Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Genetic variability in Brazilian populations of Biomphalaria straminea complex detected by simple sequence repeat anchored polymerase chain reaction amplification

Biomphalaria glabrata, B. tenagophila and B. straminea are intermediate hosts of Schistosoma mansoni, in Brazil. The latter is of epidemiological importance in the northwest of Brazil and, due to morphological similarities, has been grouped with B. intermedia and B. kuhniana in a complex named B. straminea. In the current work, we have standardized the simple sequence repeat anchored polymerase chain reaction (SSR-PCR) technique, using the primers (CA)8RY and K7, to study the genetic variability of these species. The similarity level was calculated using the Dice coefficient and genetic distance using the Nei and Li coefficient. The trees were obtained by the UPGMA and neighbor-joining methods. We have observed that the most related individuals belong to the same species and locality and that individuals from different localities, but of the same species, present clear heterogeneity. The trees generated using both methods showed similar topologies. The SSR-PCR technique was shown to be very efficient in intrapopulational and intraspecific studies of the B. straminea complex snails.

A simple modification of the Baermann method for diagnosis of strongyloidiasis

The diagnosis of Strongyloides stercoralis infections is routinely made by microscopic observation of larvae in stool samples, a low sensitivity method, or by other, most effective methods, such as the Baermann or agar culture plate methods. We propose in this paper a practical modification of Baermann method. One hundred and six stool samples from alcoholic patients were analyzed using the direct smear test, agar culture plate method, the standard Baermann method, and its proposed modification. For this modification the funnel used in the original version of the method is substituted by a test tube with a rubber stopper, perforated to allow insertion of a pipette tip. The tube with a fecal suspension is inverted over another tube containing 6 ml of saline solution and incubated at 37°C for at least 2 h. The saline solution from the second tube is centrifuged and the pellet is observed microscopically. Larva of S. stercoralis were detected in six samples (5.7%) by the two versions of the Baermann method. Five samples were positive using the agar culture plate method, and only in two samples the larva were observed using direct microscopic observation of fecal smears. Cysts of Endolimax nana and Entamoeba histolytica/dyspar were also detected in the modification of Baermann method. Data obtained by the modified Baermann method suggest that this methodology may helps concentrate larvae of S. stercoralis as efficiently as the original method.

Impact of an intervention to control risk associated with patient transfer.

QUESTION UNDER STUDY: Hospitals transferring patients retain responsibility until admission to the new health care facility. We define safe transfer conditions, based on appropriate risk assessment, and evaluate the impact of this strategy as implemented at our institution. METHODS: An algorithm defining transfer categories according to destination, equipment monitoring, and medication was developed and tested prospectively over 6 months. Conformity with algorithm criteria was assessed for every transfer and transfer category. After introduction of a transfer coordination centre with transfer nurses, the algorithm was implemented and the same survey was carried out over 1 year. RESULTS: Over the whole study period, the number of transfers increased by 40%, chiefly by ambulance from the emergency department to other hospitals and private clinics. Transfers to rehabilitation centres and nursing homes were reassigned to conventional vehicles. The percentage of patients requiring equipment during transfer, such as an intravenous line, decreased from 34% to 15%, while oxygen or i.v. drug requirement remained stable. The percentage of transfers considered below theoretical safety decreased from 6% to 4%, while 20% of transfers were considered safer than necessary. A substantial number of planned transfers could be "downgraded" by mutual agreement to a lower degree of supervision, and the system was stable on a short-term basis. CONCLUSION: A coordinated transfer system based on an algorithm determining transfer categories, developed on the basis of simple but valid medical and nursing criteria, reduced unnecessary ambulance transfers and treatment during transfer, and increased adequate supervision.

Sequencing of simple sequence repeat anchored polymerase chain reaction amplification products of Biomphalaria glabrata

Simple sequence repeat anchored polymerase chain reaction amplification (SSR-PCR) is a genetic typing technique based on primers anchored at the 5' or 3' ends of microsatellites, at high primer annealing temperatures. This technique has already been used in studies of genetic variability of several organisms, using different primer designs. In order to conduct a detailed study of the SSR-PCR genomic targets, we cloned and sequenced 20 unique amplification products of two commonly used primers, CAA(CT)6 and (CA)8RY, using Biomphalaria glabrata genomic DNA as template. The sequences obtained were novel B. glabrata genomic sequences. It was observed that 15 clones contained microsatellites between priming sites. Out of 40 clones, seven contained complex sequence repetitions. One of the repeats that appeared in six of the amplified fragments generated a single band in Southern analysis, indicating that the sequence was not widespread in the genome. Most of the annealing sites for the CAA(CT)6 primer contained only the six repeats found within the primer sequence. In conclusion, SSR-PCR is a useful genotyping technique. However, the premise of the SSR-PCR technique, verified with the CAA(CT)6 primer, could not be supported since the amplification products did not result necessarily from microsatellite loci amplification.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Simple steps to a healthy heart

The number of deaths from coronary heart disease in Northern Ireland has dropped significantly, according to recent figures. However, the Public Health Agency is urging everyone to take steps to protect their heart and reduce their chances of developing the disease during National Heart Month (February).Despite the number of deaths dropping significantly in recent years, coronary heart disease is still the number one killer across the country. Over 2,200 people died in Northern Ireland from coronary heart disease in 2010 compared to just over 2,300 people in 2009 - an overall reduction of 100 province-wide. The latest figure reveals the positive downward trend is continuing - in 2008, there were 2,410 deaths, 2,493 in 2007 and 2,554 in 2006, while in 1979 there were nearly 5,000 deaths. Throughout National Heart Month in February, the PHA is calling for people to follow a number of steps in a bid to reduce their chances of developing the disease.Smoking is a major risk factor and, the more cigarettes you smoke, the higher the risk, according to Dr Christine McMaster, Consultant in Public Health Medicine, with responsibility for cardiovascular disease in the PHA."The reduction in smoking over the past number of years through public education, stop smoking programmes and smoke free legislation has had a major impact on reducing deaths from heart disease. However, 24% of the population in Northern Ireland still smoke, putting them at risk of developing the disease."People who suffer from high blood pressure also run an increased risk of developing coronary heart disease. High blood pressure is a silent, but treatable condition. In order to minimise the risk, I would urge everyone over the age of 45 to have their blood pressure measured every five years by their GP," said Dr McMaster.Simple lifestyle changes will also reduce the risk of heart disease, including eating at least five servings of fruit and vegetables a day, avoiding saturated fats, limiting alcohol intake and taking at least 30 minutes of exercise a day, five days a week.Dr McMaster described the reduction in deaths from coronary heart disease over the past few years as "a big success story". "It shows that people can take very positive steps to reduce their risk of heart disease by getting their blood pressure checked and adopting a healthier lifestyle; in particular by not smoking," he added. "The message is clear during National Heart Month - you only have one heart and you can take steps to keep it healthy."

UOC Algorithms

Aplicació per a iPad a mode de repositori de continguts relacionats amb l'ensenyament d'assignatures d'informàtica.

How they measure up: A simple guide to food labelling

The aim of this leaflet is to help you pick out the information you need when you're trying to choose between food products. Labels may appear to be very different in shape and size, colour and design and in what they tell us about the food or drink inside.

Evaluation of organ-specific peripheral doses after 2-dimensional, 3-dimensional and hybrid intensity modulated radiation therapy for breast cancer based on Monte Carlo and convolution/superposition algorithms: implications for secondary cancer risk assessment.

To make a comprehensive evaluation of organ-specific out-of-field doses using Monte Carlo (MC) simulations for different breast cancer irradiation techniques and to compare results with a commercial treatment planning system (TPS). Three breast radiotherapy techniques using 6MV tangential photon beams were compared: (a) 2DRT (open rectangular fields), (b) 3DCRT (conformal wedged fields), and (c) hybrid IMRT (open conformal+modulated fields). Over 35 organs were contoured in a whole-body CT scan and organ-specific dose distributions were determined with MC and the TPS. Large differences in out-of-field doses were observed between MC and TPS calculations, even for organs close to the target volume such as the heart, the lungs and the contralateral breast (up to 70% difference). MC simulations showed that a large fraction of the out-of-field dose comes from the out-of-field head scatter fluence (>40%) which is not adequately modeled by the TPS. Based on MC simulations, the 3DCRT technique using external wedges yielded significantly higher doses (up to a factor 4-5 in the pelvis) than the 2DRT and the hybrid IMRT techniques which yielded similar out-of-field doses. In sharp contrast to popular belief, the IMRT technique investigated here does not increase the out-of-field dose compared to conventional techniques and may offer the most optimal plan. The 3DCRT technique with external wedges yields the largest out-of-field doses. For accurate out-of-field dose assessment, a commercial TPS should not be used, even for organs near the target volume (contralateral breast, lungs, heart).

A simple way to decompress the left ventricle during venoarterial bypass

OBJECTIVE: The aim of this investigation was to improve the hemodynamics during venoarterial bypass by remote decompression of the left ventricle (LV). METHODS: Venoarterial bypass was established in 5 bovine experiments (69+/-10 kg) by the transjugular insertion of a self-expanding cannula (smartcanula) with return through a carotid artery. Cardiogenic shock was simulated with ventricular fibrillation induced by an external stimulator. Left ventricular decompression was achieved by switching to transfemoral drainage of the pulmonary artery (PA) with a long self-expanding cannula. RESULTS: Initial pump flow was 4.7+/-0.9 l/min and the aortic pressure accounted for 75+/-21 mmHg. After induction of ventricular fibrillation, the pump flow dropped after 11+/-8 min to 2.5+/-0.1 l/min. Transfemoral decompression increased the pump flow to 5.6+/-0.7 l/min, while the RV pressure decreased from 27+/-9 to 3+/-5 mmHg, the PA pressure decreased from 29+/-7 to 5+/-4 mmHg, the LV pressure decreased from 29+/-6 to 7+/-2 mmHg, and the aortic pressure increased from 31+/-3 to 47+/-11 mmHg. CONCLUSIONS: Remote drainage of the pulmonary artery during venoarterial bypass allows for effective decompression of the left ventricle and provides superior hemodynamics.