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Synthetic hormones in wastewater effluents released into the aquatic environments may interfere with the normal endocrine systems of fish in receiving streams. Norgestrel (NGT) is a synthetic progestin widely used in oral contraceptives and frequently detected in wastewater effluents. In this study, adult female mosquitofish (Gambusia affinis) were exposed to three environmentally relevant concentrations of norgestrel (NGT) (i.e., 3.6, 35.8, and 368.0 ng L-1) for 42 d, fin morphology, histology of the ovary, and reproductive behaviors were evaluated. The results showed that NGT at all three concentrations caused an increased frequency of atretic follicular cells in ovaries and impaired mating behaviors exhibited by males toward the NGT-exposed females. In mosquitofish exposed to NGT at 35.8 and 368 ng L-1, the anal fin of females had an increased length ratio of ray4/ray 6, an increased width of ray 3, and increased number of segments in ray 3. The histopathological analysis showed that exposure to NGT increased the incidence of spermatogenesis in ovaries. Mating behavior was impaired 58.4%, 65.7%, and 76.4% (P

Synthesis and control strategy of colloidal nanocrystals are mainly concerned with dynamic factors. Generally, theory of nucleation of liquid phase colloids and crystal growth are considered. Firstly, the strategies on colloidal nanocrystalline synthesis and morphology control at the stage of nucleation, growth and ripening were reviewed in this paper. Secondly, selective-adsorption mechanism, effective-monomer mechanism and oriented-attachment mechanisms were briefly introduced for the phenomena that can not be explained by classical crystal growth theory. It was also introduced briefly the joint action of some new mechanisms or multiple mechanisms on synthesis of nano-materials in recent years. Finally, perspectives on future development of nanocrystalline synthesis and morphology control were presented. It is believed that quantitative and precise structural control is of great importance and will be a huge challenge and development trend in nanocrystalline synthesis and morphology control.

Carbon solely can form a lot of nanostructures, such as zero-dimensional nanosphere, one-dimensional nanotube and two-dimensional graphene. They perform differently in Li-ion and Li-S batteries. It is worth noting that CNTs and graphene are not appropriate to be used as electroactive materials for Li-ion or Li-S batteries for four reasons. First, when CNTs and graphene are used as an anode, they often exhibit high specific capacities during the first lithiation step, but a large fraction of lithium ions is irreversibly consumed instead of reversibly stored, leading to a low Coulombic efficiency of the cell. Second, a graphene-based anode has a large voltage hysteresis in the charge/discharge curves. Third, it has been reported a CNT-based anode lacks a steady voltage plateau with large change in voltage during discharge. Fourth, despite their high initial capacities, graphene and CNT-based anodes often suffer from fast capacity decay after a few tens of cycles. Continuous efforts have been made to build better lithium batteries with a higher energy density and wider applicability, including both current state-of-the-art Li-ion batteries and near-term Li-S batteries. Because the behavior of a rechargeable battery is mainly based on the performance of its anode and cathode, designing advanced electrode materials as well as electrode with tailored compositions and structures has been the hot topic in recent years. The role of carbon nano-materials to construct electrode materials and tailored electrodes in Li-ion and Li-S batteries in high performance was reviewed in the paper from three aspects. Firstly, the role of carbon nano-materials in modifying the electroactive materials was discussed from three aspects:electron- and ion-transport facilitators, immobilization sites and volume expansion buffering. Secondly, the role of carbon nano-materials in optimizing the inactive components was considered as follows:conducting additives, current collectors and conductive interlayers. Thirdly, the role of carbon nano-materials in designing the bendable and stretchable devices are discussed from three aspects:conductive phases in nonconductive substrates, flexible current collectors and freestanding composite electrode. Finally, perspectives on future development of Li-ion and Li-S batteries were presented. It is considered that multi-functional carbon nano-materials will be main research focus in the future.

Susceptibility to domain changes for image classification hinders the application and development of deep neural networks. Domain adaptation (DA) makes use of domain-invariant characteristics to improve the performance of a model trained on labeled data from one domain (source domain) on an unlabeled domain (target) with a different data distribution. But existing DA methods simply use pretrained models (e.g., AlexNet, ResNet) for feature extraction, which are convolutional models that are trapped in localized features and fail to acquire long-distance dependencies. Furthermore, many approaches depend too much on pseudo-labels, which can impair adaptation efficiency and lead to unstable and inconsistent results. In this research, we present S2AC, a novel approach for unsupervised deep domain adaptation, that makes use of a stacked attention architecture as a feature map extractor. Our method can fuse domain discrepancy with minimizing a linear transformation of the second statistics (covariances) extended by the p-norm, while simultaneously designing pretext tasks on heuristics to improve the generality of the learning representation. In addition, we have developed a new trainable relative position embedding that not only reduces the model parameters but also enhances model accuracy and expedites the training process. To illustrate our method's efficacy and controllability, we designed extensive experiments based on the Office31, Office_Caltech_10, and OfficeHome datasets. To the best of our knowledge, the proposed method is the first attempt at incorporating attention-based networks and self-supervised learning for image domain adaptation, and has shown promising results. [ABSTRACT FROM AUTHOR]

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