MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods.

Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field.

Nondestructive detection of anthocyanin content in fresh leaves of purple maize using hyperspectral data.

Anthocyanins are widely used in the food industry as an additive, improving antioxidant capacity and strengthening the human immune system. However, rapid and nondestructive detection methods are lacking. This study aimed to develop a rapid and nondestructive method to detect anthocyanin content in fresh purple maize leaves using hyperspectral reflectance. Sensitivity bands were screened by analyzing the correlation between the spectrum and anthocyanin, chlorophyll, and moisture content in maize leaves with models constructed. Through a combination of the sensitivity bands of the three components, the interference of chlorophyll and moisture on the spectral detection of anthocyanin in fresh leaves was analyzed. The results showed that the anthocyanin sensitivity band was approximately 550 nm. The determination coefficient and root mean square error of the optimal hyperspectral model were 0.766 and 4.215 mg/g, respectively. After excluding chlorophyll and moisture interference, the anthocyanin content detection accuracy was improved by only 2% compared to that of the original. These results indicate that hyperspectral technology can be used to nondestructively detect anthocyanin content in fresh purple maize leaves with good accuracy. Chlorophyll and moisture in the leaves did not significantly influence anthocyanin content.

Restoration Method of a Blurred Star Image for a Star Sensor Under Dynamic Conditions.

Under the dynamic working conditions of a star sensor, motion blur of the star will appear due to its energy dispersion during imaging, leading to the degradation of the star centroid accuracy and attitude accuracy of the star sensor. To address this, a restoration method of a blurred star image for a star sensor under dynamic conditions is presented in this paper. First, a kinematic model of the star centroid and the degradation function of blurred star image under different conditions are analyzed. Then, an improved curvature filtering method based on energy function is proposed to remove the noise and improve the signal-to-noise ratio of the star image. Finally, the Richardson Lucy algorithm is used and the termination condition of the iterative equation is established by using the star centroid coordinates in three consecutive frames of restored images to ensure the restoration effect of the blurred star image and the accuracy of the star centroid coordinates. Under the dynamic condition of 0~4°/s, the proposed algorithm can effectively improve the signal-to-noise ratio of a blurred star image and maintain an error of the star centroid coordinates that is less than 0.1 pixels, which meets the requirement for high centroid accuracy.

DNA barcoding revises a misidentification on mossy frog: new record and distribution extension of Theloderma corticale Boulenger, 1903 (Amphibia: Anura: Rhacophoridae).

As an endangered animal group, mossy frog (genus Theloderma) has attracted the attention of biologists and wildlife conservationists. Clarifying the taxonomic status and distribution of each species in Theloderma is important to determine the conservation status for each species, establish appropriate conservation strategies and probe the speciation process. Recently, we discovered a medium-sized species of mossy frog of the genus Theloderma in April 2015 during municipal surveys of amphibians in Dayao Mountain of Jinxiu. It was collected from the water-filled tree cavities. However, there remains some uncertainty about the species determination of the mossy frog in the Yinshan station of Dayao Mountain in Guangxi Province, China. Previously, the mossy frog in Guangxi Province was recognized as Th. kwangsiense. In order to clarify the species status of the mossy frog obtained from Guangxi, we sequenced 2414 bp of the 12S and 16S genes in the sample collected from the Dayao Mountain. Combining all the sequence in NCBI, genetic analyses from the data suggest that the sample from the Dayao Mountain is Th. corticale rather than Th. kwangsiense. It is most likely that the most previous studies had wrong species identification. And, it is the first time we use DNA barcoding to prove that the species obtained from Guangxi is a new distribution of Th. corticale.

Ambient temperature-independent dual-band mid-infrared radiation thermometry.

For temperature measurements of targets at low temperatures, dual-band radiation thermometry using mid-infrared detectors has been investigated extensively. However, the accuracy is greatly affected by the reflected ambient radiation and stray radiation, which depend on the ambient temperature. To ensure measurement accuracy, an improved dual-band measurement model is established by considering the reflected ambient radiation and the stray radiation. The effect of ambient temperature fluctuation on temperature measurement is then further analyzed in detail. Experimental results of measuring a gray-body confirm that the proposed method yields high accuracy at varying ambient temperatures. This method provides a practical approach to remove the effect of ambient temperature fluctuations on temperature measurements.