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Identifying the sex pheromone systems of local pest populations facilitates their management, especially for moth species that show significant geographic variation in sex pheromone communication. We investigated the pheromone production and behavioral responses of the Asian corn borer (Ostrinia furnacalis Guenée; ACB) in Xinjiang, China. The ACB produces three compounds: (Z)-12-tetradecenyl acetate (Z12-14:Ac) and (E)-12-tetradecenyl acetate (E12-14:Ac) which are two sex pheromone compounds, and n-tetradecyl acetate (14:Ac) which has variable roles in mediating behavioral responses. The ratios of these three compounds produced in female gland are geographically distinct among different populations. Quantitative analysis of pheromone production showed that the proportions of Z12-14:Ac in the E/Z isomers (i.e., Z and E12-14:Ac) and the proportions of 14:Ac in the ternary blend respectively averaged 60.46% (SD=5.26) and 25.00% (SD=7.37), with their probabilities normally or near-normally distributed. Trapping experiments in a cornfield indicated that deploying the E/Z isomers and the three compounds in rubber septa close to their gland ratios yielded the most captured males, while other ratios that deviated from the gland ratios showed reduced field captures. The ternary blend was significantly more attractive to males than the E/Z isomers in the field, indicating a functional role of 14:Ac as the third pheromone component used by the local population. Additionally, the dose-response test demonstrated that the application of the three compounds at dosages between 200 and 350 μg attracted significantly more males compared to other dosages. Therefore, the characterization of this local ACB pheromone system provides additional information about its geographic variation and serves as a basis for optimizing the pheromone-mediated control of this pest in Xinjiang.

Summary: The efficacy of cisplatin in treating advanced non-small cell lung cancer is limited mainly because of insensitivity and/or acquired resistance. MAPK15, previously shown by us to enhance the sensitivity of the anti-cancer drug arsenic trioxide, could also enhance the sensitivity of other anti-cancer drugs. Here, we explore the potential role of MAPK15 in chemosensitivity to cisplatin in human lung cancer cells. Our results indicated that the expression level of MAPK15 was positively correlated with cisplatin sensitivity through affecting the DNA repair capacity of cisplatin-treated cells. The expression of MAPK15 was transcriptionally regulated by the TNF-α-activated NF-κB signaling pathway, and TNF-α synergized with cisplatin, in a MAPK15-dependent manner, to exert cytotoxicity in vitro and in vivo. Therefore, levels of TNF-α dictate the responsiveness/sensitivity of lung cancer cells to cisplatin by transcriptionally upregulating MAPK15 to enhance chemosensitivity, suggesting manipulation of MAPK15 as a strategy to improve the therapeutic efficacy of chemotherapeutic drugs.

Since the rise and rapid development of nanoscale science and technology in the late 1980s, nanomaterials have been widely used in many areas including medicine, electronic products, crafts, textiles, and cosmetics, which have provided a lot of convenience to people’s life. However, while nanomaterials have been fully utilized, their negative effects, also known as nano pollution, have become increasingly apparent. The adverse effects of nanomaterials on the environment and organisms are mainly based on the unique size and physicochemical properties of nanoparticles (NPs). NPs, as the basic unit of nanomaterials, generally refer to the ultrafine particles whose spatial scale are defined in the range of 1–100 nm. In this review, we mainly introduce the basic status of the types and applications of NPs, airborne NP pollution, and the relationship between airborne NP pollution and human diseases. There are many sources of airborne NP pollutants, including engineered nanoparticles (ENPs) and non-engineered nanoparticles (NENPs). The NENPs can be further divided into those generated from natural activities and those produced by human activities. A growing number of studies have found that exposure to airborne NP pollutants can cause a variety of illnesses, such as respiratory diseases, cardiovascular diseases, and neurological disorders. To deal with the ever increasing numbers and types of NPs being unleashed to the air, we believe that extensive research is needed to provide a comprehensive understanding of NP pollution hazards and their impact mechanisms. Only in this way can we find the best solution and truly protect the safety and quality of life of human beings.

The cause of action represents the nature of the legal relationships involved in the case.A scientific and rational choice of the cause of action will facilitate the correct application of laws and enable the courts to perform classification management of cases.Cause of action prediction aims to endow computers with the ability to automatically predict the cause category based on the textual case description.Due to the small number of the samples of low-frequency categories and the difficulty of learning effective features,previous methods usually filters out the samples of low-frequency category in data preprocessing.However,in the problem of predicting the cause of action,the key challenge is how to make an accurate prediction for the cases of low-frequency cause categories.To solve this problem,in this paper,we propose a novel hierarchical learning method based on unbalanced samples for predicting cause of action.Firstly,all causes are divided into the first-level and second-level causes according to their inherent hierarchical structure.Then,the tailed ones in second-level causes can be merged into a new first-level category with sufficient samples,and the hierarchical learning is applied to realize the prediction of cause of action.Finally,we refine the loss function to alleviate the problem of data imbalance.Experimental results show that the proposed method is significantly superior over the baseline methods,leading to an improvement of 4.81% in terms of accuracy.Also,we verify the benefits of introducing the hierarchical learning as well as refining the loss function for unbalanced data.