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- Taquet, V, et al.
(författare)
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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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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 637
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Tidskriftsartikel (refereegranskat)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. |
- Marti-Bonmati, Luis, et al.
(författare)
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Considerations for artificial intelligence clinical impact in oncologic imaging : an AI4HI position paper
- 2022
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Ingår i: Insights into Imaging. - : Springer. - 1869-4101. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology.
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