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Confocal Raman Microscopy with Adaptive Optics

Confocal Raman microscopy is a precise and label-free technique for analyzing thick samples at the microscale, but its use is often limited by weak Raman signals. Sample inhomogeneities introduce wavefront aberrations, further diminishing signal strength and requiring longer acquisition times. In this study, we present the first application of Adaptive Optics in confocal Raman microscopy to correct these aberrations, achieving substantial improvements in signal intensity and image quality. This approach integrates seamlessly with commercial microscopes without the need for hardware modifications. It utilizes a wavefront sensorless method, relying on an optofluidic, transmissive spatial light modulator attached to the microscope nosepiece to measure and correct aberrations. Experimental validation shows effective correction of aberrations in artificial scatterers and mouse brain tissue, enhancing spatial resolution and increasing signal intensity by up to 3.5 times. These results establ...
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PDF LATEX equations to latex code by deep learning (pix2tex)

Have you ever asked yourself how to avoid transcribing printed (PDF) math equations to latex code automatically? This post shows how to convert any latex equation (even other fonts) into latex code immediately. The main developer is Lukas in his git repository https://github.com/lukas-blecher/LaTeX-OCR. Clone or download the repository. Then install the required libraries (check the git link out).  

Image is taken from https://github.com/lukas-blecher/LaTeX-OCR

Also, install the next libraries or programs for the GUI interface (if you hold any issue and then try to install requirements again):

conda install pytorch torchvision -c pytorch
Install microsoft visual studio C++
pip install requirements (check out the git link)
Finally, run the gui.py and start converting image equations to latex code. 

References
https://github.com/lukas-blecher/LaTeX-OCR

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