AI-Based Flood Event Understanding and Quantifying Using Online Media and Satellite Data

In this paper we study the problem of flood detection and quantification using online media and satellite data. We present a three approaches, two of them based on neural networks and a third one based on the combination of different bands of satellite images. This work aims to detect floods and also give relevant information about the flood situation such as the water level and the extension of the flooded regions, as specified in the three subtasks, for which of them we propose a specific solution

Deep learning models for road passability detection during flood events using social media data

During natural disasters, situational awareness is needed to understand the situation and respond accordingly. A key need is assessing open roads for transporting emergency support to victims. This can be done via analysis of photos from affected areas with known location. This paper studies the problem of detecting blocked / open roads from photos during floods by applying a two-step approach based on classifiers: does the image have evidence of road? If it does, is the road passable or not? We propose a single double-ended neural network (NN) architecture which addresses both tasks at the same time. Both problems are treated as a single class classification problem by the usage of a compactness loss. The study is performed on a set of tweets, posted during flooding events, that contain (i)~metadata and (ii)~visual information. We study the usefulness of each source of data and the combination of both. Finally, we do a study of the performance gain from ensembling different networks. Through the experimental results we prove that the proposed double-ended NN makes the model almost two times faster and memory lighter while improving the results with respect to training two separate networks to solve each problem independently

Mechanisms of action and antiproliferative properties of Brassica oleracea juice in human breast cancer cell lines

none7noCruciferous vegetables are an important source of compounds that may be useful for chemoprevention. In this study, we evaluated the antiproliferative activity of juice obtained from leaves of several varieties of Brassica oleracea on both estrogen receptor (ER)-positive (ER; MCF-7 and BT474) and ER-negative (ER; MDA-MB-231 and BT20) human breast cancer cell lines. The effect of juice on cell proliferation was evaluated on DNA synthesis and on cell cycle–related proteins. Juice markedly reduced DNA synthesis, evaluated by [3H]thymidine incorporation, starting from low concentrations (final concentration 5–15 mL/L), and this activity was independent of ER. All cauliflower varieties tested suppressed cell proliferation in a dose-dependent manner. Cell growth inhibition was accompanied by significant cell death at the higher juice concentrations, although no evidence of apoptosis was found. Interestingly, the juice displayed a preferential activity against breast cancer cells compared with other mammalian cell lines investigated (ECV304, VERO, Hep2, 3T3, and MCF-10A) (P 0.01). At the molecular level, the inhibition of proliferation was associated with significantly reduced CDK6 expression and an increased level of p27 in ER cells but not in ER cells, whereas a common feature in all cell lines was significantly decreased retinoblastoma protein phosphorylation. These results suggest that the edible part of Brassica oleracea contains substances that can markedly inhibit the growth of both ER and ER human breast cancer cells, although through different mechanisms. These results suggest that the widely available cruciferous vegetables are potential chemopreventive agents. JopenBrandi, Giorgio; Schiavano, GIUDITTA FIORELLA; Zaffaroni, N; De Marco, C; Paiardini, M; Cervasi, B; Magnani, MauroBrandi, G; Schiavano, Gf; Zaffaroni, N; De Marco, C; Paiardini, M; Cervasi, B; Magnani, M

Anastomosis configuration and technique following ileocaecal resection for Crohn's disease: a multicentre study

A limited ileocaecal resection is the most frequently performed procedure for ileocaecal CD and different anastomotic configurations and techniques have been described. This manuscript audited the different anastomotic techniques used in a national study and evaluated their influence on postoperative outcomes following ileocaecal resection for primary CD. This is a retrospective, multicentre, observational study promoted by the Italian Society of Colorectal Surgery (SICCR), including all adults undergoing elective ileocaecal resection for primary CD from June 2018 May 2019. Postoperative morbidity within 30 days of surgery was the primary endpoint. Postoperative length of hospital stay (LOS) and anastomotic leak rate were the secondary outcomes. 427 patients were included. The side to side anastomosis was the chosen configuration in 380 patients (89%). The stapled anastomotic (n = 286; 67%), techniques were preferred to hand-sewn (n = 141; 33%). Postoperative morbidity was 20.3% and anastomotic leak 3.7%. Anastomotic leak was independent of the type of anastomosis performed, while was associated with an ASA grade ≥ 3, presence of perianal disease and ileocolonic localization of disease. Four predictors of LOS were identified after multivariate analysis. The laparoscopic approach was the only associated with a reduced LOS (p = 0.017), while age, ASA grade ≥ 3 or administration of preoperative TPN were associated with increased LOS. The side to side was the most commonly used anastomotic configuration for ileocolic reconstruction following primary CD resection. There was no difference in postoperative morbidity according to anastomotic technique and configuration. Anastomotic leak was associated with ASA grade ≥ 3, a penetrating phenotype of disease and ileo-colonic distribution of CD

National variations in perioperative assessment and surgical management of Crohn's disease: a multicentre study

Aim: Crohn's disease (CD) requires a multidisciplinary approach and surgery should be undertaken by dedicated colorectal surgeons with audited outcomes. We present a national, multicentre study, with the aim to collect benchmark data on key performance indicators in CD surgery, to highlight areas where standards of CD surgery excel and to facilitate targeted quality improvement where indicated. Methods: All patients undergoing ileocaecal or redo ileocolic resection in the participating centres for primary and recurrent CD from June 2018 to May 2019 were included. The main objective was to collect national data on hospital volume and practice variations. Postoperative morbidity was the primary outcome. Laparoscopic surgery and stoma rate were the secondary outcomes. Results: In all, 715 patients were included: 457 primary CD and 258 recurrent CD with a postoperative morbidity of 21.6% and 34.7%, respectively. Laparoscopy was used in 83.8% of primary CD compared to 31% of recurrent CD. Twenty-five hospitals participated and the total number of patients per hospital ranged from 2 to 169. Hospitals performing more than 10 primary CD procedures per year showed a higher adoption of laparoscopy and bowel sparing surgery. Conclusions: There is significant heterogeneity in the number of CD surgeries performed per year nationally in Italy. Our data suggest that high-volume hospitals perform more complex procedures, with a higher adoption of bowel sparing surgery. The rate of laparoscopy in high-volume hospitals is higher for primary CD but not for recurrent CD compared with low-volume hospitals

Estimation of Speed and Distance of Surrounding Vehicles from a Single Camera

Deep Learning requires huge amount of data with related labels, that are necessary for proper training. Thanks to modern videogames, which aim at photorealism, it is possible to easily obtain synthetic dataset by extracting information directly from the game engine. The intent is to use data extracted from a videogame to obtain a representation of various scenarios and train a deep neural network to infer the information required for a specific task. In this work we focus on computer vision aids for automotive applications and we target to estimate the distance and speed of the surrounding vehicles by using a single dashboard camera. We propose two network models for distance and speed estimation, respectively. We show that training them by using synthetic images generated by a game engine is a viable solution that turns out to be very effective in real settings. Document type: Part of book or chapter of boo