Earth Observation Applications in the Hindu Kush Himalaya Region—Evolution and Adoptions

The year 1957 marked the start of a new era in human history with the launch of Sputnik, thus began the journey of Earth observation (EO). Then, in the early 1960s, with rapid developments in space technology and the race to reach the moon, scientific discussions veered toward the potential applications of EO in the fields of geography, agriculture, water resources, geology, and oceanography (NASA: Sputnik and the dawn of the space age, 2017; Haklay et al. in Earth observation open science and innovation, ISSI scientific report series 15, 2018).

Cerebellar nuclei evolved by repeatedly duplicating a conserved cell type set

How have complex brains evolved from simple circuits? Here we investigated brain region evolution at cell type resolution in the cerebellar nuclei (CN), the output structures of the cerebellum. Using single-nucleus RNA sequencing in mice, chickens, and humans, as well as STARmap spatial transcriptomic analysis and whole-CNS projection tracing in mice, we identified a conserved cell type set containing two classes of region-specific excitatory neurons and three classes of region-invariant inhibitory neurons. This set constitutes an archetypal CN that was repeatedly duplicated to form new regions. Interestingly, the excitatory cell class that preferentially funnels information to lateral frontal cortices in mice becomes predominant in the massively expanded human Lateral CN. Our data provide the first characterization of CN transcriptomic cell types in three species and suggest a model of brain region evolution by duplication and divergence of entire cell type sets.

Bridging the gap between theory and practice in elucidating modular gene regulatory sequence organisation within genomes

Changes to promoter regions probably have been responsible for many morphological evolutionary transitions, especially in animals. This idea is becoming testable, as data from genome projects amass and enable bioinformaticians to conduct comparative sequence analyses and test for correlations between genotypic similarities or differences and phenotypic likeness or disparity. Although such practical pursuits have initiated some theoretical considerations, a conceptual framework for understanding promoter region evolution, potentially effecting morphological evolution, is only starting to emerge, predominantly resulting from computational research. We contribute to this framework by specifying three big problems for promoter region research; reviewing computational research on promoter region evolution; and exemplifying a topic for future promoter region research — module evolution.

Evgeny Khvalkov, The Colonies of Genoa in the Black Sea Region: Evolution and Transformation. Routledge Research in Medieval Studies, 11. New York and London: Routledge, 2018, xiv, 443 pp., 10 fig., 7 tables.

Our current and very urgent goal is to transform Medieval Studies into Global Medieval Studies, a thorny, challenging, maybe also daunting task, but one that we cannot turn away if we want to progress in our field. In fact, it does not matter whether we want to go that route or not; if we want to understand the Middle Ages both holistically and in specifics, we must simply accept that many people (merchants, soldiers, diplomats, artists, craftsmen, preachers, rulers, scholars, etc.) traveled not only throughout Europe, but also far beyond those limits, and encountered in that way countless other people who arrived from other directions. One most promising area of investigation that has already been long recognized is the group of merchants traveling to the Mongol court, prime among them Marco Polo. But he, his brothers, and numerous other individuals were, after all, only exceptions, and the Chinese, for instance, did not demonstrate any real interest in the West, apart from the Arabic world. However, if we turn to the Black Sea, an entirely different situation emerges that proves to be highly promising for future research.

Trust Region Evolution Strategies

Evolution Strategies (ES), a class of black-box optimization algorithms, has recently been demonstrated to be a viable alternative to popular MDP-based RL techniques such as Qlearning and Policy Gradients. ES achieves fairly good performance on challenging reinforcement learning problems and is easier to scale in a distributed setting. However, standard ES algorithms perform one gradient update per data sample, which is not very efficient. In this paper, with the purpose of more efficient using of sampled data, we propose a novel iterative procedure that optimizes a surrogate objective function, enabling to reuse data sample for multiple epochs of updates. We prove monotonic improvement guarantee for such procedure. By making several approximations to the theoretically-justified procedure, we further develop a practical algorithm called Trust Region Evolution Strategies (TRES). Our experiments demonstrate the effectiveness of TRES on a range of popular MuJoCo locomotion tasks in the OpenAI Gym, achieving better performance than ES algorithm.