PHYLOGENY of TWO AFRICAN GENERA of SAPOTACEAE - ENGLEROPHYTUM and SYNSEPALUM

Englerophytum and Synsepalum are two closely related genera of trees and shrubs from the African tropics. Previous molecular studies have shown that these genera collectively form a clade within the subfamily Chrysophylloideae (Sapotaceae). However, little is known about the inter-relationships of the taxa within the Englerophytum-Synsepalum clade. In this study, nuclear ribosomal DNA and plastid trnH-psbA sequences were used to estimate the phylogeny within the clade. Results indicate that the clade consists of six major lineages, two composed solely of taxa from the genus Englerophytum and four composed of taxa from the genus Synsepalum. Each lineage can be distinguished by suites of vegetative and floral characters. Leaf venation patterns, calyx fusion, style length and staminodal structure were among the most useful characters for distinguishing clades. Some of the subclades within the Englerophytum-Synsepalum clade were also found to closely fit descriptions of former genera, most of which were described by Aubréville, that have since been placed in synonymy with Englerophytum and Synsepalum. The clade with the type species of Englerophytum also contains the type species of the genera Wildemaniodoxa and Zeyherella, which are confirmed as synonyms. © Trustees of the Royal Botanic Garden Edinburgh (2019)

Recreating Sheffield's Medieval Castle in situ using Outdoor Augmented Reality

Augmented Reality (AR) experiences generally function well indoors, inside buildings, where, typically, lighting conditions are stable, the scale of the environment is small and fixed, and markers can be easily placed. This is not the case for outdoor AR experiences. In this paper, we present practical solutions for an AR application that virtually restores Sheffield’s medieval castle to the Castlegate area in Sheffield city centre where it once stood. A simplified 3D model of the area, together with sensor fusion, is used to support a user alignment process and subsequent orientation tracking. Rendering realism is improved by using directional lighting matching that of the sun, a virtual ground plane and depth masking based on the same model used in the alignment stage. The depth masking ensures the castle sits correctly in front of or behind real buildings, as necessary, thus addressing the occlusion problem. The Unity game engine is used for development and the resulting app runs in real-time on recent high-spec Android mobile phones

A protein blueprint of the diatom CO2-fixing organelle

Diatoms are central to the global carbon cycle. At the heart of diatom carbon fixation is an overlooked organelle called the pyrenoid, where concentrated CO2 is delivered to densely packed Rubisco. Diatom pyrenoids fix approximately one-fifth of global CO2, but the protein composition of this organelle is largely unknown. Using fluorescence protein tagging and affinity purification-mass spectrometry, we generate a high-confidence spatially defined protein-protein interaction network for the diatom pyrenoid. Within our pyrenoid interaction network are 10 proteins with previously unknown functions. We show that six of these form a shell that encapsulates the Rubisco matrix and is critical for pyrenoid structural integrity, shape, and function. Although not conserved at a sequence or structural level, the diatom pyrenoid shares some architectural similarities to prokaryotic carboxysomes. Collectively, our results support the convergent evolution of pyrenoids across the two main plastid lineages and uncover a major structural and functional component of global CO2 fixation

Beyond spheres of influence: the myth of the state and Russia’s seductive power in Kyrgyzstan

This article questions the analytical value of “spheres of influence” for understanding power and the state in the post-Soviet region and beyond, based on a critical deconstruction of the ontological and epistemological assumptions inherent in the concept. It proposes an alternative reading of power and the state, drawing on the concept of “seductive power” at a distance and Timothy Mitchell’s “state effect.” Rather than the concept of a sphere of influence, a highly politicized concept that conveys an ontology that flattens and divides space, essentializes the state, and relies on an intentionalist account of power, we need an analytical framework that can help us make sense of the multiple, varied spatialities and historical legacies that produce the state and power. I demonstrate this through an extended discussion of Russian power in Kyrgyzstan, a country often described as a Russian client state. Mobilizing recent re-conceptualizations of state and power in anthropology and political geography, I present an analysis of Russia’s seductive power in Kyrgyzstan and the way it contributes to producing Kyrgyz state-ness. I also show how Russia’s Great Power myth is itself evolving and conclude that the differentiated, relational production of space and power in either Kyrgyz or Russian myths of the state is not captured by a the concept of a return to spheres of influence

Where Snow is a Landmark: Route Direction Elements in Alpine Contexts

Route directions research has mostly focused on urban space so far, highlighting human concepts of street networks based on a range of recurring elements such as route segments, decision points, landmarks and actions. We explored the way route directions reflect the features of space and activity in the context of mountaineering. Alpine route directions are only rarely segmented through decision points related to reorientation; instead, segmentation is based on changing topography. Segments are described with various degrees of detail, depending on difficulty. For landmark description, direction givers refer to properties such as type of surface, dimension, colour of landscape features; terrain properties (such as snow) can also serve as landmarks. Action descriptions reflect the geometrical conceptualization of landscape features and dimensionality of space. Further, they are very rich in the semantics of manner of motion

Brachydactyly

Brachydactyly ("short digits") is a general term that refers to disproportionately short fingers and toes, and forms part of the group of limb malformations characterized by bone dysostosis. The various types of isolated brachydactyly are rare, except for types A3 and D. Brachydactyly can occur either as an isolated malformation or as a part of a complex malformation syndrome. To date, many different forms of brachydactyly have been identified. Some forms also result in short stature. In isolated brachydactyly, subtle changes elsewhere may be present. Brachydactyly may also be accompanied by other hand malformations, such as syndactyly, polydactyly, reduction defects, or symphalangism

The eukaryotic CO2-concentrating organelle is liquid-like and exhibits dynamic reorganization

Approximately 30%–40% of global CO 2 fixation occurs inside a non-membrane-bound organelle called the pyrenoid, which is found within the chloroplasts of most eukaryotic algae. The pyrenoid matrix is densely packed with the CO 2-fixing enzyme Rubisco and is thought to be a crystalline or amorphous solid. Here, we show that the pyrenoid matrix of the unicellular alga Chlamydomonas reinhardtii is not crystalline but behaves as a liquid that dissolves and condenses during cell division. Furthermore, we show that new pyrenoids are formed both by fission and de novo assembly. Our modeling predicts the existence of a “magic number” effect associated with special, highly stable heterocomplexes that influences phase separation in liquid-like organelles. This view of the pyrenoid matrix as a phase-separated compartment provides a paradigm for understanding its structure, biogenesis, and regulation. More broadly, our findings expand our understanding of the principles that govern the architecture and inheritance of liquid-like organelles

Clinically applicable deep learning for diagnosis and referral in retinal disease

The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting