Transcriptome Analysis of the Oriental Fruit Fly (Bactrocera dorsalis)

The oriental fruit fly, Bactrocera dorsalis (Hendel), is one of the most economically important pests in the world, causing serious damage to fruit production. However, lack of genetic information on this organism is an obstacle to understanding the mechanisms behind its development and its ability to resist insecticides. Analysis of the B. dorsalis transcriptome and its expression profile data is essential to extending the genetic information resources on this species, providing a shortcut that will support studies on B. dorsalis.We performed de novo assembly of a transcriptome using short read sequencing technology (Illumina). The results generated 484,628 contigs, 70,640 scaffolds, and 49,804 unigenes. Of those unigenes, 27,455 (55.13%) matched known proteins in the NCBI database, as determined by BLAST search. Clusters of orthologous groups (COG), gene orthology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations were performed to better understand the functions of these unigenes. Genes related to insecticide resistance were analyzed in additional detail. Digital gene expression (DGE) libraries showed differences in gene expression profiles at different developmental stages (eggs, third-instar larvae, pupae, and adults). To confirm the DGE results, the expression profiles of six randomly selected genes were analyzed.This transcriptome greatly improves our genetic understanding of B. dorsalis and makes a huge number of gene sequences available for further study, including both genes of known importance and genes of unknown function. The DGE data provide comprehensive insight into gene expression profiles at different developmental stages. This facilitates the study of the role of each gene in the developmental process and in insecticide resistance

Preparation and in vitro/in vivo characterization of enteric-coated nanoparticles loaded with the antihypertensive peptide VLPVPR

Haiyan Sun, Dong Liu, Yan Li, Xuwei Tang, Yanli Cong Shenzhen Key Laboratory of Fermentation, Purification and Analysis, Shenzhen Polytechnic, Guangdong, People's Republic of China Abstract: Our previous study revealed that the peptide Val-Leu-Pro-Val-Pro-Arg (VLPVPR), which was prepared using deoxyribonucleic acid recombinant technology, effectively decreased the blood pressure of spontaneous hypertensive rats; however, the effect only lasts 6 hours, likely due to its low absorption in the gastrointestinal tract. To overcome this problem, the purpose of this study was to characterize (methoxy-polyethylene glycol)-b-poly(D,L-lactide-co-glycolide)-b-poly(L-lysine) nanoparticles as in vitro and in vivo carriers for the effective delivery of VLPVPR. In our study, the VLPVPR nanoparticles were prepared using a double emulsion method, coated with Eudragit S100, and freeze-dried to produce enteric-coated nanoparticles. The optimized parameters from the double emulsion method was obtained from orthogonal experiments, including drug loading (DL) and encapsulated ratio (ER) at 6.12% and 86.94%, respectively, and the average particle size was below 100 nm. The release experiment demonstrated that the nanoparticles were sensitive to pH: almost completely released at pH 7.4 after 8 hours, but demonstrated much less release at pH 4.5 or pH 1.0 in the same amount of time. Therefore, the nanoparticles are suitable for enteric release. In vivo compared with the untreated group, the medium and high doses of orally administered VLPVPR nanoparticles reduced blood pressure for more than 30 hours, demonstrating that these nanoparticles have long-lasting and significant antihypertensive effects in spontaneously hypertensive rats. Keywords: mPEG-PLGA-PLL, in vivo studies, Val-Leu-Pro-Val-Pro-Arg peptide, enteric-coated, nanoparticle, antihypertensive peptid

Active Lifelong Learning with “Watchdog”

Lifelong learning intends to learn new consecutive tasks depending on previously accumulated experiences, i.e., knowledge library. However, the knowledge among different new coming tasks are imbalance. Therefore, in this paper, we try to mimic an effective “human cognition” strategy by actively sorting the importance of new tasks in the process of unknown-to-known and selecting to learn the important tasks with more information preferentially. To achieve this, we consider to assess the importance of the new coming task, i.e., unknown or not, as an outlier detection issue, and design a hierarchical dictionary learning model consisting of two-level task descriptors to sparse reconstruct each task with the _0 norm constraint. The new coming tasks are sorted depending on the sparse reconstruction score in descending order, and the task with high reconstruction score will be permitted to pass, where this mechanism is called as “watchdog”. Next, the knowledge library of the lifelong learning framework encode the selected task by transferring previous knowledge, and then can also update itself with knowledge from both previously learned task and current task automatically. For model optimization, the alternating direction method is employed to solve our model and converges to a fixed point. Extensive experiments on both benchmark datasets and our own dataset demonstrate the effectiveness of our proposed model especially in task selection and dictionary learning

CSCL: Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation

Unsupervised domain adaptation without consuming annotation process for unlabeled target data attracts appealing interests in semantic segmentation. However, 1) existing methods neglect that not all semantic representations across domains are transferable, which cripples domain-wise transfer with untransferable knowledge; 2) they fail to narrow category-wise distribution shift due to category-agnostic feature alignment. To address above challenges, we develop a new Critical Semantic-Consistent Learning (CSCL) model, which mitigates the discrepancy of both domain-wise and category-wise distributions. Specifically, a critical transfer based adversarial framework is designed to highlight transferable domain-wise knowledge while neglecting untransferable knowledge. Transferability-critic guides transferability-quantizer to maximize positive transfer gain under reinforcement learning manner, although negative transfer of untransferable knowledge occurs. Meanwhile, with the help of confidence-guided pseudo labels generator of target samples, a symmetric soft divergence loss is presented to explore inter-class relationships and facilitate category-wise distribution alignment. Experiments on several datasets demonstrate the superiority of our model. © 2020, Springer Nature Switzerland AG

Far-field and beam characteristics of vertical-cavity surface-emitting lasers

We have determined the far-field patterns and beam parameters of vertical-cavity surface-emitting lasers (VCSELs) with different structures. The results show that the window diameter and the active-layer aperture of VCSELs strongly influence laser far-field distributions and beam characteristics; for VCSELs with small window omega=5 mu m, only one dominant lobe has been observed in the far-field profiles, even though injected current was increased up to 2 Ith; and the smaller the ratio of the window diameter to the active-layer aperture, the larger is the far-field divergence. The laser structure dependence of the K factor has also been studied. (C) 1996 American Institute of Physics