Sleep state classification using power spectral density and residual neural network with multichannel EEG signals.

This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively

Adaptively evolved Escherichia coli for improved ability of formate utilization as a carbon source in sugar???free conditions

Background: Formate converted from CO2 reduction has great potential as a sustainable feedstock for biological production of biofuels and biochemicals. Nevertheless, utilization of formate for growth and chemical production by microbial species is limited due to its toxicity or the lack of a metabolic pathway. Here, we constructed a formate assimilation pathway in Escherichia coli and applied adaptive laboratory evolution to improve formate utilization as a carbon source in sugar-free conditions. Results: The genes related to the tetrahydrofolate and serine cycles from Methylobacterium extorquens AM1 were overexpressed for formate assimilation, which was proved by the 13C-labeling experiments. The amino acids detected by GC/MS showed significant carbon labeling due to biomass production from formate. Then, 150 serial subcultures were performed to screen for evolved strains with improved ability to utilize formate. The genomes of evolved mutants were sequenced and the mutations were associated with formate dehydrogenation, folate metabolism, and biofilm formation. Last, 90 mg/L of ethanol production from formate was achieved using fed-batch cultivation without addition of sugars. Conclusion: This work demonstrates the effectiveness of the introduction of a formate assimilation pathway, combined with adaptive laboratory evolution, to achieve the utilization of formate as a carbon source. This study suggests that the constructed E. coli could serve as a strain to exploit formate and captured CO2

The Arabidopsis thaliana Homeobox Gene ATHB12 Is Involved in Symptom Development Caused by Geminivirus Infection

BACKGROUND: Geminiviruses are single-stranded DNA viruses that infect a number of monocotyledonous and dicotyledonous plants. Arabidopsis is susceptible to infection with the Curtovirus, Beet severe curly top virus (BSCTV). Infection of Arabidopsis with BSCTV causes severe symptoms characterized by stunting, leaf curling, and the development of abnormal inflorescence and root structures. BSCTV-induced symptom development requires the virus-encoded C4 protein which is thought to interact with specific plant-host proteins and disrupt signaling pathways important for controlling cell division and development. Very little is known about the specific plant regulatory factors that participate in BSCTV-induced symptom development. This study was conducted to identify specific transcription factors that are induced by BSCTV infection. METHODOLOGY/PRINCIPAL FINDINGS: Arabidopsis plants were inoculated with BSCTV and the induction of specific transcription factors was monitored using quantitative real-time polymerase chain reaction assays. We found that the ATHB12 and ATHB7 genes, members of the homeodomain-leucine zipper family of transcription factors previously shown to be induced by abscisic acid and water stress, are induced in symptomatic tissues of Arabidopsis inoculated with BSCTV. ATHB12 expression is correlated with an array of morphological abnormalities including leaf curling, stunting, and callus-like structures in infected Arabidopsis. Inoculation of plants with a BSCTV mutant with a defective c4 gene failed to induce ATHB12. Transgenic plants expressing the BSCTV C4 gene exhibited increased ATHB12 expression whereas BSCTV-infected ATHB12 knock-down plants developed milder symptoms and had lower ATHB12 expression compared to the wild-type plants. Reporter gene studies demonstrated that the ATHB12 promoter was responsive to BSCTV infection and the highest expression levels were observed in symptomatic tissues where cell cycle genes also were induced. CONCLUSIONS/SIGNIFICANCE: These results suggest that ATHB7 and ATHB12 may play an important role in the activation of the abnormal cell division associated with symptom development during geminivirus infection

Enhancing the tyosine production from acetate in Escherichia coli by optimization of glyoxylate cycle

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Biosensor Engineering for Efficient Caprolactam Production in Bacterial Strain

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Development of Caprolactam-specific Riboswitch that Regulates Translation Initiation

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Development of a Caprolactam Biosensor Based on RNA for Rapid Selection of Caprolactam Producer Cell

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Artificial lactam riboswitch for high-throughput screening and selection

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Strict-Feedback Backstepping Digital Twin and Machine Learning Solution in AE Signals for Bearing Crack Identification

Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and motor speeds. The proposed method has three main steps. In the first step, the strict-feedback backstepping digital twin is designed for acoustic emission signal modeling and estimation. After that, the acoustic emission residual signal is generated. Finally, a support vector machine is recommended for crack type/size classification. The proposed digital twin is presented in two steps, (a) AE signal modeling and (b) AE signal estimation. The AE signal in normal conditions is modeled using an autoregressive technique, the Laguerre algorithm, a support vector regression technique and a Gaussian process regression procedure. To design the proposed digital twin, a strict-feedback backstepping observer, an integral term, a support vector regression and a fuzzy logic algorithm are suggested for AE signal estimation. The Ulsan Industrial Artificial Intelligence (UIAI) Lab’s bearing dataset was used to test the efficiency of the combined strict-feedback backstepping digital twin and machine learning technique for bearing crack type/size diagnosis. The average accuracies of the crack type diagnosis and crack size diagnosis of acoustic emission signals for the bearings used in the proposed algorithm were 97.13% and 96.9%, respectively