Studies of a Lacustrine-Volcanic Mars Analog Field Site with Mars-2020-like Instruments

On the upcoming Mars‐2020 rover two remote sensing instruments, Mastcam‐Z and SuperCam, and two microscopic proximity science instruments, SHERLOC and PIXL, will collect compositional (mineralogy, chemistry, and organics) data essential for paleoenvironmental reconstruction. The synergies between and limitations of these instruments were evaluated via study of a Mars analog field site in the Mojave Desert, using instruments approximating the data that will be returned by Mars‐2020. A ground truth dataset was generated for comparison to validate the results. The site consists of a succession of clay‐rich mudstones of lacustrine origin, interbedded tuffs, a carbonate‐silica travertine deposit, and gypsiferous mudstone strata. The major geological units were mapped successfully using simulated Mars‐2020 data. Simulated Mastcam‐Z data identified unit boundaries and Fe‐bearing weathering products. Simulated SuperCam passive shortwave infrared and green Raman data were essential in identifying major mineralogical composition and changes in lacustrine facies at distance; this was possible even with spectrally downsampled passive IR data. LIBS and simulated PIXL data discriminated and mapped major element chemistry. Simulated PIXL revealed mm‐scale zones enriched in zirconium, of interest for age dating. SHERLOC‐like data mapped sulfate and carbonate at sub‐mm scale; silicates were identified with increased laser pulses/spot or by averaging of hundreds of spectra. Fluorescence scans detected and mapped varied classes of organics in all samples, characterized further with follow‐on spatially targeted deep‐UV Raman spectra. Development of dedicated organics spectral libraries is needed to aid interpretation. Given these observations, the important units in the outcrop would be sampled and cached for sample return

Studies of a Lacustrine-Volcanic Mars Analog Field Site with Mars-2020-like Instruments

On the upcoming Mars‐2020 rover two remote sensing instruments, Mastcam‐Z and SuperCam, and two microscopic proximity science instruments, SHERLOC and PIXL, will collect compositional (mineralogy, chemistry, and organics) data essential for paleoenvironmental reconstruction. The synergies between and limitations of these instruments were evaluated via study of a Mars analog field site in the Mojave Desert, using instruments approximating the data that will be returned by Mars‐2020. A ground truth dataset was generated for comparison to validate the results. The site consists of a succession of clay‐rich mudstones of lacustrine origin, interbedded tuffs, a carbonate‐silica travertine deposit, and gypsiferous mudstone strata. The major geological units were mapped successfully using simulated Mars‐2020 data. Simulated Mastcam‐Z data identified unit boundaries and Fe‐bearing weathering products. Simulated SuperCam passive shortwave infrared and green Raman data were essential in identifying major mineralogical composition and changes in lacustrine facies at distance; this was possible even with spectrally downsampled passive IR data. LIBS and simulated PIXL data discriminated and mapped major element chemistry. Simulated PIXL revealed mm‐scale zones enriched in zirconium, of interest for age dating. SHERLOC‐like data mapped sulfate and carbonate at sub‐mm scale; silicates were identified with increased laser pulses/spot or by averaging of hundreds of spectra. Fluorescence scans detected and mapped varied classes of organics in all samples, characterized further with follow‐on spatially targeted deep‐UV Raman spectra. Development of dedicated organics spectral libraries is needed to aid interpretation. Given these observations, the important units in the outcrop would be sampled and cached for sample return

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

International audienceTwo acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. Methods: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable. Findings: The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90–0·95) in EARLI and 0·88 (0·84–0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81–0·94] vs 0·92 [0·88–0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group). Interpretation: Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated. Funding: US National Institutes of Health and European Society of Intensive Care Medicine