A novel method for gas mixing and distribution in multi-chamber embryo incubators.

BACKGROUND: High-quality control of the gas environment in incubators is crucial for in vitro embryo development, which requires high accuracy, fast recovery, and low gas consumption. OBJECTIVE: In this study, we propose a novel gas mixing and distribution system and method as an alternative solution for multi-chamber embryo incubators. METHODS: The system-based embryo incubator enables a controllable gas circulation process and a quantitative supply of CO2 and N2. To determine the optimal parameters for the mixing time and flow rate of the circulated gases, we conducted contrast experiments on the system-based incubator. To evaluate the performance of the gas system in the incubator, we conducted tests under four different initial conditions, simulating various practical application scenarios. Furthermore, we performed a mouse embryo assay to assess the system's effectiveness. RESULTS: The results show that the system achieved a gas concentration accuracy of ± 0.2% (volume fraction) after stabilization, a minimum recovery time of 5 minutes, an average consumption of 8.9 L/d for N2 and 0.83 L/d for CO2 during routine operation, and a blastocyst rate exceeding 90% observed after 96 hours of culture in the incubator. CONCLUSION: The system and method demonstrate a significant advantage in terms of low gas consumption compared to existing incubators, while still maintaining high accuracy and fast recovery.

Inactivation of pathogenic microorganisms in water by electron beam excitation multi-wavelength ultraviolet irradiation: Efficiency, influence factors and mechanism.

Due to the limitations of traditional ultraviolet (UV) in microbial inactivation in water, it is necessary to explore a more suitable and efficient UV disinfection method. In this study, an electron beam excitation multi-wavelength ultraviolet (EBE-MW-UV) system was established and aims to analyze its differential microbial inactivation capabilities in comparison to single-wavelength UV-LEDs in waterborne applications. Furthermore, the inactivation mechanisms of this system on microorganisms were explored. The results showed that EBE-MW-UV had significantly higher inactivation effects on the Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis and Candida albicans in water compared to UV-LEDs (p＜0.05), and the inactivation effect of EBE-MW-UV on Escherichia coli and Pseudomonas aeruginosa at the same UV dose was 3.8 and 1.9 log higher than that of UV-LEDs, respectively, EBE-MW-UV exhibited better inactivation effects on Gram-negative bacteria. Further research found that, under the majority of irradiation doses, neither EBE-MW-UV nor UV-LEDs were significantly affected by the concentration of suspended solids (5 and 20 mg/L) or humic acids (2 and 5 mg/L) in the water. Mechanism analysis revealed that during the disinfection process of EBE-MW-UV, microbial DNA and proteins were initially damaged, which prevented the occurrence of dark repair and led to bacterial inactivation. In addition, UV irradiation led to the production of additional reactive oxygen species (ROS) inside the cells, increasing cell membrane permeability and exacerbating membrane damage. This was accompanied by a decrease in energy metabolism and depletion of ATP, ultimately resulting in microbial inactivation. Therefore, EBE-MW-UV demonstrated more effective disinfection than single-wavelength UV-LEDs, showing great potential. Our research gives new insights into the characteristics of multiple wavelength ultraviolet, and provides scientific basis for the selection of new light sources in the field of ultraviolet disinfection.

Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators.

By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.