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Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data, machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled an extensive and labeled ocean bottom seismometer (OBS) data set from 15 deployments in different tectonic settings, comprising ∼90,000 P and ∼63,000 S manual picks from 13,190 events and 355 stations. We propose PickBlue, an adaptation of the two popular deep learning networks EQTransformer and PhaseNet. PickBlue joint processes three seismometer recordings in conjunction with a hydrophone component and is trained with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. PickBlue significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation of 0.05 s for P‐waves and 0.12 s for S‐waves, and we apply the picker on the Hikurangi Ocean Bottom Tremor and Slow Slip OBS deployment offshore New Zealand. We integrate our data set and trained models into SeisBench to enable an easy and direct application in future deployments. Plain Language Summary: Ocean bottom seismometers (OBS) are seismic stations on the seafloor. Just like their counterparts on land, they record many earthquakes on three component sensors but are additionally equipped with underwater hydrophones. To determine the location of an earthquake, seismologists must precisely measure the arrival times of seismic waves. For onshore data, machine learning (ML) has been highly successful in determining earthquake arrival times. However, the noise and the signal are different in the ocean environment. For example, the recordings can contain whale songs and water layer reverberations and are disturbed by ocean bottom currents. We have assembled an extensive database of ocean bottom recordings and trained artificial neural networks to use the underwater hydrophone information and cope with the ocean noise environment. We demonstrate that the resulting ML picker picks are similar to those of human experts and outperform phase pickers based on land data only. We compare earthquake catalogs based on different pickers created from an OBS deployment offshore New Zealand and demonstrate that PICKBLUE outperforms previous pickers. We make the database and ML picker available with a standard interface so that it is easy for other scientists to apply them in their studies. Key Points: We assembled a database of ocean Bottom Seismometer (OBS) waveforms and manual P and S picks, on which we train PickBlue, a deep learning pickerOur picker significantly outperforms pickers trained with land‐based data with confidence values reflecting the likelihood of outlier picksThe picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records [ABSTRACT FROM AUTHOR]

Presents information on Ludwig Wittgenstein's ideas about dreams. Differences in the views of Wittgenstein and Sigmund Freud on dreaming; Discussion on dream and objectification.

Points out parallels between the Japanese philosopher Shuzo Kuki and the European philosophers Heidegger and Derrida with regard to their philosophical discourses on the idea of style and their respective elaborations of the notion. Aspects of philosophers' backgrounds especially to the German hermeneutic tradition and to some ideas produced by philosophers of the Kyoto School.

Encapsulation of primary bovine adrenocortical cells in alginate is an efficacious model of a bioartificial adrenal cortex. Such a bioartificial adrenal cortex can be used for the restoration of lost adrenal function in vivo as well as for in vitro modeling of the adrenal microenvironment and for investigation of cell-cell interactions in the adrenals. The aim of this work was the optimization of a bioartificial adrenal cortex, that is the generation of a highly productive, self-regenerating, long-term functioning and immune tolerant bioartificial organ. To achieve this, it is necessary that adrenocortical stem and progenitor cells are present in the bioartificial gland, as these undifferentiated cells play important roles in the function of the mature gland. Here, we verified the presence of adrenocortical progenitors in cultures of bovine adrenocortical cells, studied the dynamics of their appearance and growth and determined the optimal time point for cell encapsulation. These procedures increased the functional life span and reduced the immunogenicity of the bioartificial adrenal cortex. This model allows the use of the luteinizing hormone-releasing hormone (LHRH) agonist triptorelin, the neuropeptide bombesin, and retinoic acid to alter cell number and the release of cortisol over long periods of time.