A novel atrial fibrillation automatic detection algorithm based on ensemble learning and multi-feature discrimination.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia disorder that necessitates long-time electrocardiogram (ECG) data for clinical diagnosis, leading to low detection efficiency. Automatic detection of AF signals within short-time ECG recordings is challenging. To address these issues, this paper proposes a novel algorithm called Ensemble Learning and Multi-Feature Discrimination (ELMD) for the identification and detection of AF signals. Firstly, a robust classifier, BSK-Model, is constructed using ensemble learning. Subsequently, the ECG R-waves are detected, and the ECG signals are segmented into consecutive RR intervals. Time domain, frequency domain, and nonlinear features are extracted from these intervals. Finally, these features are fed into the BSK-Model to discriminate AF. The proposed methodology is evaluated using the MIT-BIH AF database. The results demonstrate that when RR intervals are employed as classification units, the specificity and accuracy of AF detection in long-time ECG data exceed 99%, showcasing a significant improvement over traditional single-model classification. Additionally, the sensitivity and accuracy achieved by testing cardiac segments are both above 96%. With a minimum requirement of only four cardiac segments, AF events can be accurately identified, thereby enabling rapid discrimination of short-time single-lead ECG AF events. Consequently, this approach is suitable for real-time and accurate AF detection using low-computational-power ECG diagnostic analysis devices, such as wearable devices.

Removal of antibiotic resistance genes in secondary effluent by slow filtration-NF process.

In this study, the combined process of slow filtration and low pressure nanofiltration (NF) has been used to deeply remove the antibiotic resistance genes (ARGs) in a secondary effluent, and the mechanism of ARGs removal has been subsequently explored. It is observed that the optimal filtration rate for the slow filtration without biofilm, slow filtration with the aerobic heterotrophic biofilm, slow filtration with the nitrification biofilm and slow filtration with the denitrification biofilm to remove tet A, tet W, sul I, sul II and DOC is 20 cm/h, and the slow filtration with the aerobic heterotrophic biofilm exhibits the highest removal amount. The slow filtration with biofilms removes a high extent of free ARGs. As compared with the direct NF of the secondary effluent and the slow filtration without biofilm-NF, the slow filtration with the aerobic heterotrophic biofilm-NF combined process exhibits the best ARGs removal effect. The microbial population structure and the high filtration rate in the aerobic heterotrophic biofilm promote the removal of ARGs. Strengthening the removal of 16S rDNA, intI 1 and DOC can improve the ARGs removal effect of the combined process. Overall, the slow filtration-NF combined process is a better process for removing ARGs.

Pathogenicity and Volatile Nematicidal Metabolites from Duddingtonia flagrans against Meloidogyne incognita.

Plant parasitic nematodes, especially parasitic root-knot nematodes, are one of the most destructive plant pathogens worldwide. The control of plant root-knot nematodes is extremely challenging. Duddingtonia flagrans is a type of nematode-trapping fungi (NTF), which produces three-dimensional adhesive networks to trap nematodes. In this study, the pathogenicity and volatile organic compounds (VOCs) of the NTF D. flagrans against the plant root-knot nematode, Meloidogyne incognita, were investigated. The predatory process of D. flagrans trapping M. incognita was observed using scanning electron microscopy. Gas chromatography-mass spectrometry analysis of the VOCs from D. flagrans led to the identification of 52 metabolites, of which 11 main compounds were tested individually for their activity against M. incognita. Three compounds, cyclohexanamine, cyclohexanone, and cyclohexanol, were toxic to M. incognita. Furthermore, these three VOCs inhibited egg hatching of M. incognita. Cyclohexanamine showed the highest nematicidal activity, which can cause 97.93% mortality of M. incognita at 8.71 µM within 12 h. The number of hatched juveniles per egg mass after 3 days was just 8.44 when treated with 26.14 µM cyclohexanamine. This study is the first to demonstrate the nematicidal activity of VOCs produced by D. flagrans against M. incognita, which indicates that D. flagrans has the potential to biocontrol plant root-knot nematodes.

Quasi-planar B36 boron cluster: a new potential basis for ammonia detection.

Detection of NH3 at a trace level is nowadays of great importance. Here, we investigate the reactivity and sensitivity of a B36 borophene toward NH3 gas employing DFT calculations. The energetic results point out that the adsorption process strongly depends on the orientation of NH3 relative to the B36 sheet. An NH3 molecule preferentially interacts via its N-head with a B atom of the B36 with a change of enthalpy of - 90.5 kJ/mol at room temperature and 1 atm. Mulliken charges analysis results reveal that approximately 0.35 |e| transfers from NH3 to the B36, leaving partially positive NH3. We found that the B36 electronic properties are meaningfully sensitive to the NH3 gas, and it may be a sign of further usage of B36 as a potential NH3 gas sensor. The density of state analysis shows that the B36 gap is expressively decreased from 1.55 to 1.35 eV, increasing its electrical conductance.