Deceptive learning in histopathology.

Aims: Deep learning holds immense potential for histopathology, automating tasks that are simple for expert pathologists and revealing novel biology for tasks that were previously considered difficult or impossible to solve by eye alone. However, the extent to which the visual strategies learned by deep learning models in histopathological analysis are trustworthy or not has yet to be systematically analysed. Here, we systematically evaluate deep neural networks (DNNs) trained for histopathological analysis in order to understand if their learned strategies are trustworthy or deceptive.

Design and fabrication of superamphiphobic paper surfaces.

Cellulose-based paper remains a vital component of modern day society; however, its use is severely limited in certain applications because of hydrophilic and oleophilic properties. In this manuscript we present a novel method to create superamphiphobic paper by combining the control of fiber size and structure with plasma etching and fluoropolymer deposition. The heterogeneous nature of the paper structure is drastically different from that of artificially created superamphiphobic surfaces.

Creation of superhydrophobic stainless steel surfaces by acid treatments and hydrophobic film deposition.

In this work, we present a method to render stainless steel surfaces superhydrophobic while maintaining their corrosion resistance. Creation of surface roughness on 304 and 316 grade stainless steels was performed using a hydrofluoric acid bath. New insight into the etch process is developed through a detailed analysis of the chemical and physical changes that occur on the stainless steel surfaces.