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We report a draft sequence for the genome of the domesticated silkworm (Bombyx mori), covering 90.9% of all known silkworm genes. Our estimated gene count is 18,510, which exceeds the 13,379 genes reported for Drosophila melanogaster. Comparative analyses to fruitfly, mosquito, spider, and butterfly reveal both similarities and differences in gene content.

Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.

SHP-1 is a cytosolic protein-tyrosine phosphatase that behaves as a negative regulator in eukaryotic cellular signaling pathways. To understand its regulatory mechanism, we have determined the crystal structure of the C-terminal truncated human SHP-1 in the inactive conformation at 2.8-Å resolution and refined the structure to a crystallographic R-factor of 24.0%. The three-dimensional structure shows that the ligand-free SHP-1 has an auto-inhibited conformation. Its N-SH2 domain blocks the catalytic domain and keeps the enzyme in the inactive conformation, which supports that the phosphatase activity of SHP-1 is primarily regulated by the N-SH2 domain. In addition, the C-SH2 domain of SHP-1 has a different orientation from and is more flexible than that of SHP-2, which enables us to propose an enzymatic activation mechanism in which the C-SH2 domains of SHPs could be involved in searching for phosphotyrosine activators.Tyrosine phosphorylation is a key mechanism for regulating eukaryotic cellular signaling pathways. The protein tyrosine phosphorylation level is precisely regulated by two types of enzymes: protein-tyrosine kinases (PTKs) 1 and protein-tyrosine phosphatases (PTPs), in which PTPs act to counter-balance the process through dephosphorylation of the phosphorylated tyrosines (1, 2). PTPs can be divided into two groups, receptor protein-tyrosine phosphatases and cytosolic proteintyrosine phosphatases. The SH2 domain-containing PTPs, SHP-1 and SHP-2, are both cytosolic PTPs and share many structural and regulatory features. They both have two tandem SH2 domains at the N terminus followed by a single catalytic domain and an inhibitory C-terminal tail. However, irrespective of similar structural and regulatory characteristics, these two enzymes have different biological function in vivo.Different from SHP-2, which is expressed in all kinds of tissues, SHP-1 is predominantly expressed in hematopoietic and epithelial cells and behaves mainly as a negative regulator of signaling pathways in lymphocytes (1, 2). SHP-1 is dormant in the cytosol, with its phosphatase activity inhibited by both the SH2 domains and the C-terminal tail (1,(3)(4)(5). In response to an activation signal, SHP-1 is recruited to membrane-bound inhibitory receptors via the binding of its SH2 domains to the tyrosine-phosphorylated immunoreceptor tyrosine-based inhibitory motif within the cytoplasmic domain of a receptor (6 -8). During this process, SHP-1 undergoes a structural rearrangement, exposes its active site, and binds to the downstream substrates, thereby dephosphorylating the substrates to turn off the cellular signals.SHP-1 also presents in several types of non-hematopoietic cells (9 -12). Overexpression of a catalytically inactive SHP-1 mutant in these cells strongly suppressed mitogen-activated pathways, reducing signal transduction and activation of transcription; these findings demonstrate that SHP-1 has a positive effect on mitogenic signaling in these non-hematopoietic cells (10, 11). Thus, SHP-1 probably has both the nega...

Alternaria leaf spot, Brown spot, Mosaic, Grey spot, and Rust are five common types of apple leaf diseases that severely affect apple yield. However, the existing research lacks an accurate and fast detector of apple diseases for ensuring the healthy development of the apple industry. This paper proposes a deep learning approach that is based on improved convolutional neural networks (CNNs) for the real-time detection of apple leaf diseases. In this paper, the apple leaf disease dataset (ALDD), which is composed of laboratory images and complex images under real field conditions, is first constructed via data augmentation and image annotation technologies. Based on this, a new apple leaf disease detection model that uses deep-CNNs is proposed by introducing the GoogLeNet Inception structure and Rainbow concatenation. Finally, under the hold-out testing dataset, using a dataset of 26,377 images of diseased apple leaves, the proposed INAR-SSD (SSD with Inception module and Rainbow concatenation) model is trained to detect these five common apple leaf diseases. The experimental results show that the INAR-SSD model realizes a detection performance of 78.80% mAP on ALDD, with a high-detection speed of 23.13 FPS. The results demonstrate that the novel INAR-SSD model provides a high-performance solution for the early diagnosis of apple leaf diseases that can perform real-time detection of these diseases with higher accuracy and faster detection speed than previous methods.INDEX TERMS Apple leaf diseases, real-time detection, deep learning, convolutional neural networks, feature fusion.