Background
Mutations in an enzyme target are one of the most common mechanisms whereby antibiotic resistance arises. Identification of the resistance mutations in bacteria is essential for understanding the structural basis of antibiotic resistance and design of new drugs. However, the traditionally used experimental approaches to identify resistance mutations were usually labor-intensive and costly.
Results
We present a machine learning (ML)-based classifier for predicting rifampicin (Rif) resistance mutations in bacterial RNA Polymerase subunit β (RpoB). A total of 186 mutations were gathered from the literature for developing the classifier, using 80% of the data as the training set and the rest as the test set. The features of the mutated RpoB and their binding energies with Rif were calculated through computational methods, and used as the mutation attributes for modeling. Classifiers based on five ML algorithms, i.e. decision tree, k nearest neighbors, naïve Bayes, probabilistic neural network and support vector machine, were first built, and a majority consensus (MC) approach was then used to obtain a new classifier based on the classifications of the five individual ML algorithms. The MC classifier comprehensively improved the predictive performance, with accuracy, F-measure and AUC of 0.78, 0.83 and 0.81for training set whilst 0.84, 0.87 and 0.83 for test set, respectively.
Conclusion
The MC classifier provides an alternative methodology for rapid identification of resistance mutations in bacteria, which may help with early detection of antibiotic resistance and new drug discovery.
Speakers have been widely embedded in various electronic devices as a standard configuration. The security vulnerability of microspeakers (such as earphones) is commonly overlooked because it is often assumed that soundproof boundaries, such as walls, can prevent privacy-infringing sound leakage. In this paper, we present the prototype MagEar, an eavesdropping system that leverages magnetic side-channel signals leaked by a microspeaker to recover intelligible human speech. MagEar has sufficiently high sensitivity to detect magnetic fields on the order of nanotesla, exceeding some high-precision magnetometers. It can recover high-quality audio with 90% similarity to the original audio even at a distance of 60 cm. In addition, the MagEar prototype is portable and can be hidden in a headset shell. We have implemented MagEar as a proof-of-concept system and conducted several case studies of eavesdropping on different types of speaker-embedded devices, including earphones, and we have demonstrated the ability to successfully transcribe the recovered speech using automatic speech recognition techniques even when blocked by soundproof walls. We hope that our work can push manufacturers to rethink this security vulnerability of speakers.
CCS CONCEPTS• Security and privacy → Human and societal aspects of security and privacy.
Nanoplastics (NPs) and antibiotics are emerging contaminants that co-occur ubiquitously in coastal environment and pose mixture risk to ecosystems. Here, we report single and combined effects of nano-sized polystyrene (PS)...
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