Segmentation of remotely sensed images with a neuro-fuzzy inference system

The semantic segmentation of remotely sensed images is a difficult task because the images do not represent well-defined objects. To tackle this task, fuzzy logic represents a valid alternative to convolutional neural networks—especially in the presence of very limited data—, as it allows to classify these objects with a degree of uncertainty. Unfortunately, the fuzzy rules for doing this have to be defined by hand. To overcome this limitation, in this work we propose to use an adaptive neuro-fuzzy inference system (ANFIS), which automatically infers the fuzzy rules that classify the pixels of the remotely sensed images, thus realizing their semantic segmentation. The resulting fuzzy model guarantees a good level of accuracy in the classification of pixels despite the few input features and the limited number of images used for training. Moreover, unlike the classic deep learning approaches, it is also explanatory, since the classification rules produced are similar to the way of thinking of human beings

Crowd Detection in Aerial Images Using Spatial Graphs and Fully-Convolutional Neural Networks

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Different Treatments of Symptomatic Angiomyolipomas of the Kidney: Two Case Reports

Development of more sensitive imaging techniques has caused an increase in the number of diagnosed small renal tumors. Approximately 2–3% of these lesions are proved to be angiomyolipomas (AML), a rare benign tumor of the kidney sometimes causing pain and hematuria. The most required approach is observation, but in the case of recurrent symptoms or larger tumors, which may cause bleeding, a more active treatment is required. We present two cases of symptomatic AML tumors of different sizes in the kidney: one treated with transarterial embolization (TAE), and the other with percutaneous cryoablation (CRA). The lesions were diagnosed on the basis of contrast-enhanced computed tomography (CT) scan and magnetic resonance imaging (MRI). Both treatments proved to be effective and safe for treating renal AMLs. A follow-up carried out, based on contrast-enhanced CT scan, confirmed complete treatment of AML and decreased lesion size. There are myriad minimally invasive approaches for the treatment of renal AMLs, and the preservation of renal function remains a priority. The most popular treatment option is the selective renal artery embolization. Owing to its limited invasiveness, CRA could be an attractive option for the preventive treatment of AML

Logrando excelencia competitiva en el trabajo de una compañía de seguridad.

In this paper, we propose a novel crowd detection method for drone safe landing, based on an extremely light and fast fully convolutional neural network. Such a computer vision application takes advantage of the technical tools some commercial drones are equipped with. The proposed architecture is based on a two-loss model in which the main classification task, aimed at distinguishing between crowded and non-crowded scenes, is simultaneously assisted by a regression task, aimed at people counting. In addition, the proposed method provides class activation heatmaps, useful to semantically augment the flight maps. To evaluate the effectiveness of the proposed approach, we used the challenging VisDrone dataset, characterized by a very large variety of locations, environments, lighting conditions, and so on. The model developed by the proposed two-loss deep architecture achieves good values of prediction accuracy and average precision, outperforming models developed by a similar one-loss architecture and a more classic scheme based on MobileNet. Moreover, by lowering the confidence threshold, the network achieves very high recall, without sacrificing too much precision. The method also compares favorably with the state-of-the-art, providing an effective and efficient tool for several safe drone applications

Preliminary Evaluation of TinyYOLO on a New Dataset for Search-and-Rescue with Drones

Unmanned aerial vehicles (UAVs), more commonly known as drones, are increasingly used as a technological support tool for search-and-rescue (SAR) operations and also for post-disaster area explorations. UAVs can be equipped with high-resolution cameras and embed GPUs powerful enough to provide effective and efficient aid to emergency rescue operations. Victims can be unconscious or injured, and any means of locating them as quickly as possible is critical to increasing their chances of survival. In particular, using drones that can automatically detect people in scenes can increase detection rate, while reducing rescue time. In this work, we present a new dataset specifically designed for SAR operations from drones using computer vision. Being small in size, the dataset is currently intended for testing and evaluation purposes only. We also provide baseline results on this dataset, obtained with the state-of-the-art TinyYOLOv3 object detector. The purpose of this preliminary work is to stimulate interest and encourage contributions to this research topic

Crowd Counting from Unmanned Aerial Vehicles with Fully-Convolutional Neural Networks

Crowd analysis is receiving an increasing attention in the last years because of its social and public safety implications. One of the building blocks of crowd analysis is crowd counting and the associated crowd density estimation. Several commercially available drones are equipped with onboard cameras and embed powerful GPUs, making them an excellent platform for real-time crowd counting tools. This paper proposes a light-weight and fast fully-convolutional neural network to learn a regression model for crowd counting in images acquired from drones. A robust model is derived by training the network from scratch on a subset of the very challenging VisDrone dataset, which is characterized by a high variety of locations, environments, perspectives and lighting conditions. The derived model achieves an MAE of 8.86 and an RMSE of 15.07 on the test images, outperforming models developed by state-of-the-art light-weight architectures, that are MobileNetV2 and YOLOv3

Human MiR-544a Modulates SELK Expression in Hepatocarcinoma Cell Lines

<div><p>Hepatocellular carcinoma (HCC) is a multi-factorial cancer with a very poor prognosis; therefore, there are several investigations aimed at the comprehension of the molecular mechanisms leading to development and progression of HCC and at the definition of new therapeutic strategies. We have recently evaluated the expression of selenoproteins in HCC cell lines in comparison with normal hepatocytes. Recent results have shown that some of them are down- and others up-regulated, including the selenoprotein K (SELK), whose expression was also induced by sodium selenite treatment on cells. However, so far very few studies have been dedicated to a possible effect of microRNAs on the expression of selenoproteins and their implication in HCC. In this study, the analysis of SELK 3’UTR by bioinformatics tools led to the identification of eight sites potentially targeted by human microRNAs. They were then subjected to a validation test based on luciferase reporter constructs transfected in HCC cell lines. In this functional screening, miR-544a was able to interact with SELK 3’UTR suppressing the reporter activity. Transfection of a miR-544a mimic or inhibitor was then shown to decrease or increase, respectively, the translation of the endogenous SELK mRNA. Intriguingly, miR-544a expression was found to be modulated by selenium treatment, suggesting a possible role in SELK induction by selenium.</p></div

miR-544a expression in three common HCC cell lines.

<p>miR-544a expression levels were evaluated by RT-qPCR and reported as fold change relative to that of HuH-7.</p

Human MiR-544a Modulates SELK Expression in Hepatocarcinoma Cell Lines - Fig 5

<p><b>Effect of sodium selenite on miR-544a expression and SELK</b> (A) Cells were cultured for 24 h and 48 h in standard condition or in presence of increasing sodium selenite doses; miR-544a expression was determined by RT-qPCR and reported as fold change of expression in treated samples vs untreated ones. (B) Upper panel, representative Western blot analysis of protein extracts from cells cultured in absence (-) or presence (+) of 1 μM sodium selenite for 24h and 48 h; lower panel, quantification of protein bands; values represent the mean ± s.d. of three independent experiment.</p

The silencing effect of miR-544a on SELK in HuH-7 cells.

<p>Representative Western blot analysis of protein extracts at 48 h after transfection with miR-544a mimic (A) or miR-544a inhibitor (B) and their relative control molecules at 50 nM. (C) Quantification of SELK protein band normalized to that of tubulin; signal intensity values determined for the control experiments (Ctrl-miR and Ctrl-anti-miR) were set at 1; values represent the mean ± s.d. of three independent experiment. P-values are indicated as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156908#pone.0156908.g003" target="_blank">Fig 3</a>.</p