In vivo fiber photometry of neural activity in response to optogenetically manipulated inputs in freely moving mice

In vito fber photometry is a powerful technique to analyze the dy namics of population neurons during fiunctional study of neuroscience.Here,we introduced a detailed protocol for fiber photometry-based calciun reording in freely moving mice,covering from virus injection,fiber stub insertion,optogenetical stimulation to data procurement and analysis.Furthemnore,we applied this protocol to explore neuronal activity of mice latenal-posterior(LP)thalaric nucleus in response to optogenetical stimulation of primary visual cortex(V1)neurons,and explore axon clusters activity of optogenetically evoked V1 neurons.Final confirmation of virus-based protein expression in V1 and precise fber insertion indicated that the surgery procedure of this protocol is reliable for functional calcium recording.The scripts for data analysis and some tips in our protocol are provided in details.Together,this protocol is simple,low-cost,and effective for neuronal activity detection by fiber photometry,which will hep neuroscience researchers to carry out fiunctional and behavioral study in vivo.

Rapid bacteria identification using structured illumination microscopy and machine learning

Traditionally,optical microscopy is used to visualize the morphological features of pathogenic bacteria,of which the features are further used for the detection and ident ification of the bacteria.However,due to the resolution limitation of conventional optical microscopy as well as the lack of standard pattern library for bacteria identification,the ffectiveness of this optical microscopy-based method is limited.Here,we reported a pilot study on a combined use of Structured Illumination Microscopy(SIM)with machine learning for rapid bacteria identification.After applying machine learning to the SIM image datasets from three model bacteria(including Escherichia coli,Mycobacterium smegmatis,and Pseudomonas aeruginosa),we obtained a classifcation accuracy of up to 98%.This study points out a promising possibility for rapid bacterial identification by morphological features.