As far as we know, this represents the largest published series of cDCD LT with NRP preservation. Our results demonstrate that cDCD liver grafts preserved with NRP appear far superior to those obtained by the conventional rapid recovery technique.

Traditional potato growth models evidence certain limitations, such as the cost of obtaining the input data required to run the models, the lack of spatial information in some instances, or the actual quality of input data. In order to address these issues, we develop a model to predict potato yield using satellite remote sensing. In an effort to offer a good predictive model that improves the state of the art on potato precision agriculture, we use images from the twin Sentinel 2 satellites (European Space Agency-Copernicus Programme) over three growing seasons, applying different machine learning models. First, we fitted nine machine learning algorithms with various pre-processing scenarios using variables from July, August and September based on the red, red-edge and infra-red bands of the spectrum. Second, we selected the best performing models and evaluated them against independent test data. Finally, we repeated the previous two steps using only variables corresponding to July and August. Our results showed that the feature selection step proved vital during data pre-processing in order to reduce multicollinearity among predictors. The Regression Quantile Lasso model (11.67% Root Mean Square Error, RMSE; R 2 = 0.88 and 9.18% Mean Absolute Error, MAE) and Leap Backwards model (10.94% RMSE, R 2 = 0.89 and 8.95% MAE) performed better when predictors with a correlation coefficient > 0.5 were removed from the dataset. In contrast, the Support Vector Machine Radial (svmRadial) performed better with no feature selection method (11.7% RMSE, R 2 = 0.93 and 8.64% MAE). In addition, we used a random forest model to predict potato yields in Castilla y León (Spain) 1-2 months prior to harvest, and obtained satisfactory results (11.16% RMSE, R 2 = 0.89 and 8.71% MAE). These results demonstrate the suitability of our models to predict potato yields in the region studied.The use of new technologies, such as satellite data, Geographic Information Systems (GIS) or Global Positioning Systems (GPS), can improve crop yield production and its quality [1], helping to secure food supply for the future as well as reducing the negative impacts resulting from agricultural practices [11]. More specifically, satellite remote sensing data has many applications in agriculture: soil property detection [12], crop type classification [13], crop yield forecast [14], crop health monitoring [15], soil moisture retrieval [16] or weather data assessment [17]. Remote sensing offers vast amounts of information which can be considered big data [18], and can help to improve crop modelling and decision-making. Big data has been described by Wolfert et al. [19] as "massive volumes of data with a wide variety that can be captured, analysed and used for decision-making", with said authors expecting big data to have a major impact on the agricultural sector. In order to improve the use of this data, given its size and variety, machine learning has emerged as an appropriate tool to identify rules and patterns in datasets [20], in addition to autonomously...

Although good results have been reported with the use of normothermic regional perfusion (NRP) in controlled donation after circulatory death (cDCD) liver transplantation (LT), there is a lack of evidence to demonstrate similar results to donation after brain death (DBD). We present a single‐center retrospective case‐matched (1:2) study including 100 NRP cDCD LTs and 200 DBD LTs and a median follow‐up of 36 months. Matching was done according to donor age, recipient Model for End‐Stage Liver Disease score, and cold ischemia time. The following perioperative results were similar in both groups: alanine transaminase peaks of 909 U/L in the DBD group and 836 U/L in the cDCD group and early allograft disfunction percentages of 21% and 19.2%, respectively. The 1‐year and 3‐year overall graft survival for cDCD was 99% and 93%, respectively, versus 92% and 87%, respectively, for DBD (P = 0.04). Of note, no cases of primary nonfunction or ischemic‐type biliary lesion were observed among the cDCD grafts. Our results confirm that NRP cDCD LT meets the same outcomes as those obtained with DBD LT and provides evidence to support the idea that cDCD donors per se should no longer be considered as “marginal donors” when recovered with NRP.

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Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture," J. Appl.Abstract. Desert locusts have attacked crops since antiquity. To prevent or mitigate its effects on local communities, it is necessary to precisely locate its breeding areas. Previous works have relied on precipitation and vegetation index datasets obtained by satellite remote sensing. However, these products present some limitations in arid or semiarid environments. We have explored a parameter: soil moisture (SM); and examined its influence on the desert locust wingless juveniles. We have used two machine learning algorithms (generalized linear model and random forest) to evaluate the link between hopper presences and SM conditions under different time scenarios. RF obtained the best model performance with very good validation results according to the true skill statistic and receiver operating characteristic curve statistics. It was found that an area becomes suitable for breeding when the minimum SM values are over 0.07 m 3 ∕m 3 during 6 days or more. These results demonstrate the possibility to identify breeding areas in Mauritania by means of SM, and the suitability of ESA CCI SM product to complement or substitute current monitoring techniques based on precipitation datasets. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Aim: to evaluate whether the current surveillance programs (ultrasonography and alpha-fetoprotein testing every six months) are successful in detecting patients in the early stages. Material and methods: the health records of all patients diagnosed with hepatocellular carcinoma in Donostia Hospital between 2003 and 2005 were reviewed retrospectively. Eightyfive patients (11 women and 74 men) were included in the study and demographic data, risk factors and clinical data were obtained. Patients were split into two groups according to whether or not they had been included in a surveillance program. Results: seventy per cent of patients of the surveillance group is diagnosed in early stage opposite to 26.7% of patients in no surveillance group (p < 0.05). Thirteen patients cannot receive curative treatment in spite of the diagnosis in early stage (9 in the surveillance group and 4 in the no surveillance group. The global sensibility of the surveillance program in our series is 95%. Conclusions: current hepatocellular carcinoma surveillance programs, which comprise six-monthly ultrasonography and alpha-fetoprotein tests, are highly sensitive and effective. These programs result in the detection of hepatocellular carcinoma in its early-stages, when potentially curative treatment may be offered.

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