Fish Oil Intake During Gestation and Lactation Attenuated STZ-Induced Diabetes inMale Offspring via Activation of Brown Fat and Modulating Oxylipin Profile

Fish oil (FO) has been demonstrated to activate brown thermogenesis and attenuate inflammation in the brown adipose tissue (BAT). Previously, we have reported thatmaternal FO supplementation promotes BAT activity of the weaned mice pups. However, whether maternal FO intake could confer sustainable metabolic benefits to offspring remains uncovered. Therefore, this study aimed to determine the differential impact of maternal FO during pregnancy versus lactation on BAT transcriptome and evaluate the role of bioactive lipid metabolites derived from maternal FO supplementation on the extended metabolic benefits of older pups in the context of type 1 diabetes (T1D). Conclusions: Our results suggested that maternal FO intake in pregnancy and lactation, at least partly, protects against the risk of T1D of the offspring through augmented BAT function and antiinflammatory oxylipin production

Elimination of degenerate trajectory of single atom strongly coupled to the tilted cavity TEM10 mode

We demonstrate the trajectory measurement of the single neutral atoms deterministically using a high-finesse optical micro-cavity. Single atom strongly couples to the high-order transverse vacuum TEM_{10} mode, instead of the usual TEM_{00} mode, and the parameter of the system is (g_{10},\kappa ,\gamma )=2\pi \times (20.5,2.6,2.6)MHz. The atoms simply fall down freely from the magneto-optic trap into the cavity modes and the trajectories of the single atoms are linear. The transmission spectrums of atoms passing through the TEM10 mode are detected by a single photon counting modules and well fitted. Thanks to the tilted cavity transverse TEM10 mode, which is inclined to the vertical direction about 45 degrees and it helps us, for the first time, to eliminate the degenerate trajectory of the single atom falling through the cavity and get the unique atom trajectory. Atom position with high precision of 0.1{\mu}m in the off-axis direction (axis y) is obtained, and the spatial resolution of 5.6{\mu}m is achieved in time of 10{\mu}s along the vertical direction (axis x). The average velocity of the atoms is also measured from the atom transits, which determines the temperature of the atoms in magneto-optic trap, 186{\mu}K {\pm} 19{\mu}K.Comment: 13 pages, 5figure

Anti-endometriosis Mechanism of Jiawei Foshou San Based on Network Pharmacology

Jiawei Foshou San (JFS) is the new formula originated from classic Foshou San formula, composed with ligustrazine, ferulic acid, and tetrahydropalmatine. Previously JFS inhibited the growth of endometriosis (EMS) with unclear mechanism, especially in metastasis, invasion, and epithelial–mesenchymal transition. In this study, network pharmacology was performed to explore potential mechanism of JFS on EMS. Through compound–compound target and compound target–EMS target networks, key targets were analyzed for pathway enrichment. MMP–TIMP were uncovered as one cluster of the core targets. Furthermore, autologous transplantation of EMS rat’s model were used to evaluate in vivo effect of JFS on invasion, metastasis and epithelial–mesenchymal transition. JFS significantly suppressed the growth, and reduced the volume of ectopic endometrium, with modification of pathologic structure. In-depth study, invasion and metastasis were restrained after treating with JFS through decreasing MMP-2 and MMP-9, increasing TIMP-1. Meanwhile, JFS promoted E-cadherin, and attenuated N-cadherin, Vimentin, Snail, Slug, ZEB1, ZEB2, Twist. In brief, anti-EMS effect of JFS might be related to the regulation of epithelial–mesenchymal transformation, thereby inhibition of invasion and metastasis. These findings reveal the potential mechanism of JFS on EMS and the benefit for further evaluation

Effects of AI-Generated Content (AIGC) in the Game Development : From traditional PCG to AIGC

This paper aims to investigate the effect of AI-generated content (AIGC) when it starts to be applied in game development. AIGC in games refers to the generation of game content through artificial intelligence, a concept that has recently recieved a high level of attention due to the latest rapid developments in artificial intelligence, and in traditional research, AIGC can be categorized as an advanced approach to Procedural Content Generation (PCG), i.e., Deep Learning Method. Procedural Content Generation is the creation of game content through algorithms with limited or indirect user input. Its traditional approach has been widely used in games. Recently, however, the AIGC method has also started to be used by a large number of game companies, and its impact has exceeded expectations. A questionnaire survey of 40 game developers revealed a general interest in AIGC but also concerns. Further interviews explored the use of AIGC in game development and some of the problems it has encountered and predicted future trends in its development. The result of this study provide guidance on whether and how AIGC needs to be used in future game development

DEVELOPMENT OF NOVEL MINIATURIZED SAMPLE PREPARATION PROCEDURES WITH ULTRA-HIGH PERFORMANCE LIQUID CHROMATOGRAPHY-TANDEM MASS SPECTROMETRY FOR WATER ANALYSIS

Master'sMASTER OF SCIENCE (RSH-FOS

Effects of AI-Generated Content (AIGC) in the Game Development : From traditional PCG to AIGC

This paper aims to investigate the effect of AI-generated content (AIGC) when it starts to be applied in game development. AIGC in games refers to the generation of game content through artificial intelligence, a concept that has recently recieved a high level of attention due to the latest rapid developments in artificial intelligence, and in traditional research, AIGC can be categorized as an advanced approach to Procedural Content Generation (PCG), i.e., Deep Learning Method. Procedural Content Generation is the creation of game content through algorithms with limited or indirect user input. Its traditional approach has been widely used in games. Recently, however, the AIGC method has also started to be used by a large number of game companies, and its impact has exceeded expectations. A questionnaire survey of 40 game developers revealed a general interest in AIGC but also concerns. Further interviews explored the use of AIGC in game development and some of the problems it has encountered and predicted future trends in its development. The result of this study provide guidance on whether and how AIGC needs to be used in future game development

Use Reinforcement Learning to Generate Testing Commands for Onboard Software of Small Satellites

Programmers usually write test cases to test onboard software. However, this procedure is time-consuming and needs sufficient prior knowledge. As a result, small satellite developers may not be able to test the software thoroughly. A promising direction to solve this problem is reinforcement learning (RL) based testing. It searches testing commands to maximise the return, which represents the testing goal. Testers need not specify prior knowledge besides the reward function and hyperparameters. Reinforcement learning has matured in software testing scenarios, such as GUI testing. However, migration from such scenarios to onboard software testing is still challenging because of different environments. This work is the first research to apply reinforcement learning in real onboard software testing and one of few studies that perform RL-based testing on embedded software without a GUI. In this work, the RL agent observes current code coverage and the interaction history, selects a pre-defined command, or organises a command from pre-defined parameters to maximise cumulative reward. The reward function can be code coverage (coverage testing) or estimated CPU load (stress testing). Three RL algorithms, including the tabular Q-Learning, Double Duelling Deep Q Network (D3QN), and Proximal Policy Optimization (PPO), are compared with a random testing baseline and a genetic algorithm baseline in the experiments. This study also performs regression testing with a trained RL agent, i.e., to test a version of onboard software that it has never seen before. To do that, the agent processes graph input with code coverage information. The graph is extracted from the onboard software source code via static code analysis. The work tries two graph neural network architectures (GGNN and GAT) with several graph pooling mechanisms to process the graph input. Apart from the test command generation algorithms, some middleware is also implemented, including a command/response parser, a state identification module, a branch coverage collection tool, and a tool to extract the graph representation and node features. During onboard software testing, the onboard computer (OBC) or the electrical group support equipment (EGSE) can be the master of the bus. The command generation algorithms can run on a lab PC or a cloud server. The research reveals the advantages and drawbacks of using reinforcement learning to test onboard software. On the one hand, RL-based testing performs well in non-deterministic environments (e.g., stress testing) and regression testing. On the other hand, more straightforward methods like random testing and the genetic algorithm are more useful in deterministic environments. This document also introduces relative background knowledge. It leaves many recommendations for future work, such as improving sampling efficiency, generalization, and learning a model for fault detection in satellite operation.https://github.com/StarCycle/TestCommandGeneration Testing command generation algorithms https://github.com/StarCycle/CodeCoverage Code Coverage Collection and Analysing Tool https://github.com/StarCycle/GraphExtract Graph Representation Extraction ToolDelfi-PocketQubeAerospace Engineerin

A note on the genus <i>Rhagastis</i> from China with a new record species (Lepidoptera: Sphingidae)

The genus Rhagastis Rothschild & Jordan,1903 is represented in China by 9 species.The authors collected a series specimens of family Sphingidae during the expedition to Guizhou Province (Maolan Natural Reserve,Libo) in early 2017 and Hainan Province (Jianfengling National Forest Park,Ledong) in early 2018.After examining specimens of the Macroglossini Harris,1839,a few specimens were identified as Rhagastis acuta Walker,1856,a new record from China.This article described and illustrated the male adult of R.acuta and its genitalia,with full species account of genus Rhagastis from China

Performance and security enhancement solutions for positron emission tomography medical hardware

Positron Emission Tomography (PET) is an emerging medical imaging methodology for diagnosing cancer. The optimization and security solutions surrounding this technology are essential issues in biomedical engineering. Low-resolution and unauthorized modification of medical images will affect clinical analysis and medical diagnostics. To improve image quality and security while minimizing its impact on medical hardware, this paper analyzes a highly integrated data acquisition approach based on Time-over-Threshold (ToT) and proposes a lightweight security solution based on Physically Unclonable Function (PUF) for PET scan medical hardware. Compared to existing applications, the time-based sampling method can provide very good image quality, and the proposed watermarking and encryption method based on PUF enables enhanced privacy protection with fewer hardware costs for PET medical imaging technology