Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing Units.

The advent of Industry 4.0 introduced new ways for businesses to evolve by implementing maintenance policies leading to advancements in terms of productivity, efficiency, and financial performance. In line with the growing emphasis on sustainability, industries implement predictive techniques based on Artificial Intelligence for the purpose of mitigating machine and equipment failures by predicting anomalies during their production process. In this work, a new dataset that was made publicly available, collected from an industrial blower, is presented, analyzed and modeled using a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder. Specifically the right and left mounted ball bearing units were measured during several months of normal operational condition as well as during an encumbered operational state. An anomaly detection model was developed for the purpose of analyzing the operational behavior of the two bearing units. A stacked sparse Long Short-Term Memory Autoencoder was successfully trained on the data obtained from the left unit under normal operating conditions, learning the underlying patterns and statistical connections of the data. The model was evaluated by means of the Mean Squared Error using data from the unit's encumbered state, as well as using data collected from the right unit. The model performed satisfactorily throughout its evaluation on all collected datasets. Also, the model proved its capability for generalization along with adaptability on assessing the behavior of equipment similar to the one it was trained on.

Nicotinic cholinergic system and COVID-19: In silico identification of interactions between α7 nicotinic acetylcholine receptor and the cryptic epitopes of SARS-Co-V and SARS-CoV-2 Spike glycoproteins.

SARS-CoV-2 is the coronavirus that originated in Wuhan in December 2019 and has spread globally. Studies have shown that smokers are less likely to be diagnosed with or be hospitalized for COVID-19 but, once hospitalized, have higher odds for an adverse outcome. We have previously presented the potential interaction between SARS-CoV-2 Spike glycoprotein and nicotinic acetylcholine receptors (nAChRs), due to a "toxin-like" epitope on the Spike glycoprotein, with homology to a sequence of a snake venom toxin. This epitope coincides with the well-described cryptic epitope for the human anti-SARS-CoV antibody CR3022. In this study, we present the molecular complexes of both SARS-CoV and SARS-CoV-2 Spike glycoproteins, at their open or closed conformations, with the model of the human α7 nAChR. We found that all studied protein complexes' interface involves a large part of the "toxin-like" sequences of SARS-CoV and SARS-CoV-2 Spike glycoproteins and toxin binding site of human α7 nAChR. Our findings provide further support to the hypothesis about the protective role of nicotine and other cholinergic agonists. The potential therapeutic role of CR3022 and other similar monoclonal antibodies with increased affinity for SARS-CoV-2 Spike glycoprotein against the clinical effects originating from the dysregulated cholinergic pathway should be further explored.

Nicotinic cholinergic system and COVID-19: In silico evaluation of nicotinic acetylcholine receptor agonists as potential therapeutic interventions.

SARS-CoV-2 infection was announced as a pandemic in March 2020. Since then, several scientists have focused on the low prevalence of smokers among hospitalized COVID-19 patients. These findings led to our hypothesis that the Nicotinic Cholinergic System (NCS) plays a crucial role in the manifestation of COVID-19 and its severe symptoms. Molecular modeling revealed that the SARS-CoV-2 Spike glycoprotein might bind to nicotinic acetylcholine receptors (nAChRs) through a cryptic epitope homologous to snake toxins, substrates well documented and known for their affinity to the nAChRs. This binding model could provide logical explanations for the acute inflammatory disorder in patients with COVID-19, which may be linked to severe dysregulation of NCS. In this study, we present a series of complexes with cholinergic agonists that can potentially prevent SARS-CoV-2 Spike glycoprotein from binding to nAChRs, avoiding dysregulation of the NCS and moderating the symptoms and clinical manifestations of COVID-19. If our hypothesis is verified by in vitro and in vivo studies, repurposing agents currently approved for smoking cessation and neurological conditions could provide the scientific community with a therapeutic option in severe COVID-19.

Comparison between auto-cross-correlation coefficients and coherence methods applied to the EEG for diagnostic purposes.

BACKGROUND: Power-spectrum and coherence analysis of the EEG has often been applied in the study of various forms of brain dysfunction. The examination of differences in intra-hemispheric coherence and a novel method based on auto-cross-correlation algorithms between the left and right hemispheres of the same EEG are investigated in this paper. MATERIAL/METHODS: Both coherence and the proposed method are computed from the same EEG segment across the EEG spectrum (0-64 Hz). In this experiment, five EEGs of different patients with severe brain damage were used. The novelty of this study lies in the fact that the autocorrelation coefficients are extracted in a particular spectrum, in contrast to the traditional coherence method, which is based on a measure of the square of the linear association between the two EEG signals from the entire EEG spectrum. RESULTS: The results of the application of coherence and the proposed method showed that particular pairs of symmetrical cortex channels differed dramatically in specific damaged areas. CONCLUSIONS: Statistical analysis of the above results showed that the proposed method is more accurate than coherence because it could recognize the cases of the patients to a significant statistical degree, whereas the coherence method showed weakness in recognizing them.