Mapping the diversity of land uses following deforestation across Africa.

African forest are increasingly in decline as a result of land-use conversion due to human activities. However, a consistent and detailed characterization and mapping of land-use change that results in forest loss is not available at the spatial-temporal resolution and thematic levels suitable for decision-making at the local and regional scales; so far they have only been provided on coarser scales and restricted to humid forests. Here we present the first high-resolution (5 m) and continental-scale mapping of land use following deforestation in Africa, which covers an estimated 13.85% of the global forest area, including humid and dry forests. We use reference data for 15 different land-use types from 30 countries and implement an active learning framework to train a deep learning model for predicting land-use following deforestation with an F1-score of [Formula: see text] for the whole of Africa. Our results show that the causes of forest loss vary by region. In general, small-scale cropland is the dominant driver of forest loss in Africa, with hotspots in Madagascar and DRC. In addition, commodity crops such as cacao, oil palm, and rubber are the dominant drivers of forest loss in the humid forests of western and central Africa, forming an "arc of commodity crops" in that region. At the same time, the hotspots for cashew are found to increasingly dominate in the dry forests of both western and south-eastern Africa, while larger hotspots for large-scale croplands were found in Nigeria and Zambia. The increased expansion of cacao, cashew, oil palm, rubber, and large-scale croplands observed in humid and dry forests of western and south-eastern Africa suggests they are vulnerable to future land-use changes by commodity crops, thus creating challenges for achieving the zero deforestation supply chains, support REDD+ initiatives, and towards sustainable development goals.

Social media and deep learning capture the aesthetic quality of the landscape.

Peoples' recreation and well-being are closely related to their aesthetic enjoyment of the landscape. Ecosystem service (ES) assessments record the aesthetic contributions of landscapes to peoples' well-being in support of sustainable policy goals. However, the survey methods available to measure these contributions restrict modelling at large scales. As a result, most studies rely on environmental indicator models but these do not incorporate peoples' actual use of the landscape. Now, social media has emerged as a rich new source of information to understand human-nature interactions while advances in deep learning have enabled large-scale analysis of the imagery uploaded to these platforms. In this study, we test the accuracy of Flickr and deep learning-based models of landscape quality using a crowdsourced survey in Great Britain. We find that this novel modelling approach generates a strong and comparable level of accuracy versus an indicator model and, in combination, captures additional aesthetic information. At the same time, social media provides a direct measure of individuals' aesthetic enjoyment, a point of view inaccessible to indicator models, as well as a greater independence of the scale of measurement and insights into how peoples' appreciation of the landscape changes over time. Our results show how social media and deep learning can support significant advances in modelling the aesthetic contributions of ecosystems for ES assessments.

Associated risk factors and outcomes of acute kidney injury in severe trauma: Results from the Spanish trauma ICU registry (RETRAUCI).

INTRODUCTION: Acute kidney injury (AKI) constitutes a common complication after severe trauma. Our objective was to analyse the associated risk factors and outcomes of AKI in a large, multicentre sample of trauma ICU patients. MATERIALS AND METHODS: Observational, prospective and multicentre nationwide registry (RETRAUCI). We included all patients admitted to the participating ICUs from November 2013 to May 2017. We analysed the impact of AKI evaluated by the Risk, Injury, Failure, Loss of kidney function and End-stage kidney disease (RIFLE) definition. Comparison of groups was performed using Wilcoxon test, Chi-Square Test or Fisher's exact test as appropriate. A multiple logistic regression analysis was performed to analyse associated factors to the development of AKI. Logistic regression was used to calculate AKI-related mortality. A P value<0.05 was considered significant. RESULTS: During the study period, 5882 trauma patients were admitted. Complete data were available for 5740 patients. Among them, 871 had AKI (15.17%), distributed by RIFLE R 458 (7.98%), RIFLE I 234 (4.08%) and RIFLE F 179 (3.12%). Associated risk factors were: age (OR 3.05), haemodynamic instability (OR 2.90 to OR 8.34 depending on the severity of hypotension), coagulopathy (OR 1.82), rhabdomyolysis (OR 4.67) and AIS abdomen (OR 1.54). AKI was associated with mortality (crude OR 1.93 (1.59-2.36)), even after adjusting by potential confounders (adjusted OR 1.40 (1.13-1.73)). CONCLUSION: In our large sample of trauma ICU patients we found an incidence of AKI of 15%, which was associated with an increased mortality.

Compressed imaging by sparse random convolution.

The theory of compressed sensing (CS) shows that signals can be acquired at sub-Nyquist rates if they are sufficiently sparse or compressible. Since many images bear this property, several acquisition models have been proposed for optical CS. An interesting approach is random convolution (RC). In contrast with single-pixel CS approaches, RC allows for the parallel capture of visual information on a sensor array as in conventional imaging approaches. Unfortunately, the RC strategy is difficult to implement as is in practical settings due to important contrast-to-noise-ratio (CNR) limitations. In this paper, we introduce a modified RC model circumventing such difficulties by considering measurement matrices involving sparse non-negative entries. We then implement this model based on a slightly modified microscopy setup using incoherent light. Our experiments demonstrate the suitability of this approach for dealing with distinct CS scenarii, including 1-bit CS.

Open source tools for management and archiving of digital microscopy data to allow integration with patient pathology and treatment information.

BACKGROUND: Virtual microscopy includes digitisation of histology slides and the use of computer technologies for complex investigation of diseases such as cancer. However, automated image analysis, or website publishing of such digital images, is hampered by their large file sizes. RESULTS: We have developed two Java based open source tools: Snapshot Creator and NDPI-Splitter. Snapshot Creator converts a portion of a large digital slide into a desired quality JPEG image. The image is linked to the patient's clinical and treatment information in a customised open source cancer data management software (Caisis) in use at the Australian Breast Cancer Tissue Bank (ABCTB) and then published on the ABCTB website (http://www.abctb.org.au) using Deep Zoom open source technology. Using the ABCTB online search engine, digital images can be searched by defining various criteria such as cancer type, or biomarkers expressed. NDPI-Splitter splits a large image file into smaller sections of TIFF images so that they can be easily analysed by image analysis software such as Metamorph or Matlab. NDPI-Splitter also has the capacity to filter out empty images. CONCLUSIONS: Snapshot Creator and NDPI-Splitter are novel open source Java tools. They convert digital slides into files of smaller size for further processing. In conjunction with other open source tools such as Deep Zoom and Caisis, this suite of tools is used for the management and archiving of digital microscopy images, enabling digitised images to be explored and zoomed online. Our online image repository also has the capacity to be used as a teaching resource. These tools also enable large files to be sectioned for image analysis. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/5330903258483934.