Generative adversarial networks for anonymous acneic face dataset generation.

It is well known that the performance of any classification model is effective if the dataset used for the training process and the test process satisfy some specific requirements. In other words, the more the dataset size is large, balanced, and representative, the more one can trust the proposed model's effectiveness and, consequently, the obtained results. Unfortunately, large-size anonymous datasets are generally not publicly available in biomedical applications, especially those dealing with pathological human face images. This concern makes using deep-learning-based approaches challenging to deploy and difficult to reproduce or verify some published results. In this paper, we propose an efficient method to generate a realistic anonymous synthetic dataset of human faces, focusing on attributes related to acne disorders at three distinct levels of severity (Mild, Moderate, and Severe). Notably, our approach initiates from a small dataset of facial acne images, leveraging generative techniques to augment and diversify the dataset, ensuring comprehensive coverage of acne severity levels while maintaining anonymity and realism in the synthetic data. Therefore, a specific hierarchy StyleGAN-based algorithm trained at distinct levels is considered. Moreover, the utilization of generative adversarial networks for augmentation offers a means to circumvent potential privacy or legal concerns associated with acquiring medical datasets. This is attributed to the synthetic nature of the generated data, where no actual subjects are present, thereby ensuring compliance with privacy regulations and legal considerations. To evaluate the performance of the proposed scheme, we consider a CNN-based classification system, trained using the generated synthetic acneic face images and tested using authentic face images. Consequently, we show that an accuracy of 97.6% is achieved using InceptionResNetv2. As a result, this work allows the scientific community to employ the generated synthetic dataset for any data processing application without restrictions on legal or ethical concerns. Moreover, this approach can also be extended to other applications requiring the generation of synthetic medical images.

A novel biometric authentication approach using ECG and EMG signals.

Security biometrics is a secure alternative to traditional methods of identity verification of individuals, such as authentication systems based on user name and password. Recently, it has been found that the electrocardiogram (ECG) signal formed by five successive waves (P, Q, R, S and T) is unique to each individual. In fact, better than any other biometrics' measures, it delivers proof of subject's being alive as extra information which other biometrics cannot deliver. The main purpose of this work is to present a low-cost method for online acquisition and processing of ECG signals for person authentication and to study the possibility of providing additional information and retrieve personal data from an electrocardiogram signal to yield a reliable decision. This study explores the effectiveness of a novel biometric system resulting from the fusion of information and knowledge provided by ECG and EMG (Electromyogram) physiological recordings. It is shown that biometrics based on these ECG/EMG signals offers a novel way to robustly authenticate subjects. Five ECG databases (MIT-BIH, ST-T, NSR, PTB and ECG-ID) and several ECG signals collected in-house from volunteers were exploited. A palm-based ECG biometric system was developed where the signals are collected from the palm of the subject through a minimally intrusive one-lead ECG set-up. A total of 3750 ECG beats were used in this work. Feature extraction was performed on ECG signals using Fourier descriptors (spectral coefficients). Optimum-Path Forest classifier was used to calculate the degree of similarity between individuals. The obtained results from the proposed approach look promising for individuals' authentication.

A structural, phylogenetic, and functional study of 15.5-kD/Snu13 protein binding on U3 small nucleolar RNA.

The 15.5-kD protein and its yeast homolog Snu13p bind U4 snRNA, U3 snoRNA, and the C/D box snoRNAs. In U4 snRNA, they associate with a helix-bulge-helix (K-turn) structure. U3 snoRNA contains two conserved pairs of boxes, C'/D and B/C, which were both expected to bind the 15.5-kD/Snu13 protein. Only binding to the B/C motif was experimentally demonstrated. Here, by chemical probing of in vitro reconstituted RNA/protein complexes, we demonstrate the independent binding of the 15.5-kD/Snu13 protein to each of the two motifs. Due to a highly reduced stem I (1 bp), the K-turn structure is not formed in the naked B/C motif. However, gel-shift experiments revealed a higher affinity of Snu13p for the B/C motif, compared to the C'/D motif. A phylogenetic analysis of U3 snoRNA, coupled with an analysis of Snu13p affinity for variant yeast C'/D and B/C motifs, and a study of the functionality of a truncated yeast U3 snoRNA carrying base substitutions in the C'/D and B/C motifs, revealed that conservation of the identities of residues 2 and 3 in the B/C K-turn is more important for Snu13p binding and U3 snoRNA function, than conservation of the identities of corresponding residues in the C'/D K-turn. This suggests that binding of Snu13p to K-turns with a very short helix I imposes sequence constraints in the bulge. Altogether, the data demonstrate the strong importance of the binding of the 15.5-kD/Snu13 protein to the C'/D and B/C motifs for both U3 snoRNP assembly and activity.