Prior knowledge-based deep learning method for indoor object recognition and application

Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision

Improving "color rendering" of LED lighting for the growth of lettuce

Light plays a vital role on the growth and development of plant. On the base of white light with high color rendering to the benefit of human survival and life, we proposed to improve “color rendering” of LED lighting for accelerating the growth of lettuce. Seven spectral LED lights were adopted to irradiate the lettuces under 150 μmol·m−2·s−1 for a 16 hd−1 photoperiod. The leaf area and number profiles, plant biomass, and photosynthetic rate under the as-prepared LED light treatments were investigated. We let the absorption spectrum of fresh leaf be the emission spectrum of ideal light and then evaluate the “color rendering” of as-prepared LED lights by the Pearson product-moment correlation coefficient and CIE chromaticity coordinates. Under the irradiation of red-yellow-blue light with high correlation coefficient of 0.587, the dry weights and leaf growth rate are 2-3 times as high as the sharp red-blue light. The optimized LED light for lettuce growth can be presumed to be limited to the angle (about 75°) between the vectors passed through the ideal light in the CIE chromaticity coordinates. These findings open up a new idea to assess and find the optimized LED light for plant growth

DHAV-1 2A1 Peptide – A Newly Discovered Co-expression Tool That Mediates the Ribosomal “Skipping” Function

Duck hepatitis A virus 1 (DHAV-1) belongs to the genus Avihepatovirus in the family Picornaviridae. Little research has been carried out on the non-structural proteins of this virus. This study reports that 2A1 protein, the first non-structural protein on the DHAV-1 genome, has a ribosomal “skipping” function mediated by a “-GxExNPGP-” motif. In addition, we prove that when the sequence is extended 10aa to VP1 from the N-terminal of 2A1, the ribosome “skips” completely. However, as the N-terminus of 2A is shortened, the efficiency of ribosomal “skipping” reduces. When 2A1 is shortened to 10aa, it does not function. In addition, we demonstrate that N18, P19 G20, and P21 have vital roles in this function. We find that the expression of upstream and downstream proteins linked by 2A1 is different, and the expression of the upstream protein is much greater than that of the downstream protein. In addition, we demonstrate that it is the nature of 2A1 that is responsible for the expression imbalance. We also shows that the protein “cleavage” is not due to RNA “cleavage” or RNA transcription abnormalities, and the expressed protein level is independent of RNA transcriptional level. This study provides a systematic analysis of the activity of the DHAV-1 2A1 sequence and, therefore, adds to the “tool-box” that can be deployed for the co-expression applications. It provides a reference for how to apply 2A1 as a co-expression tool

Qingfei Xiaoyan Wan, a traditional Chinese medicine formula, ameliorates Pseudomonas aeruginosa–induced acute lung inflammation by regulation of PI3K/AKT and Ras/MAPK pathways

Gram-negative pathogen–induced nosocomial infections and resistance are a most serious menace to global public health. Qingfei Xiaoyan Wan (QF), a traditional Chinese medicine (TCM) formula, has been used clinically in China for the treatment of upper respiratory tract infections, acute or chronic bronchitis and pulmonary infection. In this study, the effects of QF on Pseudomonas aeruginosa–induced acute pneumonia in mice were evaluated. The mechanisms by which four typical anti-inflammatory ingredients from QF, arctigenin (ATG), cholic acid (CLA), chlorogenic acid (CGA) and sinapic acid (SPA), regulate anti-inflammatory signaling pathways and related targets were investigated using molecular biology and molecular docking techniques. The results showed that pretreatment with QF significantly inhibits the release of cytokines (TNF-α and IL-6) and chemokines (IL-8 and RANTES), reduces leukocytes recruitment into inflamed tissues and ameliorates pulmonary edema and necrosis. In addition, ATG was identified as the primary anti-inflammatory agent with action on the PI3K/AKT and Ras/MAPK pathways. CLA and CGA enhanced the actions of ATG and exhibited synergistic NF-κB inactivation effects possibly via the Ras/MAPK signaling pathway. Moreover, CLA is speculated to target FGFR and MEK firstly. Overall, QF regulated the PI3K/AKT and Ras/MAPK pathways to inhibit pathogenic bacterial infections effectively

Musashi-1 and miR-147 Precursor Interaction Mediates Synergistic Oncogenicity Induced by Co-Infection of Two Avian Retroviruses

Synergism between avian leukosis virus subgroup J (ALV-J) and reticuloendotheliosis virus (REV) has been reported frequently in co-infected chicken flocks. Although significant progress has been made in understanding the tumorigenesis mechanisms of ALV and REV, how these two simple oncogenic retroviruses induce synergistic oncogenicity remains unclear. In this study, we found that ALV-J and REV synergistically promoted mutual replication, suppressed cellular senescence, and activated epithelial-mesenchymal transition (EMT) in vitro. Mechanistically, structural proteins from ALV-J and REV synergistically activated the expression of Musashi-1(MSI1), which directly targeted pri-miR-147 through its RNA binding site. This inhibited the maturation of miR-147, which relieved the inhibition of NF-κB/KIAA1199/EGFR signaling, thereby suppressing cellular senescence and activating EMT. We revealed a synergistic oncogenicity mechanism induced by ALV-J and REV in vitro. The elucidation of the synergistic oncogenicity of these two simple retroviruses could help in understanding the mechanism of tumorigenesis in ALV-J and REV co-infection and help identify promising molecular targets and key obstacles for the joint control of ALV-J and REV and the development of clinical technologies

Structures and Corresponding Functions of Five Types of Picornaviral 2A Proteins

Among the few non-structural proteins encoded by the picornaviral genome, the 2A protein is particularly special, irrespective of structure or function. During the evolution of the Picornaviridae family, the 2A protein has been highly non-conserved. We believe that the 2A protein in this family can be classified into at least five distinct types according to previous studies. These five types are (A) chymotrypsin-like 2A, (B) Parechovirus-like 2A, (C) hepatitis-A-virus-like 2A, (D) Aphthovirus-like 2A, and (E) 2A sequence of the genus Cardiovirus. We carried out a phylogenetic analysis and found that there was almost no homology between each type. Subsequently, we aligned the sequences within each type and found that the functional motifs in each type are highly conserved. These different motifs perform different functions. Therefore, in this review, we introduce the structures and functions of these five types of 2As separately. Based on the structures and functions, we provide suggestions to combat picornaviruses. The complexity and diversity of the 2A protein has caused great difficulties in functional and antiviral research. In this review, researchers can find useful information on the 2A protein and thus conduct improved antiviral research

Cinnamaldehyde Enhances Antimelanoma Activity through Covalently Binding ENO1 and Exhibits a Promoting Effect with Dacarbazine

At present, melanoma is a common malignant tumor with the highest mortality rate of all types of skin cancer. Although the first option for treating melanoma is with chemicals, the effects are unsatisfactory and include poor medication response and high resistance. Therefore, developing new medicines or a novel combination approach would be a significant breakthrough. Here, we present cinnamaldehyde (CA) as a potential candidate, which exerted an antitumor effect in melanoma cell lines. Chemical biology methods of target fishing, molecular imaging, and live cell tracing by an alkynyl–CA probe revealed that the α-enolase (ENO1) protein was the target of CA. The covalent binding of CA with ENO1 changed the stability of the ENO1 protein and affected the glycolytic activity. Furthermore, our results demonstrated that dacarbazine (DTIC) showed a high promoting effect with CA for antimelanoma both in vivo and in vitro. The combination improved the DTIC cell cycle arrest in the S phase and markedly impacted melanoma growth. As a covalent inhibitor of ENO1, CA combined with DTIC may be beneficial in patients with drug resistance in antimelanoma therapy

DeepHiC: A generative adversarial network for enhancing Hi-C data resolution.

Hi-C is commonly used to study three-dimensional genome organization. However, due to the high sequencing cost and technical constraints, the resolution of most Hi-C datasets is coarse, resulting in a loss of information and biological interpretability. Here we develop DeepHiC, a generative adversarial network, to predict high-resolution Hi-C contact maps from low-coverage sequencing data. We demonstrated that DeepHiC is capable of reproducing high-resolution Hi-C data from as few as 1% downsampled reads. Empowered by adversarial training, our method can restore fine-grained details similar to those in high-resolution Hi-C matrices, boosting accuracy in chromatin loops identification and TADs detection, and outperforms the state-of-the-art methods in accuracy of prediction. Finally, application of DeepHiC to Hi-C data on mouse embryonic development can facilitate chromatin loop detection. We develop a web-based tool (DeepHiC, http://sysomics.com/deephic) that allows researchers to enhance their own Hi-C data with just a few clicks