BLE beacons for sample position estimation in a life science automation laboratory

Estimation of the sample position is essential for working process monitoring and management in the life science automation laboratory.Bluetooth low-energy(BLE)beacons have the advantages of low price,small size and low energy consumption,which make them a promising solution for sample position estimation in the automated laboratory.Several fingerprinting models have been proposed to achieve indoor localization with the received signal strength(RSS)data.However,most of the research depends on intensive beacon installation.Proximity estimation,which depends entirely on one beacon,is more suitable for sample position estimation in large automated laboratories.The complexity of the life science automation laboratory environment brings challenges to the traditional path loss model(PLM),which is a widely used radio wave propagation model-based proximity estimation method.In this paper,BLE sensing devices for sample position estimation are proposed.The BLE beacon-based proximity estimation is discussed in the framework of machine learning,in which the support vector regression(SVR)is utilized to model the nonlinear relationship between the RSS data and distance,and the Kalman filter is utilized to decrease the RSS data deviation.The experimental results over different environments indicate that the SVR outperforms the PLM significantly,and provides 1 m absolute errors for more than 95%of the testing samples.The Kalman filter brings benefits to stable distance predictions.Apart from proximity-based sample position estimation,the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.

Correcting of the unexpected localization measurement for indoor automatic mobile robot transportation based on a neural network

The increasing use of mobile robots in laboratory settings has led to a higher degree of laboratory automation.However,when mobile robots move in laboratory environments,mechanical errors,environmental disturbances and signal interruptions are inevitable.This can compromise the accuracy of the robot’s localization,which is crucial for the safety of staff,robots and the laboratory.A novel time-series predicting model based on the data processing method is proposed to handle the unexpected localization measurement of mobile robots in laboratory environments.The proposed model serves as an auxiliary localization system that can accurately correct unexpected localization errors by relying solely on the historical data of mobile robots.The experimental results demonstrate the effectiveness of this proposed method.

Rapid, automated, and reliable antimicrobial susceptibility test from positive blood culture by CAST‐R

Antimicrobial susceptibility tests(ASTs)are pivotal in combating multidrug resistant pathogens,yet they can be time‐consuming,labor‐intensive,and unstable.Using the AST of tigecycline for sepsis as the main model,here we establish an automated system of Clinical Antimicrobials Susceptibility Test Ramanometry(CAST‐R),based on D2O‐probed Raman microspectroscopy.Featuring a liquid robot for sample pretreatment and a machine learning‐based control scheme for data acquisition and quality control,the 3‐h,automated CAST‐R process accelerates AST by>10‐fold,processes 96 paralleled antibiotic‐exposure reactions,and produces high‐quality Raman spectra.The Expedited Minimal Inhibitory Concentration via Metabolic Activity is proposed as a quantitative and broadly applicable parameter for metabolism‐based AST,which shows 99%essential agreement and 93%categorical agreement with the broth microdilution method(BMD)when tested on 100 Acinetobacter baumannii isolates.Further tests on 26 clinically positive blood samples for eight antimicrobials,including tigecycline,meropenem,ceftazidime,ampicillin/sulbactam,oxacillin,clindamycin,vancomycin,and levofloxacin reveal 93%categorical agreement with BMD‐based results.The automation,speed,reliability,and general applicability of CAST‐R suggest its potential utility for guiding the clinical administration of antimicrobials.