The accumulation of neutral lipids in intracellular lipid droplets has been associated with the formation and progression of many cancers, including prostate cancer (PCa). Alpha-beta Hydrolase Domain Containing 5 (ABHD5) is a key regulator of intracellular neutral lipids that has been recently identified as a tumor suppressor in colorectal cancer, yet its potential role in PCa has not been investigated. Through mining publicly accessible PCa gene expression datasets, we found that ABHD5 gene expression is markedly decreased in metastatic castration-resistant PCa (mCRPC) samples. We further demonstrated that RNAi-mediated ABHD5 silencing promotes, whereas ectopic ABHD5 overexpression inhibits, the invasion and proliferation of PCa cells. Mechanistically, we found that ABHD5 knockdown induces epithelial to mesenchymal transition, increasing aerobic glycolysis by upregulating the glycolytic enzymes hexokinase 2 and phosphofrucokinase, while decreasing mitochondrial respiration by downregulating respiratory chain complexes I and III. Interestingly, knockdown of ATGL, the best-known molecular target of ABHD5, impeded the proliferation and invasion, suggesting an ATGL-independent role of ABHD5 in modulating PCa aggressiveness. Collectively, these results provide evidence that ABHD5 acts as a metabolic tumor suppressor in PCa that prevents EMT and the Warburg effect, and indicates that ABHD5 is a potential therapeutic target against mCRPC, the deadly aggressive PCa.

Automatic marking of English compositions is a rapidly developing field in recent years. It has gradually replaced teachers’ manual reading and become an important tool to relieve the teaching burden. The existing literature shows that the error of verb consistency and the error of verb tense are the two types of grammatical errors with the highest error rate in English composition. Hence, the detection results of verb errors can reflect the practicability and effectiveness of an automatic reading system. This paper proposes an English verb’s grammar error detection algorithm based on the cyclic neural network. Since LSTM can effectively retain the valid information in the context during training, this paper decided to use LSTM to model the labeled training corpus. At the same time, how to convert the text information in English compositions into numerical values for subsequent calculation is also an important step in automatic reading. Most mainstream tools use the word bag model, i.e., each word is encoded according to the order of each word in the dictionary. Although this encoding method is simple and easy to use, it not only causes the vector to lose the sequence information of the text but also is prone to dimensional disaster. Therefore, word embedding model is adopted in this paper to encode the text, and the text information is sequentially mapped to a low-dimensional vector space. In this way, the position information of the text is not lost, and the dimensional disaster is avoided. The proposed work collects some corpus samples and compares the proposed algorithm with Jouku and Bingguo. The verification results show the superiority of the proposed algorithm in verb error detection.

This study analyzes Mandarin topic structureand the left-dislocation structure from the perspective of lexical-functional grammar (LFG) model. The study finds out that the TOP and OBJ are mapped onto the unique node of the f-structure in the topic structure, while the TOP and OBJ of the left-dislocation structure are respectively mapped onto their corresponding node. Therefore, it is easy to conclude that the topic structure are not equal with the the left-dislocation structure.