Search

Metamorphosis is a crucial process in the life cycle of Rana (Aquarana) catesbeiana. R. catesbeiana tadpoles, in their short larval period, possess a high survival rate and also a highly competitive ability in the amphibious stage. In actual seed production, the economic traits of larval period and metamorphosis rate are used as quantifiable indicators of quality for individuals and populations, respectively. However, studies of these economic traits in larval cultivation and production are still lacking. In this study, we constructed 40 full-sib families of R. catesbeiana and measured the weight and metamorphosis rate of tadpoles at different developmental stages. Subsequently, we calculated the phenotypic and genetic association between weight and metamorphosis rate in tadpoles and assessed the heritability of these two traits. The heritabilities of weight at three developmental stages were all higher than 0.40 and decreased with advancement of the developmental stage; the heritability of the metamorphosis rate was 0.18 ± 0.20, a moderate level. Correlation analysis of weight and metamorphosis rate at each developmental stage in each tadpole family showed that weight at stages 25-I, 25-II, and 25-III was significantly correlated at the phenotypic level but non-significantly at the genetic level. The metamorphosis rate was only moderately associated with stage 25-III weight (0.38, p < 0.05). The results of this study confirm the importance and transportability of tadpole weight in actual seed production and provide basic data and a potentially optimized direction for the selective breeding of high-metamorphosis-rate bullfrogs. [ABSTRACT FROM AUTHOR]

Traditionally referred to as "metabolic junk", lactate has now been recognized as essential "energy currency" and crucial "messenger" that contributes to tumor evolution, immunosuppression, etc., thus presenting a promising strategy for antitumor interventions. Similarly, kynurenine (Kyn) also exerts an immunosuppressive function, thereby significantly compromising the effectiveness of immunotherapy. This study proposes and validates a strategy for enhancing immunotherapy through photothermal‐assisted depletion of lactate sustained by cycle‐like O2 supply, with blocking the tryptophan (Trp)/Kyn metabolic pathway. In brief, a nanozyme therapeutic agent (PNDPL) is constructed, which mainly consists of PtBi nanozymes, lactate oxidase (LOX) and the indoleamine 2,3‐dioxygenase (IDO) inhibitor NLG919. The PtBi nanozymes, which exhibit a catalase (CAT)‐like activity, form a positive feedback loop with LOX to consume lactate while self‐supplying O2. Moreover, PtBi nanozymes retain enzyme‐like performance even in a slightly acidic tumor microenvironment. Under 1064 nm irradiation, photothermal therapy (PTT) not only induces tumor cell death but also accelerates lactate exhaustion. Therefore, the combination of lactate depletion‐induced starvation therapy and PTT, along with the blocking of IDO‐mediated immune escape, effectively inhibits tumor growth and reverses immunosuppressive microenvironment, thus preventing tumor metastasis. This study represents the first investigation into the synergistic antitumor effects by lactate metabolism regulation and IDO‐related immunotherapy. [ABSTRACT FROM AUTHOR]

Vision-based sign language translation technology (SLT) has brought the communication distance between deaf and ordinary people closer to a certain extent. The obstacle of SLT is mainly in two aspects: firstly, when capturing sign language action features, it is impossible to effectively overcome the shortcomings such as redundant information of sign language gesture features and motion ambiguity; secondly, it is difficult to define the alignment between action sequences and lexical sequences when processing sentence-level sign language videos. To overcome these problems, this paper proposes a sign language translation method based on residual spatial graph convolution network (Res-SGCN) and temporal attention model. Where, the Res-SGCN module is used to capture the spatial interaction feature information between the sign language skeleton nodes, and subsequently the temporal attention network is used to capture the temporal dimensional information fusion of the sign language spatial feature sequence and align it with the predicted vocabulary for translation. Experiments on public datasets show that the word error rate(WER) output by the proposed model reaches 4.17%, which is superior to other advanced sign language translation methods. [ABSTRACT FROM AUTHOR]