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- Taquet, V, et al.
(author)
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Seeds of Life in Space (SOLIS) VI. Chemical evolution of sulfuretted species along the outflows driven by the low-mass protostellar binary NGC1333-IRAS4A
- 2020
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In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 637
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Journal article (peer-reviewed)abstract
- Context. Low-mass protostars drive powerful molecular outflows that can be observed with millimetre and submillimetre telescopes. Various sulfuretted species are known to be bright in shocks and could be used to infer the physical and chemical conditions throughout the observed outflows. Aims. The evolution of sulfur chemistry is studied along the outflows driven by the NGC1333-IRAS4A protobinary system located in the Perseus cloud to constrain the physical and chemical processes at work in shocks. Methods. We observed various transitions from OCS, CS, SO, and SO2 towards NGC1333-IRAS4A in the 1.3, 2, and 3mm bands using the IRAM NOrthern Extended Millimeter Array and we interpreted the observations through the use of the Paris-Durham shock model. Results. The targeted species clearly show different spatial emission along the two outflows driven by IRAS4A. OCS is brighter on small and large scales along the south outflow driven by IRAS4A1, whereas SO2 is detected rather along the outflow driven by IRAS4A2 that is extended along the north east-south west direction. SO is detected at extremely high radial velocity up to +25 km s 1 relative to the source velocity, clearly allowing us to distinguish the two outflows on small scales. Column density ratio maps estimated from a rotational diagram analysis allowed us to confirm a clear gradient of the OCS/SO2 column density ratio between the IRAS4A1 and IRAS4A2 outflows. Analysis assuming non Local Thermodynamic Equilibrium of four SO2 transitions towards several SiO emission peaks suggests that the observed gas should be associated with densities higher than 105 cm 3 and relatively warm (T > 100 K) temperatures in most cases. Conclusions. The observed chemical differentiation between the two outflows of the IRAS4A system could be explained by a different chemical history. The outflow driven by IRAS4A1 is likely younger and more enriched in species initially formed in interstellar ices, such as OCS, and recently sputtered into the shock gas. In contrast, the longer and likely older outflow triggered by IRAS4A2 is more enriched in species that have a gas phase origin, such as SO2.
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| 2. |
- Tschandl, P., et al.
(author)
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Human-computer collaboration for skin cancer recognition
- 2020
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In: Nature Medicine. - : Springer Science and Business Media LLC. - 1078-8956 .- 1546-170X. ; 26
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Journal article (peer-reviewed)abstract
- The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human-computer collaboration in clinical practice. A systematic evaluation of the value of AI-based decision support in skin tumor diagnosis demonstrates the superiority of human-computer collaboration over each individual approach and supports the potential of automated approaches in diagnostic medicine.
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