Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline [1]. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net [2], FCN [3], and Mask- RCNN [4] were popularly used, typically based on ResNet [5] or VGG [6] base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

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Incorporation of carbon nanotubes (CNTs) into textiles without sacrificing their intrinsic properties provides a promising platform in exploring wearable technology. However, manufacture of flexible, durable, and stretchable CNT/textile composites on an industrial scale is still a great challenge. We hereby report a facile way of incorporating CNTs into the traditional yarn manufacturing process by dipping and drying CNTs into cotton rovings followed by fabricating CNT/cotton/spandex composite yarn (CCSCY) in sirofil spinning. The existence of CNTs in CCSCY brings electrical conductivity to CCSCY while the mechanical properties and stretchability are preserved. We demonstrate that the CCSCY can be used as wearable strain sensors, exhibiting ultrahigh strain sensing range, excellent stability, and good washing durability. Furthermore, CCSCY can be used to accurately monitor the real-time human motions, such as leg bending, walking, finger bending, wrist activity, clenching fist, bending down, and pronouncing words. We also demonstrate that the CCSCY can be assembled into knitted fabrics as the conductors with electric heating performance. The reported manufacturing technology of CCSCY could lead to an industrial-scale development of e-textiles for wearable applications.

Conductive cotton fabric was prepared by coating single-wall carbon nanotubes (CNTs) on a knitted cotton fabric surface through a "dip-and-dry" method. The combination of CNTs and cotton fabric was analyzed using scanning electron microscopy (SEM) and Raman scattering spectroscopy. The CNTs coating improved the mechanical properties of the fabric and imparted conductivity to the fabric. The electromechanical performance of the CNT-cotton fabric (CCF) was evaluated. Strain sensors made from the CCF exhibited a large workable strain range (0~100%), fast response and great stability. Furthermore, CCF-based strain sensors was used to monitor the real-time human motions, such as standing, walking, running, squatting and bending of finger and elbow. The CCF also exhibited strong electric heating effect. The flexible strain sensors and electric heaters made from CCF have potential applications in wearable electronic devices and cold weather conditions.