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The rapid identification of bacterial pathogens in clinical samples like blood, urine, pus, and sputum is the need of the hour. Conventional bacterial identification methods like culturing and nucleic acid-based amplification have limitations like poor sensitivity, high cost, slow turnaround time, etc. Raman spectroscopy, a label-free and noninvasive technique, has overcome these drawbacks by providing rapid biochemical signatures from a single bacterium. Raman spectroscopy combined with chemometric methods has been used effectively to identify pathogens. However, a robust approach is needed to utilize Raman features for accurate classification while dealing with complex data sets such as spectra obtained from clinical isolates, showing high sample-to-sample heterogeneity. In this study, we have used Raman spectroscopy-based identification of pathogens from clinical isolates using a deep transfer learning approach at the single-cell level resolution. We have used the data-augmentation method to increase the volume of spectra needed for deep-learning analysis. Our ResNet model could specifically extract the spectral features of eight different pathogenic bacterial species with a 99.99% classification accuracy. The robustness of our model was validated on a set of blinded data sets, a mix of cultured and noncultured bacterial isolates of various origins and types. Our proposed ResNet model efficiently identified the pathogens from the blinded data set with high accuracy, providing a robust and rapid bacterial identification platform for clinical microbiology.

A major effort on the part of Real estate firms is to increase and retain their customers and it is very essential that to know the factors influencing customers’ satisfaction and retention. This study is done with the purposes of identifying the extrinsic factors that affect the purchasing decision of the real estate customers of villas and apartments. This study uses survey questionnaires to obtain information from real estate consumers. The study was conducted in Thrissur district. The study found that the most influencing one in extrinsic factors which affect the purchase decision of real estate consumers is the availability of the facility of waste disposal. The study also discuss the opinion of real estate consumers about the changes needed in the structure of flats and villas in the light of natural calamities occurred in Kerala.