Selective Calixarene-Directed Synthesis of MXene Plates, Crumpled Sheets, Spheres, and Scrolls.

Fully exploiting the electronic and mechanical properties of 2D laminar materials not only requires efficient and effective means of their exfoliation into low dimensional layers, but also necessitates a means of changing their morphology so as to explore any enhancement that this may offer. MXenes are a rapidly emerging new class of such laminar materials with unique properties. However, access to other morphologies of MXenes has not yet been fully realised. To this end we have developed the synthesis of MXenes (Ti2 C) as plates, crumpled sheets, spheres and scrolls, which involves selective intercalation of p-phosphonic calix[n]arenes, with control in morphology arising from the choice of the size of the macrocycle, n=4, 5, 6, or 8. This opens up wider avenues of discovery/design for new morphologies from the wider family of MXenes beyond Ti2 C, along with opportunities to exploit any new physico-chemical properties proffered.

Meta-Analysis of Gene Popularity: Less Than Half of Gene Citations Stem from Gene Regulatory Networks.

The reasons for selecting a gene for further study might vary from historical momentum to funding availability, thus leading to unequal attention distribution among all genes. However, certain biological features tend to be overlooked in evaluating a gene's popularity. Here we present a meta-analysis of the reasons why different genes have been studied and to what extent, with a focus on the gene-specific biological features. From unbiased datasets we can define biological properties of genes that reasonably may affect their perceived importance. We make use of both linear and nonlinear computational approaches for estimating gene popularity to then compare their relative importance. We find that roughly 25% of the studies are the result of a historical positive feedback, which we may think of as social reinforcement. Of the remaining features, gene family membership is the most indicative followed by disease relevance and finally regulatory pathway association. Disease relevance has been an important driver until the 1990s, after which the focus shifted to exploring every single gene. We also present a resource that allows one to study the impact of reinforcement, which may guide our research toward genes that have not yet received proportional attention.

CATARACTS: Challenge on automatic tool annotation for cataRACT surgery.

Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future.