Water quality of Texas bays (nutrients, trace elements and toxic compounds)

This manuscript is designed to compare the nutrient balances and trace element significance in Texas Bays and Estuaries. The task of assigning water quality criteria in all estuarine waters rests with the federal Environmental Protection Agency. However, the Texas Bays and Estuaries represent a unique range of environments of the U.S. Coast that stand alone and therefore must be assigned standards appropriate to the environment. Therefore we have compared several Texas Bays relative to nutrients and trace elements through an analysis of data from our files, a life history data bank from literature survey, a study of the Corpus Christi area, personal communication with a wide range of individuals and information from the Texas Water Quality Board, Texas Water Development Board, the U.S. Geological Survey and the State Health Department.May 30, 1974Taken in part from a report on Development of Biological Criteria, Establishment of Guidelines for Texas Coast Management IAC-(74-75)-0685 NSF RANN-61-34870xMarine Scienc

Interactions between satellites and plasma

The interactions of a spacecraft with the surrounding, streaming plasma were determined by the following effects: the fade out of the plasma in the wake of the probe, the emission of photoelectrons and secondary electrons, the differential charging of the surface of the probe, and a spatial potential distribution in the vicinity of the space probe. These effects and their importance are discussed and following plasma conditions are considered: (1) geostationary satellite orbits; (2) in the solar wind (HELIOS mission); and (3) in the ionosphere at an altitude of 250 km (the projected OSV on Spacelab). The fundamental models are reviewed

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning. While semantic segmentation algorithms enable 3D image analysis and quantification in many applications, the design of respective specialised solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We propose nnU-Net, a deep learning framework that condenses the current domain knowledge and autonomously takes the key decisions required to transfer a basic architecture to different datasets and segmentation tasks. Without manual tuning, nnU-Net surpasses most specialised deep learning pipelines in 19 public international competitions and sets a new state of the art in the majority of the 49 tasks. The results demonstrate a vast hidden potential in the systematic adaptation of deep learning methods to different datasets. We make nnU-Net publicly available as an open-source tool that can effectively be used out-of-the-box, rendering state of the art segmentation accessible to non-experts and catalyzing scientific progress as a framework for automated method design.Comment: * Fabian Isensee and Paul F. J\"ager share the first authorshi

Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON plans

We participate in the AutoPET II challenge by modifying nnU-Net only through its easy to understand and modify 'nnUNetPlans.json' file. By switching to a UNet with residual encoder, increasing the batch size and increasing the patch size we obtain a configuration that substantially outperforms the automatically configured nnU-Net baseline (5-fold cross-validation Dice score of 65.14 vs 33.28) at the expense of increased compute requirements for model training. Our final submission ensembles the two most promising configurations. At the time of submission our method ranks first on the preliminary test set