Machine-Learning-Driven New Geologic Discoveries at Mars Rover Landing Sites: Jezero Crater and NE Syrtis

A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) images. Its utility in detecting small exposures of uncommon phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the Jezero crater floor and in fluvial deposits at NE Syrtis. Jarosite and silica are found on the Jezero crater floor while chlorite-smectite and Al phyllosilicates are found in the Jezero crater walls. These detections point to a multi-stage, multi-chemistry history of water in Jezero crater and the surrounding region and provide new information for guiding the Mars-2020 rover's landed exploration. In particular, the akaganeite, silica, and jarosite in the floor deposits suggest either a later episode of salty, Fe-rich waters that post-date the Jezero crater delta or groundwater alteration of portions of the Jezero crater sedimentary sequence

Machine-Learning-Driven New Geologic Discoveries at Mars Rover Landing Sites: Jezero and NE Syrtis

A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) imagery. Its utility in detecting rare phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the Jezero crater floor and in fluvial deposits at NE Syrtis. Jarosite and silica are found on the Jezero crater floor while chlorite-smectite and Al phyllosilicates are found in the Jezero crater walls. These detections point to a multi-stage, multi-chemistry history of water in Jezero crater and the surrounding region and provide new information for guiding the Mars-2020 rover's landed exploration. In particular, the akaganeite, silica, and jarosite in the floor deposits suggest either a later episode of salty, Fe-rich waters that post-date Jezero delta or groundwater alteration of portions of the Jezero sedimentary sequence

Machine-Learning-Driven New Geologic Discoveries at Mars Rover Landing Sites: Jezero Crater and NE Syrtis

A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) images. Its utility in detecting small exposures of uncommon phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the Jezero crater floor and in fluvial deposits at NE Syrtis. Jarosite and silica are found on the Jezero crater floor while chlorite-smectite and Al phyllosilicates are found in the Jezero crater walls. These detections point to a multi-stage, multi-chemistry history of water in Jezero crater and the surrounding region and provide new information for guiding the Mars-2020 rover's landed exploration. In particular, the akaganeite, silica, and jarosite in the floor deposits suggest either a later episode of salty, Fe-rich waters that post-date the Jezero crater delta or groundwater alteration of portions of the Jezero crater sedimentary sequence

Automated assessment of disease progression in Acute Myeloid Leukemia by probabilistic analysis of flow cytometry data

Objective: Flow cytometry (FC) is a widely acknowledged technology in diagnosis of acute myeloid leukemia (AML) and has been indispensable in determining progression of the disease. Although FC plays a key role as a post-therapy prognosticator and evaluator of therapeutic efficacy, the manual analysis of cytometry data is a barrier to optimization of reproducibility and objectivity. This study investigates the utility of our recently introduced non-parametric Bayesian framework in accurately predicting the direction of change in disease progression in AML patients using FC data. Methods: The highly flexible non-parametric Bayesian model based on the infinite mixture of infinite Gaussian mixtures is used for jointly modeling data from multiple FC samples to automatically identify functionally distinct cell populations and their local realizations. Phenotype vectors are obtained by characterizing each sample by the proportions of recovered cell populations, which are in turn used to predict the direction of change in disease progression for each patient. Results: We used 200 diseased and non-diseased immunophenotypic panels for training and tested the system with 36 additional AML cases collected at multiple time points. The proposed framework identified the change in direction of disease progression with accuracies of 90% (9 out of 10) for relapsing cases and 100% (26 out of 26) for the remaining cases. Conclusions: We believe that these promising results are an important first step towards the development of automated predictive systems for disease monitoring and continuous response evaluation. Significance: Automated measurement and monitoring of therapeutic response is critical not only for objective evaluation of disease status prognosis but also for timely assessment of treatment strategies

Moderators of the effect of psychological interventions on depression and anxiety in cardiac surgery patients: A systematic review and meta-analysis

Cardiac surgery patients may be provided with psychological interventions to counteract depression and anxiety associated with surgical procedures. This systematic review and meta-analysis investigated whether intervention efficacy was impacted by type of cardiac procedure/cardiac event; control condition content; intervention duration; intervention timing; facilitator type; and risk of bias. MEDLINE, EMBASE, and PsycINFO were searched for randomized controlled trials comparing anxiety and depression outcomes, pre and post psychological and cardiac interventions. Twenty-four studies met the inclusion criteria for the systematic review (N = 2718) and 16 of those were meta-analysed (N = 1928). Depression and anxiety outcomes were reduced more in interventions that lasted longer, were delivered post-surgery, and by trained health professionals. Depression (but not anxiety) was reduced more when the experimental intervention was compared to an ‘alternative’ intervention, and when the intervention was delivered to coronary artery bypass graft patients. Anxiety (but not depression) was decreased more when interventions were delivered to implantable cardioverter defibrillator patients, and were of ‘high’ or ‘unclear’ risk of bias. In addition to estimating efficacy, future work in this domain needs to take into account the moderating effects of intervention, sample, and study characteristics

Challenges in the Search for Perchlorate and Other Hydrated Minerals With 2.1-μm Absorptions on Mars

A previously unidentified artifact has been found in Compact Reconnaissance Imaging Spectrometer for Mars targeted I/F data. It exists in a small fraction (<0.05%) of pixels within 90% of images investigated and occurs in regions of high spectral/spatial variance. This artifact mimics real mineral absorptions in width and depth and occurs most often at 1.9 and 2.1 μm, thus interfering in the search for some mineral phases, including alunite, kieserite, serpentine, and perchlorate. A filtering step in the data processing pipeline, between radiance and I/F versions of the data, convolves narrow artifacts (“spikes”) with real atmospheric absorptions in these wavelength regions to create spurious absorption-like features. The majority of previous orbital detections of alunite, kieserite, and serpentine we investigated can be confirmed using radiance and raw data, but few to none of the perchlorate detections reported in published literature remain robust over the 1.0- to 2.65-μm wavelength range

Gecko-Inspired Biocidal Organic Nanocrystals Initiated from a Pencil-Drawn Graphite Template

The biocidal properties of gecko skin and cicada wings have inspired the synthesis of synthetic surfaces decorated with high aspect ratio nanostructures that inactivate microorganisms. Here, we investigate the bactericidal activity of oriented zinc phthalocyanine (ZnPc) nanopillars grown using a simple pencil-drawn graphite templating technique. By varying the evaporation time, nanopillars initiated from graphite that was scribbled using a pencil onto silicon substrates were optimized to yield a high inactivation of the Gram-negative bacteria, Escherichia coli. We next adapted the procedure so that analogous nanopillars could be grown from pencil-drawn graphite scribbled onto stainless steel, flexible polyimide foil, and glass substrates. Time-dependent bacterial cytotoxicity studies indicate that the oriented nanopillars grown on all four substrates inactivated up to 97% of the E. coli quickly, in 15 min or less. These results suggest that organic nanostructures, which can be easily grown on a broad range of substrates hold potential as a new class of biocidal surfaces that kill microbes quickly and potentially, without spreading antibiotic-resistance genes

A machine learning toolkit for CRISM image analysis

Hyperspectral images collected by remote sensing have played a significant role in the discovery of aqueous alteration minerals, which in turn have important implications for our understanding of the changing habitability on Mars. Traditional spectral analyzes based on summary parameters have been helpful in converting hyperspectral cubes into readily visualizable three channel maps highlighting high-level mineral composition of the Martian terrain. These maps have been used as a starting point in the search for specific mineral phases in images. Although the amount of labor needed to verify the presence of a mineral phase in an image is quite limited for phases that emerge with high abundance, manual processing becomes laborious when the task involves determining the spatial extent of detected phases or identifying small outcrops of secondary phases that appear in only a few pixels within an image. Thanks to extensive use of remote sensing data and rover expeditions, significant domain knowledge has accumulated over the years about mineral composition of several regions of interest on Mars, which allow us to collect reliable labeled data required to train machine learning algorithms. In this study we demonstrate the utility of machine learning in two essential tasks for hyperspectral data analysis: nonlinear noise removal and mineral classification. We develop a simple yet effective hierarchical Bayesian model for estimating distributions of spectral patterns and extensively validate this model for mineral classification on several test images. Our results demonstrate that machine learning can be highly effective in exposing tiny outcrops of specific phases in orbital data that are not uncovered by traditional spectral analysis. We package implemented scripts, documentation illustrating use cases, and pixel-scale training data collected from dozens of well-characterized images into a new toolkit. We hope that this new toolkit will provide advanced and effective processing tools and improve community’s ability to map compositional units in remote sensing data quickly, accurately, and at scale