Aeroporto Sanzio: quale futuro?

Contributo pubblicato sulla rivista Realt\ue0 industriale, n. 10 (ottobre), Confindustria March

An interactive tool for semi-automated leaf annotation

High throughput plant phenotyping is emerging as a necessary step towards meeting agricultural demands of the future. Central to its success is the development of robust computer vision algorithms that analyze images and extract phenotyping information to be associated with genotypes and environmental conditions for identifying traits suitable for further development. Obtaining leaf level quantitative data is important towards understanding better this interaction. While certain efforts have been made to obtain such information in an automated fashion, further innovations are necessary. In this paper we present an annotation tool that can be used to semi-automatically segment leaves in images of rosette plants. This tool, which is designed to exist in a stand-alone fashion but also in cloud based environments, can be used to annotate data directly for the study of plant and leaf growth or to provide annotated datasets for learning-based approaches to extracting phenotypes from images. It relies on an interactive graph-based segmentation algorithm to propagate expert provided priors (in the form of pixels) to the rest of the image, using the random walk formulation to find a good per-leaf segmentation. To evaluate the tool we use standardized datasets available from the LSC and LCC 2015 challenges, achieving an average leaf segmentation accuracy of almost 97% using scribbles as annotations. The tool and source code are publicly available at http://www.phenotiki.com and as a GitHub repository at https://github.com/phenotiki/LeafAnnotationTool

Learning to Count Leaves in Rosette Plants

Counting the number of leaves in plants is important for plant phenotyping, since it can be used to assess plant growth stages. We propose a learning-based approach for counting leaves in rosette (model) plants. We relate image-based descriptors learned in an unsupervised fashion to leaf counts using a supervised regression model. To take advantage of the circular and coplanar arrangement of leaves and also to introduce scale and rotation invariance, we learn features in a log-polar representation. Image patches extracted in this log-polar domain are provided to K-means, which builds a codebook in a unsupervised manner. Feature codes are obtained by projecting patches on the codebook using the triangle encoding, introducing both sparsity and specifically designed representation. A global, per-plant image descriptor is obtained by pooling local features in specific regions of the image. Finally, we provide the global descriptors to a support vector regression framework to estimate the number of leaves in a plant. We evaluate our method on datasets of the \textit{Leaf Counting Challenge} (LCC), containing images of Arabidopsis and tobacco plants. Experimental results show that on average we reduce absolute counting error by 40% w.r.t. the winner of the 2014 edition of the challenge -a counting via segmentation method. When compared to state-of-the-art density-based approaches to counting, on Arabidopsis image data ~75% less counting errors are observed. Our findings suggest that it is possible to treat leaf counting as a regression problem, requiring as input only the total leaf count per training image

Imatinib-mesylate for all patients with hypereosinophilic syndrome?

Some recent papers have focused on the activity of imatinib-mesylate, a selective inhibitor of tyrosine kinase, in idiopathic hypereosinophilic syndrome (HES) [1], [2], [3] and [4]. In this setting, a possible therapeutic target was identified by Cools et al. [2], who described the fusion tyrosine-kinase gene FIP1L1/PDGFRA as the result of an interstitial deletion within chromosome 4 in nine out of sixteen (56%) patients affected by HES. Of interest, although in this study the response to imatinib was strictly correlated with the presence of FIP1L1/PDGFRA rearrangement (all patients with such a molecular lesion treated with imatinib responded), only five out of nine responding patients evidenced the abnormal transcript [2]. Among the possible alternative mechanisms for the activation of the PDGFRA tyrosine-kinase domain, these authors suggested there may be a different fusion gene

Minilaparoscopic Versus Open Pyeloplasty in Children Less Than 1 Year

Purpose: The aim of this study is to compare minilaparoscopic (MLS) and open pyeloplasty (OP) in children <1 year in terms of intra- and perioperative outcomes and esthetic results. Materials and Methods: Patients <1 year of age, with prenatal hydronephrosis, who underwent Anderson-Hynes pyeloplasty for monolateral ureteropelvic junction obstruction (UPJO) at our center from January 2016 to August 2017 were enrolled in the study. Outcomes evaluated were as follows: operative time, length of hospital stay, and postoperative pain anterior-posterior pelvic diameter (APD) reduction. The Vancouver Scar Scale (VSS) was utilized to evaluate esthetic results. Mean follow-up was 26.5 months. Results: Eighteen patients (11M, 7F) of mean age 8.1 months (range 4-12) and mean weight 8.5 kg (range 7-10) underwent Anderson-Hynes pyeloplasty in the study period. Nine of eighteen underwent OP, and 9/18 underwent MLS. Mean operative time was 167 minutes for MLS versus 153 minutes for OP (P = .14). Mean hospital stay was 3.9 days for MLS versus 5.3 days for OP (P = .11). Mean APD reduction was 13.6 mm for MLS and 16.5 mm for OP procedures (P = .63). Mean VSS score was 1.3 for VLS versus 3.4 for OP (P = .04). Conclusions: MLS pyeloplasty is feasible and safe, and reported equivalent results as open procedure for management of UPJO also in toddlers and infants. We found that the only significant difference between the two approaches in children <1 year was represented by the esthetic outcome in the short follow-up period

A survey of the main technology, biochemical and microbiological features influencing the concentration of biogenic amines of twenty Apulian and Sicilian (Southern Italy) cheeses

Abstract Twenty Apulian and Sicilian cheeses were analysed for their concentrations of eight biogenic amines (BAs), free amino acids, pH, water activity, and subjected to microbiological characterisation. In addition, lactic acid bacteria isolated from cheeses were assayed for their capacity to generate BAs. Principal component analysis was performed to find the effect of different parameters on the distribution of the cheeses. Although short-ripened (≤30 d) cheeses did not show significant BA concentrations, the only BA showing high positive correlation with time of ripening was histamine. Concentration of histidine and, especially, percentage of histidine-decarboxylase bacteria presumably affected histamine concentration. High pH values were negatively correlated to the concentration of tyramine, putrescine, and cadaverine. Fifty percent of the cheeses contained at least one BA at potentially toxic concentrations. Unambiguous and ever-valid relations among parameters and BAs are difficult to determine, because BAs are the result of combined and varied factors