A DEEP LEARNING APPROACH FOR THE RECOGNITION OF URBAN GROUND PAVEMENTS IN HISTORICAL SITES

Urban management is a topic of great interest for local administrators, particularly because it is strongly connected to smart city issues and can have a great impact on making cities more sustainable. In particular, thinking about the management of the physical accessibility of cities, the possibility of automating data collection in urban areas is of great interest. Focusing then on historical centres and urban areas of cities and historical sites, it can be noted that their ground surfaces are generally characterised by the use of a multitude of different pavements. To strengthen the management of such urban areas, a comprehensive mapping of the different pavements can be very useful. In this paper, the survey of a historical city (Sabbioneta, in northern Italy) carried out with a Mobile Mapping System (MMS) was used as a starting point. The approach here presented exploit Deep Learning (DL) to classify the different pavings. Firstly, the points belonging to the ground surfaces of the point cloud were selected and the point cloud was rasterised. Then the raster images were used to perform a material classification using the Deep Learning approach, implementing U-Net coupled with ResNet 18. Five different classes of materials were identified, namely sampietrini, bricks, cobblestone, stone, asphalt. The average accuracy of the result is 94%

Configuring the neighbourhood effect in irregular cellular automata based models

Cellular automata (CA) models have been widely employed to simulate urban growth and land use change. In order to represent urban space more realistically, new approaches to CA models have explored the use of vector data instead of traditional regular grids. However, the use of irregular CA-based models brings new challenges as well as opportunities. The most strongly affected factor when using an irregular space is neighbourhood. Although neighbourhood definition in an irregular environment has been reported in the literature, the question of how to model the neighbourhood effect remains largely unexplored. In order to shed light on this question, this paper proposed the use of spatial metrics to characterise and measure the neighbourhood effect in irregular CA-based models. These metrics, originally developed for raster environments, namely the enrichment factor and the neighbourhood index, were adapted and applied in the irregular space employed by the model. Using the results of these metrics, distance-decay functions were calculated to reproduce the push-and-pull effect between the simulated land uses. The outcomes of a total of 55 simulations (five sets of different distance functions and eleven different neighbourhood definition distances) were compared with observed changes in the study area during the calibration period. Our results demonstrate that the proposed methodology improves the outcomes of the urban growth simulation model tested and could be applied to other irregular CA-based models

LINGO1 Variants in the French-Canadian Population

Essential tremor (ET) is a complex genetic disorder for which no causative gene has been found. Recently, a genome-wide association study reported that two variants in the LINGO1 locus were associated to this disease. The aim of the present study was to test if this specific association could be replicated using a French-Canadian cohort of 259 ET patients and 479 ethnically matched controls. Our genotyping results lead us to conclude that no association exists between the key variant rs9652490 and ET (Pcorr = 1.00)

Efficacy and safety of lurbinectedin and doxorubicin in relapsed small cell lung cancer. Results from an expansion cohort of a phase I study

Background A phase I study found remarkable activity and manageable toxicity for doxorubicin (bolus) plus lurbinectedin (1-h intravenous [i.v.] infusion) on Day 1 every three weeks (q3wk) as second-line therapy in relapsed small cell lung cancer (SCLC). An expansion cohort further evaluated this combination. Patients and methods Twenty-eight patients with relapsed SCLC after no more than one line of cytotoxic-containing chemotherapy were treated: 18 (64%) with sensitive disease (chemotherapy-free interval [CTFI] ≥90 days) and ten (36%) with resistant disease (CTFI <90 days; including six with refractory disease [CTFI ≤30 days]). Results Ten patients showed confirmed response (overall response rate [ORR] = 36%); median progression-free survival (PFS) = 3.3 months; median overall survival (OS) = 7.9 months. ORR was 50% in sensitive disease (median PFS = 5.7 months; median OS = 11.5 months) and 10% in resistant disease (median PFS = 1.3 months; median OS = 4.6 months). The main toxicity was transient and reversible myelosuppression. Treatment-related non-hematological events (fatigue, nausea, decreased appetite, vomiting, alopecia) were mostly mild or moderate. Conclusion Doxorubicin 40 mg/m(2) and lurbinectedin 2.0 mg/m(2) on Day 1 q3wk has shown noteworthy activity in relapsed SCLC and a manageable safety profile. The combination is being evaluated as second-line therapy for SCLC in an ongoing, randomized phase III trial. Clinical trial registration www.ClinicalTrials.gov code: NCT01970540. Date of registration: 22 October, 2013

Parkinson's disease-linked mutations in VPS35 induce dopaminergic neurodegeneration

Mutations in the vacuolar protein sorting 35 homolog (VPS35) gene at the PARK17 locus, encoding a key component of the retromer complex, were recently identified as a new cause of late-onset, autosomal dominant Parkinson's disease (PD). Here we explore the pathogenic consequences of PD-associated mutations in VPS35 using a number of model systems. VPS35 exhibits a broad neuronal distribution throughout the rodent brain, including within the nigrostriatal dopaminergic pathway. In the human brain, VPS35 protein levels and distribution are similar in tissues from control and PD subjects, and VPS35 is not associated with Lewy body pathology. The common D620N missense mutation in VPS35 does not compromise its protein stability or localization to endosomal and lysosomal vesicles, or the vesicular sorting of the retromer cargo, sortilin, SorLA and cation-independent mannose 6-phosphate receptor, in rodent primary neurons or patient-derived human fibroblasts. In yeast we show that PD-linked VPS35 mutations are functional and can normally complement VPS35 null phenotypes suggesting that they do not result in a loss-of-function. In rat primary cortical cultures the overexpression of human VPS35 induces neuronal cell death and increases neuronal vulnerability to PD-relevant cellular stress. In a novel viral-mediated gene transfer rat model, the expression of D620N VPS35 induces the marked degeneration of substantia nigra dopaminergic neurons and axonal pathology, a cardinal pathological hallmark of PD. Collectively, these studies establish that dominant VPS35 mutations lead to neurodegeneration in PD consistent with a gain-of-function mechanism, and support a key role for VPS35 in the development of PD

Intercalação e ordenação de arquivos por algoritmos de fusão.

Os algoritmos que utilizam técnicas de fusão (merge) de arquivos para a intercalação ou a ordenação de dois ou mais arquivos seqüenciais são conhecidos há muitos anos. Entretanto, as facilidades atualmente disponíveis para manipulação de bases de dados e arquivos como, por exemplo, a ampla e crescente utilização de sistemas gerenciadores de banco de dados, fazem com que esses algoritmos, em geral, rápidos e de fácil desenvolvimento e entendimento, sejam pouco considerados por profissionais e pouco estudados em cursos de graduação. Contudo, essas soluções de intercalação e ordenação são facilmente implementadas em ambientes de computação com poucos recursos e podem ser utilizadas em memória principal ou secundária, de modo interativo ou recursivo, gerando eficientes soluções que, praticamente, não dependem do aporte computacional do sistema hospedeiro

Impact analysis of accidents on the traffic flow based on massive floating car data

The wide usage of GPS-equipped devices enables the mass recording of vehicle movement trajectories describing the movement behavior of the traffic participants. An important aspect of the road traffic is the impact of anomalies, like accidents, on traffic flow. Accidents are especially important as they contribute to the the aspects of safety and also influence travel time estimations. In this paper, the impact of accidents is determined based on a massive GPS trajectory and accident dataset. Due to the missing precise date of the accidents in the data set used, first, the date of the accident is estimated based on the speed profile at the accident time. Further, the temporal impact of the accident is estimated using the speed profile of the whole day. The approach is applied in an experiment on a one month subset of the datasets. The results show that more than 72% of the accident dates are identified and the impact on the temporal dimension is approximated. Moreover, it can be seen that accidents during the rush hours and on high frequency road types (e.g. motorways, trunks or primaries) have an increasing effect on the impact duration on the traffic flow