Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realizing the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high‐quality ground‐truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope (NE) of HeLa cells imaged with serial blockface scanning electron microscopy. We present our approach for aggregating multiple volunteer annotations to generate a high‐quality consensus segmentation and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the NE, which we share here, in addition to our archived benchmark data.

Our results suggest that integrated care monitoring technologies have a potential for providing improved care and can have positive impact on well-being of the elderly by enabling timely intervention. Long-term BP and pulse oximetry data could indicate exacerbation and lead to effective intervention; physical activity data provided important information on the well-being of patients. However, there remains a need for better understanding of long-term variations in vital signs and activity data to establish intervention protocols for improved disease management.

This paper describes the implementation of an end-to-end remote monitoring platform based on the IEEE 11073 standards for personal health devices (PHD). It provides an overview of the concepts and approaches and describes how the standard has been optimized for small devices with limited resources of processor, memory, and power that use short-range wireless technology. It explains aspects of IEEE 11073, including the domain information model, state model, and nomenclature, and how these support its plug-and-play architecture. It shows how these aspects underpin a much larger ecosystem of interoperable devices and systems that include IHE PCD-01, HL7, and BlueTooth LE medical devices, and the relationship to the Continua Guidelines, advocating the adoption of data standards and nomenclature to support semantic interoperability between health and ambient assisted living in future platforms. The paper further describes the adaptions that have been made in order to implement the standard on the ZigBee Health Care Profile and the experiences of implementing an end-to-end platform that has been deployed to frail elderly patients with chronic disease(s) and patients with diabetes.

As countercurrent chromatography (CCC) is becoming an established method in chromatography for scaling from analytical CCC in the laboratory to full process scale in the industrial manufacture of products, it is becoming increasingly important to model the process and to be able to predict coil=column scale-up parameters for a given process. This paper offers a method of modelling CCC on the basis of an eluting countercurrent distribution (CCD) model. The model confirms that peak width in CCC varies in proportion to the square root of the length of the column, establishes a formula predicting peak width in terms of retention factor and retention time, and provides a method for determining the efficiency of a given CCC instrument. This allows, for the first time, the mixing efficiency of different CCC approaches and=or devices to be compared and perhaps, more importantly, predictions to be made that are outside the current operating parameters of existing CCC instrumentation. This will greatly assist in the design of new equipment, particularly in scale-up, and will also help users optimize the results from their CCC instruments.