78 research outputs found
The Butterfly Fauna Of The Italian Maritime Alps:Results Of The «Edit» Project
Bonelli, Simona, Barbero, Francesca, Casacci, Luca Pietro, Cerrato, Cristiana, Balletto, Emilio (2015): The butterfly fauna of the Italian Maritime Alps: results of the EDIT project. Zoosystema 37 (1): 139-167, DOI: 10.5252/z2015n1a6, URL: http://dx.doi.org/10.5252/z2015n1a
Plasma protein binding of prednisolone in normal volunteers and arthritic patients
The plasma binding of prednisolone was studied in twenty normal volunteers and twenty rheumatoid arthritis patients. An in vitro assessment of the binding following the addition of prednisolone, prednisone, and hydrocortisone to the plasmas obtained from the subjects showed significant differences in the percentage of prednisolone bound. However, the differences observed were regarded as clinically insignificant. The plasma protein binding was determined by an in vitro equilibrium dialysis of the individual plasma samples at 37° C. Prednisolone levels on both sides of the dialysis membrane were determined using radioactivity and HPLC analytical methodologies. The percentages of prednisolone bound calculated from the analytical results of either the radiochemical or HPLC method were not significantly different. The change in the percentage of prednisolone bound to plasma proteins was studied as a function of the total prednisolone plasma concentration in a normal volunteer and in a systemic lupus erythematosis patient. As a result of prednisolone binding to both transcortin and albumin, the binding of prednisolone changes as a function of prednisolone concentration. The binding data were fitted using nonlinear least squares regression, and the affinity constants for the binding of prednisolone to transcortin and albumin were estimated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46638/1/228_2004_Article_BF00568200.pd
ExoClock Project III: 450 new exoplanet ephemerides from ground and space observations
The ExoClock project has been created with the aim of increasing the
efficiency of the Ariel mission. It will achieve this by continuously
monitoring and updating the ephemerides of Ariel candidates over an extended
period, in order to produce a consistent catalogue of reliable and precise
ephemerides. This work presents a homogenous catalogue of updated ephemerides
for 450 planets, generated by the integration of 18000 data points from
multiple sources. These sources include observations from ground-based
telescopes (ExoClock network and ETD), mid-time values from the literature and
light-curves from space telescopes (Kepler/K2 and TESS). With all the above, we
manage to collect observations for half of the post-discovery years (median),
with data that have a median uncertainty less than one minute. In comparison
with literature, the ephemerides generated by the project are more precise and
less biased. More than 40\% of the initial literature ephemerides had to be
updated to reach the goals of the project, as they were either of low precision
or drifting. Moreover, the integrated approach of the project enables both the
monitoring of the majority of the Ariel candidates (95\%), and also the
identification of missing data. The dedicated ExoClock network effectively
supports this task by contributing additional observations when a gap in the
data is identified. These results highlight the need for continuous monitoring
to increase the observing coverage of the candidate planets. Finally, the
extended observing coverage of planets allows us to detect trends (TTVs -
Transit Timing Variations) for a sample of 19 planets. All products, data, and
codes used in this work are open and accessible to the wider scientific
community.Comment: Recommended for publication to ApJS (reviewer's comments
implemented). Main body: 13 pages, total: 77 pages, 7 figures, 7 tables. Data
available at http://doi.org/10.17605/OSF.IO/P298
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with Machine Learning
The Euclid mission is expected to image millions of galaxies with high
resolution, providing an extensive dataset to study galaxy evolution. We
investigate the application of deep learning to predict the detailed
morphologies of galaxies in Euclid using Zoobot a convolutional neural network
pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot
for emulated Euclid images, generated based on Hubble Space Telescope COSMOS
images, and with labels provided by volunteers in the Galaxy Zoo: Hubble
project. We demonstrate that the trained Zoobot model successfully measures
detailed morphology for emulated Euclid images. It effectively predicts whether
a galaxy has features and identifies and characterises various features such as
spiral arms, clumps, bars, disks, and central bulges. When compared to
volunteer classifications Zoobot achieves mean vote fraction deviations of less
than 12% and an accuracy above 91% for the confident volunteer classifications
across most morphology types. However, the performance varies depending on the
specific morphological class. For the global classes such as disk or smooth
galaxies, the mean deviations are less than 10%, with only 1000 training
galaxies necessary to reach this performance. For more detailed structures and
complex tasks like detecting and counting spiral arms or clumps, the deviations
are slightly higher, around 12% with 60000 galaxies used for training. In order
to enhance the performance on complex morphologies, we anticipate that a larger
pool of labelled galaxies is needed, which could be obtained using
crowdsourcing. Finally, our findings imply that the model can be effectively
adapted to new morphological labels. We demonstrate this adaptability by
applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can
readily predict morphological catalogues for Euclid images.Comment: 27 pages, 26 figures, 5 tables, submitted to A&
Recommended from our members
ExoClock Project. III. 450 New Exoplanet Ephemerides from Ground and Space Observations
The ExoClock project has been created to increase the efficiency of the Ariel mission. It will achieve this by continuously monitoring and updating the ephemerides of Ariel candidates, in order to produce a consistent catalog of reliable and precise ephemerides. This work presents a homogenous catalog of updated ephemerides for 450 planets, generated by the integration of ∼18,000 data points from multiple sources. These sources include observations from ground-based telescopes (the ExoClock network and the Exoplanet Transit Database), midtime values from the literature, and light curves from space telescopes (Kepler, K2, and TESS). With all the above, we manage to collect observations for half of the postdiscovery years (median), with data that have a median uncertainty less than 1 minute. In comparison with the literature, the ephemerides generated by the project are more precise and less biased. More than 40% of the initial literature ephemerides had to be updated to reach the goals of the project, as they were either of low precision or drifting. Moreover, the integrated approach of the project enables both the monitoring of the majority of the Ariel candidates (95%), and also the identification of missing data. These results highlight the need for continuous monitoring to increase the observing coverage of the candidate planets. Finally, the extended observing coverage of planets allows us to detect trends (transit-timing variations) for a sample of 19 planets. All the products, data, and codes used in this work are open and accessible to the wider scientific community
Euclid preparation XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning
The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samples, we investigate the application of deep learning in predicting the detailed morphologies of galaxies in Euclid using Zoobot, a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for use with emulated Euclid images generated based on Hubble Space Telescope COSMOS images and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We experimented with different numbers of galaxies and various magnitude cuts during the training process. We demonstrate that the trained Zoobot model successfully measures detailed galaxy morphology in emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features, such as spiral arms, clumps, bars, discs, and central bulges. When compared to volunteer classifications, Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy of above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes, such as disc or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. On the other hand, for more detailed structures and complex tasks, such as detecting and counting spiral arms or clumps, the deviations are slightly higher, of namely around 12% with 60000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowd sourcing. We estimate that, with our model, the detailed morphology of approximately 800 million galaxies of the Euclid Wide Survey could be reliably measured and that approximately 230 million of these galaxies would display features. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images
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