The proposed research develops novel, interdisciplinary methods that facilitate the extraction of information from historical maps to study the evolution of land use patterns and urban growth. Specifically, we will (i) employ insights from imagery techniques and photogrammetry to extract colour-coded information on land-use and (ii) use recognition algorithms to detect points of interest, street names and labels on maps that can be matched to data sources like population censuses. Together, this will help draw a uniquely detailed picture of changes in the socio-economic structure and organisation of cities. A few recent studies show that high-resolution maps from archives provide numerous pieces of information. The microfeatures embedded in historical maps can be systematically exploited with the help of (a) recent advancements in machine learning, (b) novel methods to delineate and classify urban areas (de Bellefon et al., 2019), and (c) the recent development of analytical tools in economic geography (see Redding and Rossi-Hansberg, 2017, for a summary). To our knowledge, this proposal marks the first attempt (i) to digitise historical maps with such time and spatial coverage, (ii) to demonstrate the scope of recognition methods on historical maps, and (iii) to draw an almost complete picture of the urban structure and socio-economic composition of cities, regions and countries in the context of three distinct historical environments: France, England and Wales, and North America (e.g., Toronto, Montreal) over the past centuries. Our research will advance current knowledge in social sciences through the development of three innovative methodologies: I. A machine learning approach to extract land use patterns from historical colour maps (1750-1950), adapted from state-of-the-art imagery techniques; II. A recognition algorithm to detect, tag and geo-locate points of interest in high-quality scans of urban centres (22,000 Ordnance Survey maps covering the 70 largest industrial areas in England and Wales, 1870-1960, and fire maps, Toronto/Montreal); III. A location algorithm which geo-locates entries from Micro-censuses (England and Wales, 1851-1911) and trade registers, and combines this source of information with maps. These newly developed methods will overcome practical limitations in the use of hand-drawn historical maps and help exploit this mostly unexploited source of historical information. This will advance our knowledge on long-run urban development and generate the following output: IV. High-resolution vectorised maps of France (1750-1950), cities of England & Wales (1870-1960), Toronto and Montreal (1876-1975); V. A dynamic model brought to the data of the physical development of cities accounting for persistent building stock and changing drivers of location over time from agriculture to manufacturing to services--informed by novel stylised facts covering 250 years of city growth in France; VI. An analysis of the long-run impact of atmospheric pollution on urban centres and their outskirts in England and Wales (1870-1960); VII. An empirical and theoretical investigation of the horizontal and vertical growth of Toronto and Montreal; the identification of drivers of the physical development of two cities that have experienced English or French influences.

The correct regulation of gene expression is fundamental to multicellular life. The fastest and most direct way to alter protein levels in individual cells or over time is by regulating mRNA translation. While genomics and quantitative imaging has transformed our understanding of transcription, recent technological advances made by ourselves and others place us on the cusp of a similar revolution in decoding translation regulation. Therefore, our overarching aim is to identify how translation of the transcriptome is spatiotemporally regulated during embryonic development and dissect the regulatory mechanisms. We will leverage the unique advantages of the Drosophila embryo to address this problem. Firstly, we will develop a spatial translatomics method that will allow us to directly visualize and quantitate translation in every cell at single-mRNA resolution during embryogenesis. Secondly, we will dissect the mechanisms of translational regulation by elucidating the function of specific RNA binding proteins. Thirdly, we will test the exciting hypothesis that the physical location of distant genes in the nucleus and their transcription coupling also directs their co-translation to maximize the efficiency of gene expression. Results from this study will not only provide new mechanistic insights into translational regulation and efficiency during development, but the spatial translatomics method developed will be easily transferable to study translation in other tissues and organisms.

Mitosis is a fundamental process required for the generation of multicellular organisms, for tissue renewal and homeostasis. During development, both the orientation of the division plane and the timing of mitotic entry, have fundamental influence on the positioning of daughter cells and their organization into tissues. However, the mechanisms that control the timing of mitotic entry remain poorly understood. Entry into mitosis is triggered by the activation of a mitotic kinase cascade and the simultaneous inactivation of counteracting phosphatases. Since the mitotic kinases themselves are activated by phosphorylation, a central question arises: how are mitotic kinases activated while phosphatases activity predominates? During development, how does the regulation and cross-talk between mitotic kinases and opposing phosphatases ensure timely mitotic entry? The objective of this proposal is to decipher how the parallel regulation of kinases and phosphatases control asynchronous mitotic entry during development.