EPAT: a user-friendly MATLAB toolbox for EEG/ERP data processing and analysis.

Background: At the intersection of neural monitoring and decoding, event-related potential (ERP) based on electroencephalography (EEG) has opened a window into intrinsic brain function. The stability of ERP makes it frequently employed in the field of neuroscience. However, project-specific custom code, tracking of user-defined parameters, and the large diversity of commercial tools have limited clinical application. Methods: We introduce an open-source, user-friendly, and reproducible MATLAB toolbox named EPAT that includes a variety of algorithms for EEG data preprocessing. It provides EEGLAB-based template pipelines for advanced multi-processing of EEG, magnetoencephalography, and polysomnogram data. Participants evaluated EEGLAB and EPAT across 14 indicators, with satisfaction ratings analyzed using the Wilcoxon signed-rank test or paired t-test based on distribution normality. Results: EPAT eases EEG signal browsing and preprocessing, EEG power spectrum analysis, independent component analysis, time-frequency analysis, ERP waveform drawing, and topological analysis of scalp voltage. A user-friendly graphical user interface allows clinicians and researchers with no programming background to use EPAT. Conclusion: This article describes the architecture, functionalities, and workflow of the toolbox. The release of EPAT will help advance EEG methodology and its application to clinical translational studies.

Solvent-Responsive Nonporous Adaptive Crystals Derived from Pyridinium Hydrochloride and the Application in Iodine Adsorption.

Nonporous adaptive crystals (NACs) are crystalline nonporous materials that can undergo a structural adaptive phase transformation to accommodate specific guest via porous cavity or lattice voids. Most of the NACs are based on pillararenes because of their flexible backbone and intrinsic porous structure. Here a readily prepared organic hydrochloride of 4-(4-(diphenylamino)phenyl)pyridin-1-ium chloride (TPAPyH), exhibiting the solvent dimension-dependent adaptive crystallinity is reported. Wherein it forms a nonporous α crystal in a solvent with larger dimensions, while forming two porous ß and γ crystals capable of accommodating solvent molecules in solvent with small size. Furthermore, the thermal-induced single-crystal-to-single-crystal (SCSC) transition from the ß to α phase can be initiated. Upon exposure to iodine vapor or immersion in aqueous solution, the nonporous α phase transforms to porous ß phase by adsorbing iodine molecules. Owing to the formation of trihalide anion I2Cl- within the crystal cavity, TPAPyH exhibits remarkable performance in iodine storage, with a high uptaking capacity of 1.27 g g-1 and elevated iodine desorption temperature of up to 110 and 82 °C following the first and second adsorption stage. The unexpected adaptivity of TPAPyH inspires the design of NACs for selective adsorption and separation of volatile compound from organic small molecules.

Application of the Intelligent High-Throughput Antimicrobial Sensitivity Testing/Phage Screening System and Lar Index of Antimicrobial Resistance.

To improve the efficiency of antimicrobial susceptibility testing (AST) and phage high-throughput screening for resistant bacteria and to reduce the detection cost, an intelligent high-throughput AST/phage screening system, including a 96-dot matrix inoculator, image acquisition converter, and corresponding software, was developed according to AST criteria and the breakpoints of resistance (R) formulated by the Clinical & Laboratory Standards Institute (CLSI). AST and statistics of minimum inhibitory concentration (MIC) distributions (from R/8 to 8R) of 1,500 Salmonella strains isolated from poultry in Shandong, China, against 10 antimicrobial agents were carried out by the intelligent high-throughput AST/phage screening system. The Lar index, meaning "less antibiosis, less resistance and residual until little antibiosis", was obtained by calculating the weighted average of each MIC and dividing by R. This approach improves accuracy in comparison with using the prevalence of resistance to characterize the antimicrobial resistance (AMR) degree of highly resistant strains. For the strains of Salmonella with high AMR, lytic phages were efficiently screened from the phage library by this system, and the lysis spectrum was computed and analyzed. The results showed that the intelligent high-throughput AST/phage screening system was operable, accurate, highly efficient, inexpensive, and easy to maintain. Combined with the Shandong veterinary antimicrobial resistance monitoring system, the system was suitable for scientific research and clinical detection related to AMR.

Fast electrochemical impedance spectroscopy of lithium-ion batteries based on the large square wave excitation signal.

Electrochemical impedance spectroscopy (EIS) is a technique for electrochemical characterization that is sensitive to the battery state and can uncover multidimensional electrochemical evolution information within the battery. Lithium-ion batteries usually need to be used in conjunction with power conversion circuits, while conventional EIS testing is conducted offline and is time-consuming, which cannot effectively monitor the battery characteristics during use. To match the characteristics of the square wave signal during power switching, a rapid EIS measurement method for lithium-ion batteries based on the large square wave excitation signal is proposed in this paper, and develops a testing device with a response time of microseconds. The proposed method and device are applied to estimate the state of health (SOH) of the battery. In conclusion, we proposed method enhances the capabilities of EIS testing technology and has a good application prospect in real-time online impedance monitoring.

Assessment of Automated Identification of Phases in Videos of Cataract Surgery Using Machine Learning and Deep Learning Techniques.

Importance: Competence in cataract surgery is a public health necessity, and videos of cataract surgery are routinely available to educators and trainees but currently are of limited use in training. Machine learning and deep learning techniques can yield tools that efficiently segment videos of cataract surgery into constituent phases for subsequent automated skill assessment and feedback. Objective: To evaluate machine learning and deep learning algorithms for automated phase classification of manually presegmented phases in videos of cataract surgery. Design, Setting, and Participants: This was a cross-sectional study using a data set of videos from a convenience sample of 100 cataract procedures performed by faculty and trainee surgeons in an ophthalmology residency program from July 2011 to December 2017. Demographic characteristics for surgeons and patients were not captured. Ten standard labels in the procedure and 14 instruments used during surgery were manually annotated, which served as the ground truth. Exposures: Five algorithms with different input data: (1) a support vector machine input with cross-sectional instrument label data; (2) a recurrent neural network (RNN) input with a time series of instrument labels; (3) a convolutional neural network (CNN) input with cross-sectional image data; (4) a CNN-RNN input with a time series of images; and (5) a CNN-RNN input with time series of images and instrument labels. Each algorithm was evaluated with 5-fold cross-validation. Main Outcomes and Measures: Accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, and precision. Results: Unweighted accuracy for the 5 algorithms ranged between 0.915 and 0.959. Area under the receiver operating characteristic curve for the 5 algorithms ranged between 0.712 and 0.773, with small differences among them. The area under the receiver operating characteristic curve for the image-only CNN-RNN (0.752) was significantly greater than that of the CNN with cross-sectional image data (0.712) (difference, -0.040; 95% CI, -0.049 to -0.033) and the CNN-RNN with images and instrument labels (0.737) (difference, 0.016; 95% CI, 0.014 to 0.018). While specificity was uniformly high for all phases with all 5 algorithms (range, 0.877 to 0.999), sensitivity ranged between 0.005 (95% CI, 0.000 to 0.015) for the support vector machine for wound closure (corneal hydration) and 0.974 (95% CI, 0.957 to 0.991) for the RNN for main incision. Precision ranged between 0.283 and 0.963. Conclusions and Relevance: Time series modeling of instrument labels and video images using deep learning techniques may yield potentially useful tools for the automated detection of phases in cataract surgery procedures.