In situ 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. The cemented duricrusts are likely formed via water vapor-frost cycling at the atmosphere-soil interface, as supported by the local meteorological conditions. Soils and sands contain elevated magnesium and water, attributed to both hydrated magnesium salts and adsorbed water. The compositional and meteorological evidence indicates potential Amazonian brine activities and present-day water vapor cycling at the soil-atmosphere interface. Searching for further clues to water-related activities and determining the water source by Zhurong are critical to constrain the volatile evolution history at the landing site.

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. GSDME facilitates the tumour cells to express mucin 1 and mucin 13, which form a barrier to prevent chymotrypsin-mediated destruction. Inoculation of GSDME-/- PDAC cells results in subcutaneous but not orthotopic tumour formation in mice. Inhibition or knockout of mucin 1 or mucin 13 abrogates orthotopic PDAC growth in NOD-SCID mice. Mechanistically, GSDME interacts with and transports YBX1 into the nucleus where YBX1 directly promotes mucin expression. This GSDME-YBX1-mucin axis is also confirmed in patients with PDAC. These findings uncover a unique survival mechanism of PDAC cells in pancreatic microenvironments.

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. This work considered a population of 95 woman patients initially suspected of an ovarian cancer and finally fixed with a cancer or a cyst. Additionally, 79 normal controls completed the ensemble of samples. Such sample collection proposed us a study case where a ternary classification should be realized with Raman spectroscopy of the collected blood samples coupled with suitable spectroscopic data treatment algorithms. In the medical as well as data points of view, the appearance of the cyst case considerably reduces the distances among the different populations and makes their distinction much more difficult, since the intermediate cyst case can share the specific features of the both cancer and normal cases. After a proper spectrum pretreatment, we first demonstrated the evidence of different behaviors among the Raman spectra of the 3 types of samples. Such difference was further visualized in a high dimensional space, where the data points of the cancer and the normal cases are separately clustered, whereas the data of the cyst case were scattered into the areas respectively occupied by the cancer and normal cases. We finally developed and tested an ensemble of models for a ternary classification with 2 consequent steps of binary classifications, based on machine learning algorithms, allowing identification with sensitivity and specificity of 81.0% and 97.3% for cancer samples, 63.6% and 91.5% for cyst samples, 100% and 90.6% for normal samples.

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. Although the matrix effects correspond to a general issue in LIBS, they become accentuated in the case of rock analysis for Mars exploration, because of the large variation of rock compositions leading to the chemical matrix effect, and the difference in surface physical properties between laboratory standards (in pressed powder pellet, glass or ceramic) used to establish calibration models and natural rocks encountered on Mars, leading to the physical matrix effect. The chemical matrix effect has been tackled in the ChemCam project with large sets of laboratory standards offering a good representation of various compositions of Mars rocks. The present work more specifically deals with the physical matrix effect which is still lacking a satisfactory solution. The approach consists in introducing transfer learning in LIBS data treatment. For the specific application of total alkali-silica (TAS) classification of rocks (either with a polished surface or in the raw state), the results show a significant improvement in the ability to predict of pellet-based models when trained together with suitable information from rocks in a procedure of transfer learning. The correct TAS classification rate increases from 25% for polished rocks and 33.3% for raw rocks with a machine learning model, to 83.3% with a transfer learning model for both types of rock samples.

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. Laser-induced breakdown spectroscopy (LIBS) was employed in this work to record the elemental fingerprint of human blood plasma. A machine learning data treatment process was developed combining feature selection and regression with a back-propagation neural network, resulting in classification models for cancer detection among 176 blood plasma samples collected from patients, including also ovarian cyst and normal cases. Cancer diagnosis sensitivity and specificity of respectively 71.4% and 86.5% were obtained for randomly selected validation samples.

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 193.090 nm VUV line is absorbed, while the C I 247.856 nm near UV one heavily interferes with iron lines. This work uses machine learning, especially a combination of least absolute shrinkage and selection operator (LASSO) for spectral feature selection and back-propagation neural networks (BPNN) for regression, to correlate a LIBS spectrum to the carbon concentration for its precise determination without explicitly including carbon-related emission lines in the selected spectral features.

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. The developed multivariate correction model led to a precise determination of the concentration of a minor element (magnesium for instance) in the samples (aluminum alloys in this work) with a precision of 6.3% (relative standard deviation, RSD) using the LIBS spectra affected by the laser pulse energy change. A comparison to the classical univariate corrections with laser pulse energy, total spectral intensity, ablation crater volume and plasma temperature, further highlights the significance of the developed method.