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Ensuring optimal climate conditions during cultivation is essential for successful and reproducible cell culture based investigation. Being the gold standard, carbon dioxide incubators fulfill this demand in various geometries and sizes to suit diverse cultivation applications. A door opening results in a climate breakdown followed by a restoration period during which optimal conditions cannot be guaranteed. The following paper investigates the influence of incubator door design to optimize climate retainment during the above mentioned event.

DESY is one of the largest accelerator laboratories in Europe. It develops and operates state of the art accelerators for fundamental science in the areas of high energy physics, photon science and accelerator development. While for decades high energy physics (HEP) has been the most prominent user of the DESY compute, storage and network infrastructure, various scientific areas as science with photons and accelerator development have caught up and are now dominating the demands on the DESY infrastructure resources, with significant consequences for the IT resource provisioning. In this contribution, we will present an overview of the computational, storage and network resources covering the various physics communities on site. Ranging from high-throughput computing (HTC) batch-like offline processing in the Grid and the interactive user analyses resources in the National Analysis Factory (NAF) for the HEP community, to the computing needs of accelerator development or of photon sciences such as PETRA III or the European XFEL. Since DESY is involved in these experiments and their data taking, their requirements include fast low-latency online processing for data taking and calibration as well as offline processing, thus high-performance computing (HPC) workloads, that are run on the dedicated Maxwell HPC cluster. As all communities face significant challenges due to changing environments and increasing data rates in the following years, we will discuss how this will reflect in necessary changes to the computing and storage infrastructures. We will present DESY compute cloud and container orchestration plans as a basis for infrastructure and platform services. We will show examples of Jupyter notebooks for small scale interactive analysis, as well as its integration into large scale resources such as batch systems or Spark clusters. To overcome the fragmentation of the various resources for all scientific communities at DESY, we explore how to integrate them into a seamless user experience in an Interdisciplinary Data Analysis Facility.

OBJECTIVE--To assess the interobserver variability between two observers from different echocardiographic laboratories. DESIGN--Two observers reviewed video recordings blinded to the other's diagnosis. In part I (n = 88), they determined interobserver variability for spontaneous echo contrast, left atrial thrombi, and appendage thrombi. No diagnostic criteria for thrombi were defined. In part II (n = 85), diagnostic criteria for thrombi were defined. RESULTS--Part I: Both observers agreed in diagnosing spontaneous echo contrast in 97%, left atrial thrombi in 90%, left atrial appendage thrombi in 94%. Part II: With predefined criteria no disagreement occurred in diagnosing left atrial thrombi. In the diagnosis of left atrial appendage thrombi both observers agreed in 89%. The mean diameters of the 10 thrombi on which the observers agreed were greater than of the nine appendage thrombi on which they disagreed. CONCLUSIONS--Interobserver variability in the diagnosis of spontaneous echo contrast is low. Defined criteria decrease interobserver variability for left atrial and appendage thrombi, although one third of the thrombi diagnosed by one observer were not confirmed by the other. Interobserver variability is high in the assessment of small structures (< 15 mm) within the left atrial appendage. [ABSTRACT FROM PUBLISHER]

The massive repository of images of the Sun captured by the Solar Dynamics Observatory (SDO) mission has ushered in the era of Big Data for Solar Physics. In this work, we investigate the entire public collection of events reported to the Heliophysics Event Knowledgebase (HEK) from automated solar feature recognition modules operated by the SDO Feature Finding Team (FFT). With the SDO mission recently surpassing five years of operations, and over 280,000 event reports for seven types of solar phenomena, we present the broadest and most comprehensive large-scale dataset of the SDO FFT modules to date. We also present numerous statistics on these modules, providing valuable contextual information for better understanding and validating of the individual event reports and the entire dataset as a whole. After extensive data cleaning through exploratory data analysis, we highlight several opportunities for knowledge discovery from data (KDD). Through these important prerequisite analyses presented here, the results of KDD from Solar Big Data will be overall more reliable and better understood. As the SDO mission remains operational over the coming years, these datasets will continue to grow in size and value. Future versions of this dataset will be analyzed in the general framework established in this work and maintained publicly online for easy access by the community.