analysis of surface composition and meteorology at the Zhurong landing site on Mars.

The Zhurong rover of the Tianwen-1 mission landed in southern Utopia Planitia, providing a unique window into the evolutionary history of the Martian lowlands. During its first 110 sols, Zhurong investigated and categorized surface targets into igneous rocks, lithified duricrusts, cemented duricrusts, soils and sands. The lithified duricrusts, analysed by using laser-induced breakdown spectroscopy onboard Zhurong, show elevated water contents and distinct compositions from those of igneous rocks.

Gasdermin E mediates resistance of pancreatic adenocarcinoma to enzymatic digestion through a YBX1-mucin pathway.

Pancreatic ductal adenocarcinoma (PDAC) originates from normal pancreatic ducts where digestive juice is regularly produced. It remains unclear how PDAC can escape autodigestion by digestive enzymes. Here we show that human PDAC tumour cells use gasdermin E (GSDME), a pore-forming protein, to mediate digestive resistance.

From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration.

With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining the elemental compositions of soils, crusts, and rocks. The American Perseverance rover and the Chinese Zhurong rover respectively landed on Mars on February 18 and May 15, 2021, further increase the number of LIBS instruments on Mars. Such an unprecedented situation requires a reinforced research effort on the methods of LIBS spectral data analysis.

Screening ovarian cancers with Raman spectroscopy of blood plasma coupled with machine learning data processing.

The mortality of ovarian cancer is closely related to its poor rate of early detection. In the search of an efficient diagnosis method, Raman spectroscopy of blood features as a promising technique allowing simple, rapid, minimally-invasive and cost-effective detection of cancers, in particular ovarian cancer. Although Raman spectroscopy has been demonstrated to be effective to detect ovarian cancers with respect to normal controls, a binary classification remains idealized with respect to the real clinical practice.

Machine learning-based LIBS spectrum analysis of human blood plasma allows ovarian cancer diagnosis.

Early-stage screening and diagnosis of ovarian cancer represent an urgent need in medicine. Usual ultrasound imaging and cancer antigen CA-125 test when prescribed to a suspicious population still require reconfirmations. Spectroscopic analyses of blood, at the molecular and atomic levels, provide useful supplementary tests when coupled with effective information extraction methods.

Correlation-based carbon determination in steel without explicitly involving carbon-related emission lines in a LIBS spectrum.

As any spectrochemical analysis method, laser-induced breakdown spectroscopy (LIBS) usually relates characteristic spectral lines of the elements or molecules to be analyzed to their concentrations in a material. It is however not always possible for a given application scenario, to rely on such lines because of various practical limitations as well as physical perturbations in the spectrum excitation and recording process. This is actually the case for determination of carbon in steel with LIBS operated in the ambient gas, where the intense C I 193.

Machine learning efficiently corrects LIBS spectrum variation due to change of laser fluence.

This work demonstrates the efficiency of machine learning in the correction of spectral intensity variations in laser-induced breakdown spectroscopy (LIBS) due to changes of the laser pulse energy, such changes can occur over a wide range, from 7.9 to 71.1 mJ in our experiment.