2D-3D registration of CT vertebra volume to fluoroscopy projection: A calibration model assessment (doi:10.1155/2010/806094)

This study extends a previous research concerning intervertebral motion registration by means of 2D dynamic fluoroscopy to obtain a more comprehensive 3D description of vertebral kinematics. The problem of estimating the 3D rigid pose of a CT volume of a vertebra from its 2D X-ray fluoroscopy projection is addressed. 2D-3D registration is obtained maximising a measure of similarity between Digitally Reconstructed Radiographs (obtained from the CT volume) and real fluoroscopic projection. X-ray energy correction was performed. To assess the method a calibration model was realised a sheep dry vertebra was rigidly fixed to a frame of reference including metallic markers. Accurate measurement of 3D orientation was obtained via single-camera calibration of the markers and held as true 3D vertebra position; then, vertebra 3D pose was estimated and results compared. Error analysis revealed accuracy of the order of 0.1 degree for the rotation angles of about 1?mm for displacements parallel to the fluoroscopic plane, and of order of 10?mm for the orthogonal displacement.<br/

A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data

Accurate flood mapping is important for both planning activities during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this paper, a Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood that occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%

JET ANALYSIS BY NEURAL NETWORKS IN HIGH ENERGY HADRON-HADRON COLLISIONS

We study the possibility to employ neural networks to simulate jet clustering procedures in high energy hadron-hadron collisions. We concentrate our analysis on the Fermilab Tevatron energy and on the $k_\bot$ algorithm. We consider both supervised multilayer feed-forward network trained by the backpropagation algorithm and unsupervised learning, where the neural network autonomously organizes the events in clusters.Comment: 9 pages, latex, 2 figures not included

Global approaches and local strategies for phase unwrapping

Phase unwrapping, i.e. the retrieval of absolute phases from wrapped, noisy measures, is a tough problem because of the presence of rotational inconsistencies (residues), randomly generated by noise and undersampling on the principal phase gradient field. These inconsistencies prevent the recovery of the absolute phase field by direct integration of the wrapped gradients. In this paper we examine the relative merit of known global approaches and then we present evidence that our approach based on “stochastic annealing” can recover the true phase field also in noisy areas with severe undersampling, where other methods fail. Then, some experiments with local approaches are presented. A fast neural filter has been trained to eliminate close residue couples by joining them in a way which takes into account the local phase information. Performances are about 60–70% of the residues. Finally, other experiments have been aimed at designing an automated method for the determination of weight matrices to use in conjunction with local phase unwrapping algorithms. The method, tested with the minimum cost flow algorithm, gives good performances over both simulated and real data

High-frequency information content in end-user foreign exchange order flows

This article considers the impact of foreign exchange (FX) order flows on contemporaneous and future stock market returns using a new database of customer order flows in the euro-dollar exchange rate market as seen by a leading European bank. We do not find clear contemporaneous relationships between FX order flows and stock market changes at high frequencies, but FX flows do appear to have significant power to forecast stock index returns over 1–30 min horizons, after controlling for lagged exchange rate and stock market returns. The effects of order flows from financial customers on future stock market changes are negative, while the effects of corporate orders are positive. The latter results are consistent with the premise that corporate order flows contain dispersed, passively acquired information about fundamentals. Thus, purchases of the dollar by corporate customers represent good news about the state of the US economy. Importantly, though, there also appears to be extra information in corporate flows which is directly relevant to equity prices over and above the impact derived from stock prices reacting to (predicted) exchange rate changes. Our findings suggest that financial customer flows only affect stock prices through their impact on the value of the dollar

Implications of miRNA expression pattern in bovine oocytes and follicular fluids for developmental competence

Developmental competence determines the oocyte capacity to support initial embryo growth, but the molecular mechanisms underlying this phenomenon are still ill-defined. Changes in microRNA (miRNA) expression pattern have been described during follicular growth in several species. Therefore, aim of this study was to investigate whether miRNA expression pattern in cow oocyte and follicular fluid (FF) is associated with the acquisition of developmental competence. Samples were collected from ovaries with more than, or fewer than, 10 mid-antral follicles (H- and L-ovaries) because previous studies demonstrated that this parameter is a reliable predictor of oocyte competence. After miRNA deep sequencing and bioinformatic data analysis, we identified 58 miRNAs in FF and 6 in the oocyte that were differentially expressed between H- and L-ovaries. Overall, our results indicate that miRNA levels both in FF and in the ooplasm must remain within specific thresholds and that changes in either direction compromising oocyte competence. Some of the miRNAs found in FF (miR-769, miR-1343, miR-450a, miR-204, miR-1271 and miR-451) where already known to regulate follicle growth and their expression pattern indicate that they are also involved in the acquisition of developmental competence. Some miRNAs were differentially expressed in both compartments but with opposite patterns, suggesting that miRNAs do not flow freely between FF and oocyte. Gene Ontology analysis showed that the predicted gene targets of most differentially expressed miRNAs are part of a few signalling pathways. Regulation of maternal mRNA storage and mitochondrial activity seem to be the processes more functionally relevant in determining oocyte quality. In conclusion, our data identified a few miRNAs in the follicular fluid and in the ooplasm that modulate the oocyte developmental competence. This provides new insights that could help with the management of cattle reproductive efficiency

The IASI Water Deficit Index to Monitor Vegetation Stress and Early Drying in Summer Heatwaves: An Application to Southern Italy

The boreal hemisphere has been experiencing increasing extreme hot and dry conditions over the past few decades, consistent with anthropogenic climate change. The continental extension of this phenomenon calls for tools and techniques capable of monitoring the global to regional scales. In this context, satellite data can satisfy the need for global coverage. The main objective we have addressed in the present paper is the capability of infrared satellite observations to monitor the vegetation stress due to increasing drought and heatwaves in summer. We have designed and implemented a new water deficit index (wdi) that exploits satellite observations in the infrared to retrieve humidity, air temperature, and surface temperature simultaneously. These three parameters are combined to provide the water deficit index. The index has been developed based on the Infrared Atmospheric Sounder Interferometer or IASI, which covers the infrared spectral range 645 to 2760 cm−1 with a sampling of 0.25 cm−1. The index has been used to study the 2017 heatwave, which hit continental Europe from May to October. In particular, we have examined southern Italy, where Mediterranean forests suffer from climate change. We have computed the index’s time series and show that it can be used to indicate the atmospheric background conditions associated with meteorological drought. We have also found a good agreement with soil moisture, which suggests that the persistence of an anomalously high water deficit index was an essential driver of the rapid development and evolution of the exceptionally severe 2017 droughts

Integration of persistent scatterer interferometry and ground data for landslide monitoring: the Pianello landslide (Bovino, Southern Italy)

We present an example of integration of persistent scatterer interferometry (PSI) and in situ measurements over a landslide in the Bovino hilltop town, in Southern Italy. First, a wide-area analysis of PSI data, derived from legacy ERS and ENVISAT SAR image time series, highlighted the presence of ongoing surface displacements over the known limits of the Pianello landslide, located at the outskirts of the Bovino municipality, in the periods 1995–1999 and 2003–2008, respectively. This prompted local authorities to install borehole inclinometers on suitable locations. Ground data collected by these sensors during the following years were then compared and integrated with more recent PSI data from a series of Sentinel-1 images, acquired from March 2014 to October 2016. The integration allows sketching a consistent qualitative model of the landslide spatial and subsurface structure, leading to a coherent interpretation of remotely sensed and ground measurements. The results were possible thanks to the synergistic operation of local authorities and remote sensing specialists, and could represent an example for best practices in environmental management and protection at the regional scale

Stochastic learning in a neural network with adapting synapses

We consider a neural network with adapting synapses whose dynamics can be analitically computed. The model is made of $N$ neurons and each of them is connected to $K$ input neurons chosen at random in the network. The synapses are $n$-states variables which evolve in time according to Stochastic Learning rules; a parallel stochastic dynamics is assumed for neurons. Since the network maintains the same dynamics whether it is engaged in computation or in learning new memories, a very low probability of synaptic transitions is assumed. In the limit $N\to\infty$ with $K$ large and finite, the correlations of neurons and synapses can be neglected and the dynamics can be analitically calculated by flow equations for the macroscopic parameters of the system.Comment: 25 pages, LaTeX fil

Age is not the only risk factor in COVID-19: the role of comorbidities and of long staying in residential care homes

Background: The actual SARS-CoV-2 outbreak caused a highly transmissible disease with a tremendous impact on elderly people. So far, few studies focused on very elderly patients (over 80 years old). In this study we examined the clinical presentation and the outcome of the disease in this group of patients, admitted to our Hospital in Rome. Methods: This is a single-center, retrospective study performed in the Sant’Andrea University Hospital of Rome. We included patients older than 65 years of age with a diagnosis of COVID-19, from March 2020 to May 2020, divided in two groups according to their age (Elderly: 65–80 years old; Very Elderly &gt; 80 years old). Data extracted from the each patient record included age, sex, comorbidities, symptoms at onset, the Pneumonia Severity Index (PSI), the ratio of the partial pressure of oxygen in arterial blood (PaO2) to the inspired oxygen fraction (FiO2) (P/F) on admission, laboratory tests, radiological findings on computer tomography (CT), length of hospital stay (LOS), mortality rate and the viral shedding. The differences between the two groups were analyzed by the Fisher’s exact test or the Wilcoxon signed-rank test for categorical variables and the Mann-Whitney U test for continuous variables. To assess significance among multiple groups of factors, we used the Bonferroni correction. The survival time was estimated by Kaplan-Meier method and Log Rank Test. Univariate and Multivariate logistic regression were performed to estimate associations between age, comorbidities, provenance from long-stay residential care homes (LSRCH) s and clinical outcomes. Results: We found that Very Elderly patients had an increased mortality rate, also due to the frequent occurrence of multiple comorbidities. Moreover, we found that patients coming from LSRCHs appeared to be highly susceptible and vulnerable to develop severe manifestations of the disease. Conclusion: We demonstrate that there were considerable differences between Elderly and Very Elderly patients in terms of inflammatory activity, severity of disease, adverse clinical outcomes. To establish a correct risk stratification, comorbidities and information about provenience from LSRCHs should be considered