In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network's ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.
Humans ; Colonic Polyps/diagnostic imaging* ; Computer Simulation ; Electric Power Supplies ; Semantics ; Image Processing, Computer-Assisted

Macaque is a common animal model in drug safety assessment. Its behavior reflects its health condition before and after drug administration, which can effectively reveal the side effects of drugs. At present, researchers usually rely on artificial methods to observe the behavior of macaque, which cannot achieve uninterrupted 24-hour monitoring. Therefore, it is urgent to develop a system to realize 24-hour observation and recognition of macaque behavior. In order to solve this problem, this paper constructs a video dataset containing nine kinds of macaque behaviors (MBVD-9), and proposes a network called Transformer-augmented SlowFast for macaque behavior recognition (TAS-MBR) based on this dataset. Specifically, the TAS-MBR network converts the red, green and blue (RGB) color mode frame input by its fast branches into residual frames on the basis of SlowFast network and introduces the Transformer module after the convolution operation to obtain sports information more effectively. The results show that the average classification accuracy of TAS-MBR network for macaque behavior is 94.53%, which is significantly improved compared with the original SlowFast network, proving the effectiveness and superiority of the proposed method in macaque behavior recognition. This work provides a new idea for the continuous observation and recognition of the behavior of macaque, and lays the technical foundation for the calculation of monkey behaviors before and after medication in drug safety evaluation.
Animals ; Electric Power Supplies ; Macaca ; Recognition, Psychology

The synergistic effect of drug combinations can solve the problem of acquired resistance to single drug therapy and has great potential for the treatment of complex diseases such as cancer. In this study, to explore the impact of interactions between different drug molecules on the effect of anticancer drugs, we proposed a Transformer-based deep learning prediction model-SMILESynergy. First, the drug text data-simplified molecular input line entry system (SMILES) were used to represent the drug molecules, and drug molecule isomers were generated through SMILES Enumeration for data augmentation. Then, the attention mechanism in the Transformer was used to encode and decode the drug molecules after data augmentation, and finally, a multi-layer perceptron (MLP) was connected to obtain the synergy value of the drugs. Experimental results showed that our model had a mean squared error of 51.34 in regression analysis, an accuracy of 0.97 in classification analysis, and better predictive performance than the DeepSynergy and MulinputSynergy models. SMILESynergy offers improved predictive performance to assist researchers in rapidly screening optimal drug combinations to improve cancer treatment outcomes.
Electric Power Supplies ; Neural Networks, Computer ; Antineoplastic Agents/pharmacology*

Child ; Humans ; Infant ; Esophagus ; Electric Power Supplies ; Eating ; Foreign Bodies

Facing the increasingly severe energy shortage and environmental pollution, electrocatalytic processes using electroactive microorganisms provide a new alternative for achieving environmental-friendly production. Because of its unique respiratory mode and electron transfer ability, Shewanella oneidensis MR-1 has been widely used in the fields of microbial fuel cell, bioelectrosynthesis of value-added chemicals, metal waste treatment and environmental remediation system. The electrochemically active biofilm of S. oneidensis MR-1 is an excellent carrier for transferring the electrons of the electroactive microorganisms. The formation of electrochemically active biofilm is a dynamic and complex process, which is affected by many factors, such as electrode materials, culture conditions, strains and their metabolism. The electrochemically active biofilm plays a very important role in enhancing bacterial environmental stress resistance, improving nutrient uptake and electron transfer efficiency. This paper reviewed the formation process, influencing factors and applications of S. oneidensis MR-1 biofilm in bio-energy, bioremediation and biosensing, with the aim to facilitate and expand its further application.
Bioelectric Energy Sources/microbiology* ; Biofilms ; Electrodes ; Electron Transport ; Shewanella/metabolism*

Electrocardiogram (ECG) signal is an important basis for the diagnosis of arrhythmia and myocardial infarction. In order to further improve the classification effect of arrhythmia and myocardial infarction, an ECG classification algorithm based on Convolutional vision Transformer (CvT) and multimodal image fusion was proposed. Through Gramian summation angular field (GASF), Gramian difference angular field (GADF) and recurrence plot (RP), the one-dimensional ECG signal was converted into three different modes of two-dimensional images, and fused into a multimodal fusion image containing more features. The CvT-13 model could take into account local and global information when processing the fused image, thus effectively improving the classification performance. On the MIT-BIH arrhythmia dataset and the PTB myocardial infarction dataset, the algorithm achieved a combined accuracy of 99.9% for the classification of five arrhythmias and 99.8% for the classification of myocardial infarction. The experiments show that the high-precision computer-assisted intelligent classification method is superior and can effectively improve the diagnostic efficiency of arrhythmia as well as myocardial infarction and other cardiac diseases.
Humans ; Electrocardiography ; Heart Diseases ; Myocardial Infarction/diagnostic imaging* ; Algorithms ; Electric Power Supplies

This study introduced a time-delay exposure system independent of the mobile digital radiography equipment. The system consisted of lithium battery, delay control circuit, micro electric motor and related auxiliary facilities. When the starting time was reached through the delay circuit, the motor pushed out the rod to squeeze the exposure button and completed the exposure. The accessories used in this system were easy to purchase and cheap. At the same time, the technology was mature and had good compatibility. The exposure success rate was high and the exposure effect was satisfactory. This time-delay exposure system had good practicability and popularization value.
Radiographic Image Enhancement ; Technology ; Electric Power Supplies

Power supply plays a key role in ensuring animal robots to obtain effective stimulation. To extending the stimulating time, there is a need to apply photovoltaic cells and monitor their parameter variations, which can help operators to obtain the optimal stimulation strategy. In this paper, an online monitoring system of photovoltaic cells for animal robot stimulators was presented. It was composed of battery information sampling circuit, multi-channel neural signal generator, power module and human-computer interaction interface. When the signal generator was working, remote navigation control of animal robot could be achieved, and the battery voltage, current, temperature and electricity information was collected through the battery information sampling circuit and displayed on the human-computer interaction system in real time. If there was any abnormal status, alarm would be activated. The battery parameters were obtained by charging and discharging test. The battery life under different light intensity and the stimulation effect of neural signal generator were tested. Results showed that the sampling errors of battery voltage, current and electric quantity were less than 15 mV, 5 mA and 6 mAh, respectively. Compared with the system without photovoltaic cells, the battery life was extended by 148% at the light intensity of 78 320 lx, solving the battery life problem to some extent. When animal robot was stimulated with this system, left and right turns could be controlled to complete with the success rate more than 80%. It will help researchers to optimize animal robot control strategies through the parameters obtained in this system.
Animals ; Humans ; Robotics ; Electric Power Supplies ; Electricity ; User-Computer Interface

This study introduces a portable multi-channel EEG signal acquisition system. The system is mainly composed of EEG electrode connector, signal conditioning circuit, EEG acquisition part, main control MCU and power supply part. The low-power EEG acquisition front-end ADS1299 and STM32 are used to form the signal acquisition and data communication part. The collected EEG signal can be transmitted to the PC for real-time display. After relevant tests, the system has small volume, low power consumption, high signal-to-noise ratio, and meets the requirements of portable wearable medical devices.
Electric Power Supplies ; Electrodes ; Electroencephalography ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio

A microbial fuel cell (MFC)-based microbial electrochemical sensor was developed for real-time on-line monitoring of heavy metals in water environment. The microbial electrochemical sensor was constructed with staggered flow distribution method to optimize the parameters such as external resistance value and external circulation rate. The inhibition of concentration of simulated heavy metal wastewater on voltage under optimal parameters was analyzed. The results showed that the best performance of MFC electrochemical sensor was achieved when the external resistance value was 130 Ω and the external circulation rate was 1.0 mL/min. In this case, the microbial electrochemical sensors were responsive to 1-10 mg/L Cu²⁺, 0.25-1.25 mg/L Cd²⁺, 0.25-1.25 mg/L Cr⁶⁺ and 0.25-1.00 mg/L Hg²⁺ within 60 minutes. The maximum rejection rates of the output voltage were 92.95%, 73.11%, 82.76% and 75.80%, respectively, and the linear correlation coefficients were all greater than 0.95. In addition, the microbial electrochemical sensor showed a good biological reproducibility. The good performance for detecting heavy metals by the newly developed microbial electrochemical sensor may facilitate the real-time on-line monitoring of heavy metals in water environment.
Bioelectric Energy Sources ; Electrodes ; Metals, Heavy/analysis* ; Reproducibility of Results ; Waste Water ; Water