Temporal association between antibiotic use and resistance in Gram-negative bacteria

The β-lactam/lactamase inhibitors (BLBLIs) combination drugs are considered an effective alternative to carbapenems. However, there is a growing concern that the increased use of BLBLIs may lead to increased resistance. This study determined the temporal association between the consumption of BLBLI and the antimicrobial resistance in Gram-negative bacteria. In this retrospective study, electronic data on the Gram-negative bacterial isolates, including A. baumannii, P. aeruginosa, E. coli, and K. pneumoniae from in-patients and susceptibility testing results were retrieved from the medical records of the clinical laboratory. A linear regression and cross-correlation analysis were performed on the acquired data. Increasing trends (p<0.05) in the consumption of BIBLI and carbapenem with a median use of 27.68 and 34.46 DDD/1000 PD per quarter were observed, respectively. A decreased trend (p=0.023) in the consumption of fluoroquinolones with a median use of 29.13 DDD/1000 PD per quarter was observed. The resistance rate of K. pneumoniae was synchronized with the BIBLI and carbapenem consumptions with a correlation coefficient of 0.893 (p=0.012) and 0.951 (p=0.016), respectively. The cross-correlation analysis against the consumption of BIBLI and meropenem resistant K. pneumoniae was peaked at 0-quarter lag (r=951, p=0.016). There was an increasing trend in the consumption of BLBLI and carbapenems. The increasing trend in the rates of resistance to piperacillin/tazobactam, in line with the increasing consumption of BLBLI, suggests that BLBLI has to be used with caution and cannot be directly considered as a long-term alternative to carbapenems.

A dual-ended 400 km OFDR for vibration detection

Optical frequency-domain reflectometer (OFDR) has been widely used in vibration detection because of its unique advantages of simple configuration and high spatial resolution. Based on remote fiber amplification, an unrepeatered OFDR is experimentally investigated for vibration monitoring. To locate the vibration, we present an algorithm by calculating segmental cross-correlation between the beating signals with and without disturbances on the sensing fiber. It is shown that the OFDR demonstrates the ability of detecting the vibration over 222 km testing distance (112 km + 110 km). After sensing the first spool fiber of 112 km, the remnant laser is amplified by a remote-pumped EDFA before proceeding to probe the vibration in the second spool one of 110 km. To be specific, the PZT-induced vibrations positioned at z=110.9 km and z=220.9 km are both detected. More importantly, the OFDR system can be extended to operate in bi-directional sensing mode and to double detection range from 200 km to 400 km.

EEG–EMG coupling as a hybrid method for steering detection in car driving settings

Understanding mental processes in complex human behavior is a key issue in driving, representing a milestone for developing user-centered assistive driving devices. Here, we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left and right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings of 128-channel EEG and EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side using cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate the steering side earlier relative to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy, and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results prove how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

Active Binaural Auditory Perceptual System for a Socially Interactive Humanoid Robot

Sound Source Localization (SSL) and gaze shift to the sound source behavior is an integral part of a socially interactive humanoid robot perception system. In noisy and reverberant environments, it is non-trivial to estimate the location of a sound source and accurately shift gaze in its direction. Previous SSL algorithms are deficient in the optimum approximation of distance to audio sources and to accurately detect, interpret, and differentiate the actual sound from comparable sound sources due to challenging acoustic environments. In this article, a learning-based model is presented to achieve noiseless and reverberation-resistant sound source localization in the real-world scenarios. The proposed system utilizes a multi-layered Gaussian Cross-Correlation with Phase Transform (GCC-PHAT) signal processing technique as a baseline for a Generalized Cross Correlation Convolution Neural Network (GCC-CNN) model. The proposed model is integrated with an efficient rotation algorithm to predict and orient toward the sound source. The performance of the proposed method is compared with the state-of-art deep network-based sound source localization methods. The findings of the proposed method outperform the existing neural network-based approaches by achieving the highest accuracy of 96.21% for an active binaural auditory perceptual system.

Identification of Corrosion Minerals Using Shortwave Infrared Hyperspectral Imaging

In this study, we propose a new method to identify corrosion minerals in carbon steel using hyperspectral imaging (HSI) in the shortwave infrared range (900–1700 nm). Seven samples were artificially corroded using a neutral salt spray test and examined using a hyperspectral camera. A normalized cross-correlation algorithm is used to identify four different corrosion minerals (goethite, magnetite, lepidocrocite and hematite), using reference spectra. A Fourier Transform Infrared spectrometer (FTIR) analysis of the scraped corrosion powders was used as a ground truth to validate the results obtained by the hyperspectral camera. This comparison shows that the HSI technique effectively detects the dominant mineral present in the samples. In addition, HSI can also accurately predict the changes in mineral composition that occur over time.