LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals

Electroencephalography (EEG) signals collected from human brains have generally been used to diagnose diseases. Moreover, EEG signals can be used in several areas such as emotion recognition, driving fatigue detection. This work presents a new emotion recognition model by using EEG signals. The primary aim of this model is to present a highly accurate emotion recognition framework by using both a hand-crafted feature generation and a deep classifier. The presented framework uses a multilevel fused feature generation network. This network has three primary phases, which are tunable Q-factor wavelet transform (TQWT), statistical feature generation, and nonlinear textural feature generation phases. TQWT is applied to the EEG data for decomposing signals into different sub-bands and create a multilevel feature generation network. In the nonlinear feature generation, an S-box of the LED block cipher is utilized to create a pattern, which is named as Led-Pattern. Moreover, statistical feature extraction is processed using the widely used statistical moments. The proposed LED pattern and statistical feature extraction functions are applied to 18 TQWT sub-bands and an original EEG signal. Therefore, the proposed hand-crafted learning model is named LEDPatNet19. To select the most informative features, ReliefF and iterative Chi2 (RFIChi2) feature selector is deployed. The proposed model has been developed on the two EEG emotion datasets, which are GAMEEMO and DREAMER datasets. Our proposed hand-crafted learning network achieved 94.58%, 92.86%, and 94.44% classification accuracies for arousal, dominance, and valance cases of the DREAMER dataset. Furthermore, the best classification accuracy of the proposed model for the GAMEEMO dataset is equal to 99.29%. These results clearly illustrate the success of the proposed LEDPatNet19.     

Biotransformation of some steroids by Aspergillus wentii

First steroid biotransformations performed by Aspergillus wentii MRC 200316 are reported. Testosterone (1) yields 6beta-hydroxytestosterone (3) and 14alpha-hydroxytestosterone (4) while progesterone (2) yields 11alpha-hydroxyprogesterone (5).     

A collodion baby with hypothyroidism

Collodion baby is a rare keratinizing congenital disorder. Although it is milder in degree than harlequin fetus, the infant is at risk for increased water loss, thermal instability, percutaneous toxicity, and infection as a result of an impaired skin barrier function. Here we report on an 11 days-old collodion baby with hypernatremic dehydratation, septicemia and congenital hypothyroidism. To our knowledge congenital hypothyroidism associated with collodion baby is reported in only one case up to date.     

Characterization of the endopeptidase activity of tripeptidyl-peptidase II

Tripeptidyl-peptidase II (TPP II) is a giant cytosolic peptidase with a proposed role in cellular protein degradation and protection against apoptosis. Beside its well-characterised exopeptidase activity, TPP II also has an endopeptidase activity. Little is known about this activity, and since it could be important for the physiological role of TPP II, we have investigated it in more detail. Two peptides, Nef(69-87) and LL37, were incubated with wild-type murine TPP II and variants thereof as well as TPP II from human and Drosophila melanogaster. Two intrinsically disordered proteins were also included in the study. We conclude that the endopeptidase activity is more promiscuous than previously reported. It is also clear that TPP II can attack longer disordered peptides up to 75 amino acid residues. Using a novel FRET substrate, the catalytic efficiency of the endopeptidase activity could be determined to be 5 orders of magnitude lower than for the exopeptidase activity.