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ABSTRACT Motile plant-associated bacteria use chemotaxis and dedicated chemoreceptors to navigate gradients in their surroundings and to colonize host plant surfaces. Here, we characterize a chemoreceptor that we named Tlp2 in the soil alphaproteobacteriumAzospirillum brasilense. We show that the Tlp2 ligand-binding domain is related to the 4-helix bundle family and is conserved in chemoreceptors found in the genomes of many soil- and sediment-dwelling alphaproteobacteria. The promoter oftlp2is regulated in an NtrC- and RpoN-dependent manner and is most upregulated under conditions of nitrogen fixation or in the presence of nitrate. Using fluorescently tagged Tlp2 (Tlp2-YFP), we show that this chemoreceptor is present in low abundance in chemotaxis-signaling clusters and is prone to degradation. We also obtained evidence that the presence of ammonium rapidly disrupts Tlp2-YFP localization. Behavioral experiments using a strain lacking Tlp2 and variants of Tlp2 lacking conserved arginine residues suggest that Tlp2 mediates chemotaxis in gradients of nitrate and nitrite, with the R159 residue being essential for Tlp2 function. We also provide evidence that Tlp2 is essential for root surface colonization of some plants (teff, red clover, and cowpea) but not others (wheat, sorghum, alfalfa, and pea). These results highlight the selective role of nitrate sensing and chemotaxis in plant root surface colonization and illustrate the relative contribution of chemoreceptors to chemotaxis and root surface colonization.IMPORTANCEBacterial chemotaxis mediates host-microbe associations, including the association of beneficial bacteria with the roots of host plants. Dedicated chemoreceptors specify sensory preferences during chemotaxis. Here, we show that a chemoreceptor mediating chemotaxis to nitrate is important in the beneficial soil bacterium colonization of some but not all plant hosts tested. Nitrate is the preferred nitrogen source for plant nutrition, and plants sense and tightly control nitrate transport, resulting in varying nitrate uptake rates depending on the plant and its physiological state. Nitrate is thus a limiting nutrient in the rhizosphere. Chemotaxis and dedicated chemoreceptors for nitrate likely provide motile bacteria with a competitive advantage to access this nutrient in the rhizosphere. 

Abstract Monitoring machine health and product quality enables predictive maintenance that optimizes repairs to minimize factory downtime. Data-driven intelligent manufacturing often relies on probabilistic techniques with intractable distributions. For example, generative models of data distributions can balance fault classes with synthetic data, and sampling the posterior distribution of hidden model parameters enables prognosis of degradation trends. Normalizing flows can address these problems while avoiding the training instability or long inference times of other generative Deep Learning (DL) models like Generative Adversarial Networks (GAN), Variational Autoencoders (VAE), and diffusion networks. To evaluate normalizing flows for manufacturing, experiments are conducted to synthesize surface defect images from an imbalanced data set and estimate parameters of a tool wear degradation model from limited observations. Results show that normalizing flows are an effective, multi-purpose DL architecture for solving these problems in manufacturing. Future work should explore normalizing flows for more complex degradation models and develop a framework for likelihood-based anomaly detection. Code is available at https://github.com/uky-aism/flows-for-manufacturing. 

Early identification of rotating machinery faults is crucial to avoid catastrophic failures upon installation. Contact-based vibration acquisition approaches are traditionally used for the purpose of machine health monitoring and end-of-line quality control. In complex working conditions, it can be difficult to perform an accurate accelerometer based vibration test. Acoustic signals (sound pressure and particle velocity) also contain important information about the operating state of mechanical equipment and can be used to detect different faults. A deep learning approach, namely one-dimensional Convolution Neural Networks (1D-CNN) can directly process raw time signals thereby eliminating the human dependance on fault feature extraction. An experimental research study is conducted to test the proposed 1D-CNN methodology on three different electric motor faults. The results from the study indicate that the fault detection performance from the new acoustic-based method is very effective and thus can be a good replacement to the conventional accelerometer-based methods for detection and diagnosis of mechanical faults in electric motors. 

Given its demonstrated ability in analyzing and revealing patterns underlying data, Deep Learning (DL) has been increasingly investigated to complement physics-based models in various aspects of smart manufacturing, such as machine condition monitoring and fault diagnosis, complex manufacturing process modeling, and quality inspection. However, successful implementation of DL techniques relies greatly on the amount, variety, and veracity of data for robust network training. Also, the distributions of data used for network training and application should be identical to avoid the internal covariance shift problem that reduces the network performance applicability. As a promising solution to address these challenges, Transfer Learning (TL) enables DL networks trained on a source domain and task to be applied to a separate target domain and task. This paper presents a domain adversarial TL approach, based upon the concepts of generative adversarial networks. In this method, the optimizer seeks to minimize the loss (i.e., regression or classification accuracy) across the labeled training examples from the source domain while maximizing the loss of the domain classifier across the source and target data sets (i.e., maximizing the similarity of source and target features). The developed domain adversarial TL method has been implemented on a 1-D CNN backbone network and evaluated for prediction of tool wear propagation, using NASA's milling dataset. Performance has been compared to other TL techniques, and the results indicate that domain adversarial TL can successfully allow DL models trained on certain scenarios to be applied to new target tasks.