Vitellogenin receptor (VgR) plays a pivotal role in ovarian vitellogenin (Vg) uptake and vertical transmission of pathogenic microbes and Wolbachia symbionts. However, the regulatory mechanisms of VgR action as an endocytic receptor and translocation from oocyte cytoplasm to the membrane remain poorly understood. Here, by using the migratory locust Locusta migratoria as a model system, we report that juvenile hormone (JH) promotes VgR phosphorylation at Ser1361 in the second EGF-precursor homology domain. A signaling cascade including GPCR, PLC, extracellular calcium, and PKC-ι is involved in JH-stimulated VgR phosphorylation. This posttranslational regulation is a prerequisite for VgR binding to Vg on the external surface of the oocyte membrane and subsequent VgR/Vg endocytosis. Acidification, a condition in endosomes, induces VgR dephosphorylation along with the dissociation of Vg from VgR. Phosphorylation modification is also required for VgR recycling from oocyte cytoplasm to the membrane. Additionally, VgR phosphorylation and its requirement for Vg uptake and VgR recycling are evolutionarily conserved in other representative insects including the cockroach Periplaneta americana and the cotton bollworm Helicoverpa armigera. This study fills an important knowledge gap of low-density lipoprotein receptors in posttranslational regulation, endocytosis, and intracellular recycling.

Botnets often use domain generation algorithms (DGA) to connect to a command and control (C2) server, which enables the compromised hosts connect to the C2 server for accessing many domains. The detection of DGA domains is critical for blocking the C2 server, and for identifying the compromised hosts as well. However, the detection is difficult, because some DGA domain names look normal. Much of the previous work based on statistical analysis of machine learning relies on manual features and contextual information, which causes long response time and cannot be used for realtime detection. In addition, when a new family of DGA appears, the classifier has to be retrained from the very beginning. This paper presents a deep learning approach based on bidirectional long short-term memory (Bi-LSTM) model for DGA domain detection. The classifier can extract features without the need for manual feature extraction, and the trainable model can effectively deal with new unknown DGA family members. In addition, the proposed model only needs the domain name without any additional context information. All domain names are preprocessed by bigram and the length of each processed domain name is set as a value longer than the most samples. Bidirectional LSTM model receives the encoded data and returns labels to check whether domain names are normal or not. Experiments show that our model outperforms state-of-the-art approaches and is able to detect new DGA families reliably.

No abstract

No abstract