first time. A most specific monoclonal antibody against tartrazine was created and exhibited an IC50 value of 0.105 ng/mL and a limit of detection of 0.014 ng/mL, with no cross-reactivity to other structurally-related pigments. The established immunoassay was applied to the determination of tartrazine in fortified samples of orange juice and in real positive samples of carbonated beverages

Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA's-AVHRR.

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha(-1). Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly

Results of the stepwise regression models for remotely sensed rice yield using AVHRR-derived NDVI measures as independent variables.

<p>R: multiple correlation coefficient.</p>*<p>significant at 0.05 level; ** significant at 0.01 level.</p

The locations of the study areas within Mainland China.

<p>Heilongjiang is designated by HLJ, Jiangxi by JX, Guangxi by GX, Sichuan by SC, and Hunan by HN.</p

Physical Properties of CaTiO₃-Modified NaNbO₃ Thin Films

NaNbO3(NN)-based lead-free materials are attracting widespread attention due to their environment-friendly and complex phase transitions, which can satisfy the miniaturization and integration for future electronic components. However, NN materials usually have large remanent polarization and obvious hysteresis, which are not conducive to energy storage. In this work, we investigated the effect of introducing CaTiO3((1−x)NaNbO3-xCaTiO3) on the physical properties of NN. The results indicated that as x increased, the surface topography, oxygen vacancy and dielectric loss of the thin films were significantly improved when optimal value was achieved at x = 0.1. Moreover, the 0.9NN-0.1CT thin film shows reversible polarization domain structures and well-established piezoresponse hysteresis loops. These results indicate that our thin films have potential application in future advanced pulsed power electronics

Rice yield trends for the provinces' of Heilongjiang (HLJ), Hunan (HN), Jiangxi (JX), Sichuan (SC) and Guangxi (GX) from 1979 to 2006.

<p>Rice yield trends for the provinces' of Heilongjiang (HLJ), Hunan (HN), Jiangxi (JX), Sichuan (SC) and Guangxi (GX) from 1979 to 2006.</p

General information on Rice cropping system, Life span, Total annual rainfall (mm), Annual accumulated temperature (≥10°C), Area (kha) and Production (kt) for the study areas.

<p>General information on Rice cropping system, Life span, Total annual rainfall (mm), Annual accumulated temperature (≥10°C), Area (kha) and Production (kt) for the study areas.</p