Site-Specific Profiling of N-Glycans in Drosophila melanogaster.

BACKGROUND: Drosophila melanogaster is a well-studied and highly tractable genetic model system for deciphering the molecular mechanisms underlying various biological processes. Although being one of the most critical post-translational modifications of proteins, the understanding of glycosylation in Drosophila is still lagging behind compared with that of other model organisms. METHODS: In this study, we systematically investigated the site-specific N-glycan profile of Drosophila melanogaster using intact glycopeptide analysis technique. This approach identified the glycans, proteins, and their glycosites in Drosophila, as well as information on site-specific glycosylation, which allowed us to know which glycans are attached to which glycosylation sites. RESULTS: The results showed that the majority of N-glycans in Drosophila were high-mannose type (69.3%), consistent with reports in other insects. Meanwhile, fucosylated N-glycans were also highly abundant (22.7%), and the majority of them were mono-fucosylated. In addition, 24 different sialylated glycans attached with 16 glycoproteins were identified, and these proteins were mainly associated with developmental processes. Gene ontology analysis showed that N-glycosylated proteins in Drosophila were involved in multiple biological processes, such as axon guidance, N-linked glycosylation, cell migration, cell spreading, and tissue development. Interestingly, we found that seven glycosyltransferases and four glycosidases were N-glycosylated, which suggested that N-glycans may play a regulatory role in the synthesis and degradation of N-glycans and glycoproteins. CONCLUSIONS: To our knowledge, this work represents the first comprehensive analysis of site-specific N-glycosylation in Drosophila, thereby providing new perspectives for the understanding of biological functions of glycosylation in insects.

Integration of Multiple Heterointerfaces in a Hierarchical 0D@2D@1D Structure for Lightweight, Flexible, and Hydrophobic Multifunctional Electromagnetic Protective Fabrics.

The development of wearable multifunctional electromagnetic protective fabrics with multifunctional, low cost, and high efficiency remains a challenge. Here, inspired by the unique flower branch shape of "Thunberg's meadowsweet" in nature, a nanofibrous composite membrane with hierarchical structure was constructed. Integrating sophisticated 0D@2D@1D hierarchical structures with multiple heterointerfaces can fully unleash the multifunctional application potential of composite membrane. The targeted induction method was used to precisely regulate the formation site and morphology of the metal-organic framework precursor, and intelligently integrate multiple heterostructures to enhance dielectric polarization, which improves the impedance matching and loss mechanisms of the electromagnetic wave absorbing materials. Due to the synergistic enhancement of electrospinning-derived carbon nanofiber "stems", MOF-derived carbon nanosheet "petals" and transition metal selenide nano-particle "stamens", the CoxSey/NiSe@CNSs@CNFs (CNCC) composite membrane obtains a minimum reflection loss value (RLmin) of -68.40 dB at 2.6 mm and a maximum effective absorption bandwidth (EAB) of 8.88 GHz at a thin thickness of 2.0 mm with a filling amount of only 5 wt%. In addition, the multi-component and hierarchical heterostructure endow the fibrous membrane with excellent flexibility, water resistance, thermal management, and other multifunctional properties. This work provides unique perspectives for the precise design and rational application of multifunctional fabrics.

Generative dynamic link prediction.

In networks, a link prediction task aims at learning potential relations between nodes to predict unknown potential linkage states. At present, most link prediction methods are used to process static networks. These methods cannot produce good prediction results for dynamic networks. However, for most dynamic networks in the real world, the vertices and links of these networks change over time. Dynamic link prediction (DLP) has attracted more attention as it can better mimic the evolution nature of the networks. Inspired by successful applications of the generative adversarial network in generating fake images, which are comparable with the real ones, we propose a novel generative dynamic link prediction (GDLP) method. Different from other DLP methods, we model the link prediction task as a network generation process. More specifically, GDLP utilizes the historical networks structure information to generate the network snapshot of next time stamp by an end-to-end deep generative model. This model contains a generator and a discriminator. The generator of GDLP is a spatiotemporal prediction model, which is responsible for generating the future networks based on the historical network snapshots, while the discriminator is a classification model to classify the generated networks and the ground-truth ones. With the two-player game training and learning strategy, GDLP is capable of accurate prediction for dynamic networks using the structural and temporal information. Experimental results validate that GDLP significantly outperforms several existing baseline methods on many types of dynamic networks, which improves the effectiveness of dynamic link prediction.

Adverse Long-term Metabolic and Endometrial Consequences in Women with Polycystic Ovarian Syndrome: A Report of Two Cases.

BACKGROUND: Little prospective data exist on associations between polycystic ovary syndrome (PCOS) and long-term metabolic disorders/endometrial malignancy in women. We report the cases of 2 obese, infertile women with PCOS who experienced long-term adverse metabolic and endometrial consequences. CASE: The first woman developed type 2 diabetes and hypertension after a 10-year history of PCOS and subsequent endometrioid carcinoma after having PCOS for 16 years. The second woman was first diagnosed with endometrial atypical hyperplasia, which failed to respond to high-dose progestin treatment, and then was diagnosed with PCOS after a 10-year history of oligomenorrhea. Laboratory testing revealed dyslipidemia and impaired glucose tolerance. Both women had a family history of diabetes. The second woman was administered oral contraceptives and metformin. Her endometrial atypia regressed and insulin sensitivity improved after 6 months of treatment. CONCLUSION: PCOS may be associated with long-term adverse metabolic and endometrial consequences. Timely diagnosis and appropriate monitoring and management should be emphasized in order to prevent these women from adverse long-term complications. Oral contraceptives in combination with metformin reversed endometrial atypical hyperplasia in the obese, progestin-resistant woman with PCOS. Further clinical studies may be needed to confirm the effectiveness of such treatment on endometrial atypia in obese, progestin-resistant women for fertility sparing.