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To explore the application of biosensor in real-time monitoring of composite heavy metal polluted wastewater in view of the low performance of MFC sensor, this study used sodium alginate to immobilize biochar to the anode of MFC biosensor, and conducted a study on the sensor performance and related biological processes. The results showed that under the optimal HRT conditions, the output power of the MFC-sensor (BC-300) was 0.432 W/m3 after biochar modification, which was much higher than the highest power density of CG and BC-0 of 0.117 and 0.088 W/m3. The correlation coefficient was greater than that of the control group at the plating wastewater concentration of 0.1–1.0 M and had a wider detection range, and the time to recover the output voltage was 1/3 of that of the control group. The biochar significantly promoted the sensitivity, interference resistance, recovery and anti-interference performance of the MFC-sensor. The intrinsic mechanism was that the composition and structure of biochar lead to a 1.53 fold increase in the abundance of electrogenic microorganisms and the abundance of functional genes such as cytochrome c (MtrABC, CymA, Cox, etc.) and flavin (riba, Rib B, gdh, ushA, IDH, etc.) increased by about 1.03–3.20 times, which promoted the shift of electrons from intracellular to extracellular receptors and significantly improved the electron transfer and the energy metabolism efficiency. The results of this study can provide a reference for the application of MFCsensor to the detection of complex heavy metal effluents. [Display omitted] • Biochar enhanced the correlation, sensitivity and resilience of the MFC sensor. • Microbiological mechanism of biochar enhanced sensor performance was revealed. • The relationship between energy metabolism and biochar in MFC sensor was elucidated. • Electron transfer efficiency and gene abundance affected the performance of sensor. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning with eye-tracking has performed with high accuracy to identify ASD. • A high degree of heterogeneity was found among included studies. • Methodological drawbacks might deteriorate the reliability of reported results. • Further investigation of optimal eye-tracking paradigms and algorithms is warranted. Machine learning has been widely used to identify Autism Spectrum Disorder (ASD) based on eye-tracking, but its accuracy is uncertain. We aimed to summarize the available evidence on the performances of machine learning algorithms in classifying ASD and typically developing (TD) individuals based on eye-tracking data. We searched Medline, Embase, Web of Science, Scopus, Cochrane Library, IEEE Xplore Digital Library, Wan Fang Database, China National Knowledge Infrastructure, Chinese BioMedical Literature Database, VIP Database for Chinese Technical Periodicals, from database inception to December 24, 2021. Studies using machine learning methods to classify ASD and TD individuals based on eye-tracking technologies were included. We extracted the data on study population, model performances, algorithms of machine learning, and paradigms of eye-tracking. This study is registered with PROSPERO, CRD42022296037. 261 articles were identified, of which 24 studies with sample sizes ranging from 28 to 141 were included (n = 1396 individuals). Machine learning based on eye-tracking yielded the pooled classified accuracy of 81 % (I2 = 73 %), specificity of 79 % (I2 = 61 %), and sensitivity of 84 % (I2 = 61 %) in classifying ASD and TD individuals. In subgroup analysis, the accuracy was 88 % (95 % CI: 85–91 %), 79 % (95 % CI: 72–84 %), 71 % (95 % CI: 59–91 %) for preschool-aged, school-aged, and adolescent-adult group. Eye-tracking stimuli and machine learning algorithms varied widely across studies, with social, static, and active stimuli and Support Vector Machine and Random Forest most commonly reported. Regarding the model performance evaluation, 15 studies reported their final results on validation datasets, four based on testing datasets, and five did not report whether they used validation datasets. Most studies failed to report the information on eye-tracking hardware and the implementation process. Using eye-tracking data, machine learning has shown potential in identifying ASD individuals with high accuracy, especially in preschool-aged children. However, the heterogeneity between studies, the absence of test set-based performance evaluations, the small sample size, and the non-standardized implementation of eye-tracking might deteriorate the reliability of results. Further well-designed and well-executed studies with comprehensive and transparent reporting are needed to determine the optimal eye-tracking paradigms and machine learning algorithms. [ABSTRACT FROM AUTHOR]