These results suggest that less arginine is converted to citrulline to form nitric oxide in preterm than in full-term neonates. The result is discussed in terms of the immature enzymatic systems in the preterm neonate.

The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid medical diagnoses and reveal various unique patterns of biochemical and immune features that can serve as early disease biomarkers. In this report, we demonstrate the feasibility of using an AI/ML approach in a relatively small dataset to discriminate among three categories of samples obtained from mice that either rejected or tolerated their pancreatic islet allografts following transplant in the anterior chamber of the eye, and from naïve controls. We created a locked software based on a support vector machine (SVM) technique for pattern recognition in electropherograms (EPGs) generated by micellar electrokinetic chromatography and laser induced fluorescence detection (MEKC-LIFD). Predictions were made based only on the aligned EPGs obtained in microliter-size aqueous humor samples representative of the immediate local microenvironment of the islet allografts. The analysis identified discriminative peaks in the EPGs of the three sample categories. Our classifier software was tested with targeted and untargeted peaks. Working with the patterns of untargeted peaks (i.e., based on the whole pattern of EPGs), it was able to achieve a 21 out of 22 positive classification score with a corresponding 95.45% prediction accuracy among the three sample categories, and 100% accuracy between the rejecting and tolerant recipients. These findings demonstrate the feasibility of AI/ML approaches to classify small numbers of samples and they warrant further studies to identify the analytes/biochemicals corresponding to discriminative features as potential biomarkers of islet allograft immune rejection and tolerance.

Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocardiogram (EEG), electroencephalogram (ECG), skin temperature, along with facial expressions, voice, posture to name a few, in order to classify emotions. Spiking neural networks (SNNs) represent the third generation of neural networks and employ biologically plausible models of neurons. SNNs have been shown to handle Spatio-temporal data, which is essentially the nature of the data encountered in emotion recognition problem, in an efficient manner. In this work, for the first time, we propose the application of SNNs in order to solve the emotion recognition problem with the multimodal dataset. Specifically, we use the NeuCube framework, which employs an evolving SNN architecture to classify emotional valence and evaluate the performance of our approach on the MAHNOB-HCI dataset. The multimodal data used in our work consists of facial expressions along with physiological signals such as ECG, skin temperature, skin conductance, respiration signal, mouth length, and pupil size. We perform classification under the Leave-One-Subject-Out (LOSO) cross-validation mode. Our results show that the proposed approach achieves an accuracy of 73.15% for classifying binary valence when applying feature-level fusion, which is comparable to other deep learning methods. We achieve this accuracy even without using EEG, which other deep learning methods have relied on to achieve this level of accuracy. In conclusion, we have demonstrated that the SNN can be successfully used for solving the emotion recognition problem with multimodal data and also provide directions for future research utilizing SNN for Affective computing. In addition to the good accuracy, the SNN recognition system is requires incrementally trainable on new data in an adaptive way. It only one pass training, which makes it suitable for practical and on-line applications. These features are not manifested in other methods for this problem.

A novel approach for CE data analysis based on pattern recognition techniques in the wavelet domain is presented. Low-resolution, denoised electropherograms are obtained by applying several preprocessing algorithms including denoising, baseline correction, and detection of the region of interest in the wavelet domain. The resultant signals are mapped into character sequences using first derivative information and multilevel peak height quantization. Next, a local alignment algorithm is applied on the coded sequences for peak pattern recognition. We also propose 2-D and 3-D representations of the found patterns for fast visual evaluation of the variability of chemical substances concentration in the analyzed samples. The proposed approach is tested on the analysis of intracerebral microdialysate data obtained by CE and LIF detection, achieving a correct detection rate of about 85% with a processing time of less than 0.3 s per 25,000-point electropherogram. Using a local alignment algorithm on low-resolution denoised electropherograms might have a great impact on high-throughput CE since the proposed methodology will substitute automatic fast pattern recognition analysis for slow, human based time-consuming visual pattern recognition methods.

This paper shows a feature extraction method for electrocardiographic signals (ECG) based on dynamic programming algorithms. Specifically, we applied local alignment technique for recognition of template in continuous ECG signals. First, we encoded the signal to characters based on the sign and magnitude of first derivative, then we applied local alignment algorithm to search for a complex PQRST template in target continuous ECG signal. Finally, we arrange the data for direct measurement of morphological features in all PQRST segment detected. To validate these algorithms, we contrasted them with conventional analysis by measuring QT segments in the Massachusetts Institute of Technology (MIT) data base. We obtained processing time at least 100 times lower than those obtained via conventional manual analysis and error rates in QT measurement below 5%. The automated massive analysis of ECG presented in this work is suitable for postprocessing methods like data mining, classification, and assisted diagnosis of cardiac pathologies.Keywords: ECG, Denoising, Dynamic Programming, Local Alignment, Template Classification. ResumenEste trabajo muestra un método de extracción de características de las señales electrocardíacas (ECG) basado en algoritmos de programación dinámica. Específicamente, se aplica la técnica de alineamiento local para el reconocimiento de una plantilla en la señal de ECG en tiempo continuo. En primer lugar, se codifica la señal a caracteres en base al signo y a la primera derivada, luego se aplica el algoritmo de alineamiento local para buscar una plantilla con el complejo PQRST en la señal ECG. Finalmente, se dispone de los datos para mediciones directas de las características morfológicas de todos los segmentos PQRST detectados. Para validar los algoritmos, se hace una comparación con el análisis convencional en mediciones del segmento QT sobre la base de datos del Massachusetts Institute of Technology (MIT). En los resultados se obtienen tiempos de procesamiento de al menos un centenar de veces más bajos que los obtenidos por el análisis convencional y las tasas de error en la medición del intervalo QT se encuentran por debajo del 5%. El análisis automático masivo del ECG que se presenta en este trabajo es adecuado para métodos de procesamiento orientados a la minería de datos, clasificación y diagnóstico asistido de patologías cardiacas.