One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction

An earth pressure balance (EPB) TBM is used in soft ground conditions, and these conditions lead to the fluctuation and instability of machine parameters. Machine parameters influence cutter wear and tunnel excavation. For this reason, to evaluate and predict the cutter wear of an EPB TBM, a 1D CNN model was used to provide machine-parameter-based cutter wear prediction using an EPB TBM operational dataset. The machine parameters were split into 80% training and 20% test datasets. Compared to traditional machine learning applications and two deep neural network models, the proposed model provided reliable results with a reasonable computational time. The correlation coefficient was 89.6% R-2, the mean squared error (MSE) was 57.6, the mean absolute error (MAE) was 1.6, and the computational wall time was 3 min 22 s

The Biomphalaria glabrata DNA methylation machinery displays spatial tissue expression, is differentially active in distinct snail populations and is modulated by interactions with Schistosoma mansoni

BBSRC Grant (BB/K005448/1)Background The debilitating human disease schistosomiasis is caused by infection with schistosome parasites that maintain a complex lifecycle alternating between definitive (human) and intermediate (snail) hosts. While much is known about how the definitive host responds to schistosome infection, there is comparably less information available describing the snail?s response to infection. Methodology/Principle findings Here, using information recently revealed by sequencing of the Biomphalaria glabrata intermediate host genome, we provide evidence that the predicted core snail DNA methylation machinery components are associated with both intra-species reproduction processes and inter-species interactions. Firstly, methyl-CpG binding domain protein (Bgmbd2/3) and DNA methyltransferase 1 (Bgdnmt1) genes are transcriptionally enriched in gonadal compared to somatic tissues with 5-azacytidine (5-AzaC) treatment significantly inhibiting oviposition. Secondly, elevated levels of 5-methyl cytosine (5mC), DNA methyltransferase activity and 5mC binding in pigmented hybrid- compared to inbred (NMRI)- B. glabrata populations indicate a role for the snail?s DNA methylation machinery in maintaining hybrid vigour or heterosis. Thirdly, locus-specific detection of 5mC by bisulfite (BS)-PCR revealed 5mC within an exonic region of a housekeeping protein-coding gene (Bg14-3-3), supporting previous in silico predictions and whole genome BS-Seq analysis of this species? genome. Finally, we provide preliminary evidence for parasite-mediated host epigenetic reprogramming in the schistosome/snail system, as demonstrated by the increase in Bgdnmt1 and Bgmbd2/3 transcript abundance following Bge (B. glabrata embryonic cell line) exposure to parasite larval transformation products (LTP). Conclusions/Significance The presence of a functional DNA methylation machinery in B. glabrata as well as the modulation of these gene products in response to schistosome products, suggests a vital role for DNA methylation during snail development/oviposition and parasite interactions. Further deciphering the role of this epigenetic process during Biomphalaria/Schistosoma co-evolutionary biology may reveal key factors associated with disease transmission and, moreover, enable the discovery of novel lifecycle intervention strategiespublishersversionPeer reviewe

Large-Scale Gene Disruption in Magnaporthe oryzae Identifies MC69, a Secreted Protein Required for Infection by Monocot and Dicot Fungal Pathogens

To search for virulence effector genes of the rice blast fungus, Magnaporthe oryzae, we carried out a large-scale targeted disruption of genes for 78 putative secreted proteins that are expressed during the early stages of infection of M. oryzae. Disruption of the majority of genes did not affect growth, conidiation, or pathogenicity of M. oryzae. One exception was the gene MC69. The mc69 mutant showed a severe reduction in blast symptoms on rice and barley, indicating the importance of MC69 for pathogenicity of M. oryzae. The mc69 mutant did not exhibit changes in saprophytic growth and conidiation. Microscopic analysis of infection behavior in the mc69 mutant revealed that MC69 is dispensable for appressorium formation. However, mc69 mutant failed to develop invasive hyphae after appressorium formation in rice leaf sheath, indicating a critical role of MC69 in interaction with host plants. MC69 encodes a hypothetical 54 amino acids protein with a signal peptide. Live-cell imaging suggested that fluorescently labeled MC69 was not translocated into rice cytoplasm. Site-directed mutagenesis of two conserved cysteine residues (Cys36 and Cys46) in the mature MC69 impaired function of MC69 without affecting its secretion, suggesting the importance of the disulfide bond in MC69 pathogenicity function. Furthermore, deletion of the MC69 orthologous gene reduced pathogenicity of the cucumber anthracnose fungus Colletotrichum orbiculare on both cucumber and Nicotiana benthamiana leaves. We conclude that MC69 is a secreted pathogenicity protein commonly required for infection of two different plant pathogenic fungi, M. oryzae and C. orbiculare pathogenic on monocot and dicot plants, respectively

One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction

An earth pressure balance (EPB) TBM is used in soft ground conditions, and these conditions lead to the fluctuation and instability of machine parameters. Machine parameters influence cutter wear and tunnel excavation. For this reason, to evaluate and predict the cutter wear of an EPB TBM, a 1D CNN model was used to provide machine-parameter-based cutter wear prediction using an EPB TBM operational dataset. The machine parameters were split into 80% training and 20% test datasets. Compared to traditional machine learning applications and two deep neural network models, the proposed model provided reliable results with a reasonable computational time. The correlation coefficient was 89.6% R-2, the mean squared error (MSE) was 57.6, the mean absolute error (MAE) was 1.6, and the computational wall time was 3 min 22 s

One-Dimensional Convolutional Neural Network for Drill Bit Failure Detection in Rotary Percussion Drilling

Drill bit failure is a prominent concern in the drilling process of any mine, as it can lead to increased mining costs. Over the years, the detection of drill bit failure has been based on the operator’s skills and experience, which are subjective and susceptible to errors. To enhance the efficiency of mining operations, it is necessary to implement applications of artificial intelligence to produce a superior method for drill bit monitoring. This research proposes a new and reliable method to detect drill bit failure in rotary percussion drills using deep learning: a one-dimensional convolutional neural network (1D CNN) with time-acceleration as input data. 18 m3 of granite rock were drilled horizontally using a rock drill and intact tungsten carbide drill bits. The time acceleration of drill vibrations was measured using acceleration sensors mounted on the guide cell of the rock drill. The drill bit failure detection model was evaluated on five drilling conditions: normal, defective, abrasion, high pressure, and misdirection. The model achieved a classification accuracy of 88.7%. The proposed model was compared to three state-of-the-art (SOTA) deep learning neural networks. The model outperformed SOTA methods in terms of classification accuracy. Our method provides an automatic and reliable way to detect drill bit failure in rotary percussion drills

One-Dimensional Convolutional Neural Network for Drill Bit Failure Detection in Rotary Percussion Drilling

Drill bit failure is a prominent concern in the drilling process of any mine, as it can lead to increased mining costs. Over the years, the detection of drill bit failure has been based on the operator’s skills and experience, which are subjective and susceptible to errors. To enhance the efficiency of mining operations, it is necessary to implement applications of artificial intelligence to produce a superior method for drill bit monitoring. This research proposes a new and reliable method to detect drill bit failure in rotary percussion drills using deep learning: a one-dimensional convolutional neural network (1D CNN) with time-acceleration as input data. 18 m3 of granite rock were drilled horizontally using a rock drill and intact tungsten carbide drill bits. The time acceleration of drill vibrations was measured using acceleration sensors mounted on the guide cell of the rock drill. The drill bit failure detection model was evaluated on five drilling conditions: normal, defective, abrasion, high pressure, and misdirection. The model achieved a classification accuracy of 88.7%. The proposed model was compared to three state-of-the-art (SOTA) deep learning neural networks. The model outperformed SOTA methods in terms of classification accuracy. Our method provides an automatic and reliable way to detect drill bit failure in rotary percussion drills