Predicting Adverse Events in Children with Fever and Chemotherapy-Induced Neutropenia. Results of the Prospective Multicenter SPOG 2003 FN Study

PURPOSE To develop a score predicting the risk of adverse events (AEs) in pediatric patients with cancer who experience fever and neutropenia (FN) and to evaluate its performance. PATIENTS AND METHODS Pediatric patients with cancer presenting with FN induced by nonmyeloablative chemotherapy were observed in a prospective multicenter study. A score predicting the risk of future AEs (ie, serious medical complication, microbiologically defined infection, radiologically confirmed pneumonia) was developed from a multivariate mixed logistic regression model. Its cross-validated predictive performance was compared with that of published risk prediction rules. Results An AE was reported in 122 (29%) of 423 FN episodes. In 57 episodes (13%), the first AE was known only after reassessment after 8 to 24 hours of inpatient management. Predicting AE at reassessment was better than prediction at presentation with FN. A differential leukocyte count did not increase the predictive performance. The score predicting future AE in 358 episodes without known AE at reassessment used the following four variables: preceding chemotherapy more intensive than acute lymphoblastic leukemia maintenance (weight = 4), hemoglobin > or = 90 g/L (weight = 5), leukocyte count less than 0.3 G/L (weight = 3), and platelet count less than 50 G/L (weight = 3). A score (sum of weights) > or = 9 predicted future AEs. The cross-validated performance of this score exceeded the performance of published risk prediction rules. At an overall sensitivity of 92%, 35% of the episodes were classified as low risk, with a specificity of 45% and a negative predictive value of 93%. CONCLUSION This score, based on four routinely accessible characteristics, accurately identifies pediatric patients with cancer with FN at risk for AEs after reassessment.

Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar

In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.

Transfer of cadmium and barium from soil to crops grown in tropical soils

Phytotoxicity and transfer of potentially toxic elements, such as cadmium (Cd) or barium (Ba), depend on the availability of these elements in soils and on the plant species exposed to them. With this study, we aimed to evaluate the effect of Cd and Ba application rates on yields of pea (Pisum sativum L.), sorghum (Sorghum bicolor L.), soybean (Glycine max L.), and maize (Zea mays L.) grown under greenhouse conditions in an Oxisol and an Entisol with contrasting physical and chemical properties, and to correlate the amount taken up by plants with extractants commonly used in routine soil analysis, along with transfer coefficients (Bioconcentration Factor and Transfer Factor) in different parts of the plants. Plants were harvested at flowering stage and measured for yield and Cd or Ba concentrations in leaves, stems, and roots. The amount of Cd accumulated in the plants was satisfactorily evaluated by both DTPA and Mehlich-3 (M-3). Mehlich-3 did not relate to Ba accumulated in plants, suggesting it should not be used to predict Ba availability. The transfer coefficients were specific to soils and plants and are therefore not recommended for direct use in risk assessment models without taking soil properties and group of plants into account.

Potential of Spectroradiometry to Classify Soil Clay Content

ABSTRACT Diffuse reflectance spectroscopy (DRS) is a fast and cheap alternative for soil clay, but needs further investigation to assess the scope of application. The purpose of the study was to develop a linear regression model to predict clay content from DRS data, to classify the soils into three textural classes, similar to those defined by a regulation of the Brazilian Ministry of Agriculture, Livestock and Food Supply. The DRS data of 412 soil samples, from the 0.0-0.5 m layer, from different locations in the state of Rio Grande do Sul, Brazil, were measured at wavelengths of 350 to 2,500 nm in the laboratory. The fitting of the linear regression model developed to predict soil clay content from the DRS data was based on a R2 value of 0.74 and 0.75, with a RMSE of 7.82 and 8.51 % for the calibration and validation sets, respectively. Soil texture classification had an overall accuracy of 79.0 % (calibration) and 80.9 % (validation). The heterogeneity of soil samples affected the performance of the prediction models. Future studies should consider a previous classification of soil samples in different groups by soil type, parent material and/or sampling region.

Early antifungal intervention strategies in ICU patients.

PURPOSE OF REVIEW: Despite progress in the understanding of the pathophysiology of invasive candidiasis, and the development of new classes of well tolerated antifungals, invasive candidiasis remains a disease difficult to diagnose, and associated with significant morbidity and mortality. Early antifungal treatment may be useful in selected groups of patients who remain difficult to identify prospectively. The purpose of this review is to summarize the recent development of risk-identification strategies targeting early identification of ICU patients susceptible to benefit from preemptive or empirical antifungal treatment. RECENT FINDINGS: Combinations of different risk factors are useful in identifying high-risk patients. Among the many risk factors predisposing to invasive candidiasis, colonization has been identified as one of the most important. In contrast to prospective surveillance of the dynamics of colonization (colonization index), integration of clinical colonization status in risk scores models significantly improve their accuracy in identifying patients at risk of invasive candidiasis. SUMMARY: To date, despite limited prospective validation, clinical models targeted at early identification of patients at risk to develop invasive candidiasis represent a major advance in the management of patients at risk of invasive candidiasis. Moreover, large clinical studies using such risk scores or predictive rules are underway.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies.

ABSTRACT: BACKGROUND: Decision curve analysis has been introduced as a method to evaluate prediction models in terms of their clinical consequences if used for a binary classification of subjects into a group who should and into a group who should not be treated. The key concept for this type of evaluation is the "net benefit", a concept borrowed from utility theory. METHODS: We recall the foundations of decision curve analysis and discuss some new aspects. First, we stress the formal distinction between the net benefit for the treated and for the untreated and define the concept of the "overall net benefit". Next, we revisit the important distinction between the concept of accuracy, as typically assessed using the Youden index and a receiver operating characteristic (ROC) analysis, and the concept of utility of a prediction model, as assessed using decision curve analysis. Finally, we provide an explicit implementation of decision curve analysis to be applied in the context of case-control studies. RESULTS: We show that the overall net benefit, which combines the net benefit for the treated and the untreated, is a natural alternative to the benefit achieved by a model, being invariant with respect to the coding of the outcome, and conveying a more comprehensive picture of the situation. Further, within the framework of decision curve analysis, we illustrate the important difference between the accuracy and the utility of a model, demonstrating how poor an accurate model may be in terms of its net benefit. Eventually, we expose that the application of decision curve analysis to case-control studies, where an accurate estimate of the true prevalence of a disease cannot be obtained from the data, is achieved with a few modifications to the original calculation procedure. CONCLUSIONS: We present several interrelated extensions to decision curve analysis that will both facilitate its interpretation and broaden its potential area of application.

Bud dormancy in apple trees after thermal fluctuations

The objective of this work was to evaluate the effect of heat waves on the evolution of bud dormancy, in apple trees with contrasting chilling requirements. Twigs of 'Castel Gala' and 'Royal Gala' were collected in orchards in Papanduva, state of Santa Catarina, Brazil, and were exposed to constant (3°C) or alternating (3 and 15°C for 12/12 hours) temperature, combined with zero, one or two days a week at 25°C. Two additional treatments were evaluated: constant temperature (3°C), with a heat wave of seven days at 25°C, in the beginning or in the middle of the experimental period. Periodically, part of the twigs was transferred to 25°C for daily budburst evaluation of apical and lateral buds. Endodormancy (dormancy induced by cold) was overcome with less than 330 chilling hours (CH) of constant cold in 'Castel Gala' and less than 618 CH in 'Royal Gala'. A daily 15°C-temperature cycle did not affect the endodormancy process. Heat waves during endodormancy resulted in an increased CH to achieve bud requirements. The negative effect of high temperature depended on the lasting of this condition. Chilling was partly cancelled during dormancy when the heat wave lasted 36 continuous hours or more. Therefore, budburst prediction models need adjustments, mainly for regions with mild and irregular winters, such as those of Southern Brazil.

Genes outside The Hla Region affecting Susceptibility to Type 1 Diabetes - The Role of Iddm2 and Iddm9 in the Finnish Population

Tyypin 1 diabeteksen perinnöllinen alttius Suomessa - HLA-alueen ulkopuolisten alttiuslokusten IDDM2 ja IDDM9 rooli taudin periytymisessä HLA-alue, joka sijaitsee kromosomissa 6p21.3, vastaa noin puolesta perinnöllisestä alttiudesta sairastua tyypin 1 diabetekseen. Myös HLA-alueen ulkopuolisten lokusten on todettu liittyvän sairausalttiuteen. Näistä kolmen lokuksen on varmistettu olevan todellisia alttiuslokuksia ja lisäksi useiden muiden, vielä varmistamattomien lokusten, on todettu liittyvän sairausalttiuteen. Tässä tutkimuksessa 12:n HLA-alueen ulkopuolisen alttiuslokuksen kytkentä tyypin 1 diabetekseen tutkittiin käyttäen 107:aa suomalaista multiplex-perhettä. Jatkotutkimuksessa analysoitiin IDDM9-alueen kytkentä ja assosiaatio sairauteen laajennetuissa perhemateriaaleissa sekä IDDM2-alueen mahdollinen interaktio HLA-alueen kanssa sairauden muodostumisessa. Lisäksi suoritettiin IDDM2-alueen suojaavien haplotyyppien alatyypitys tarkoituksena tutkia eri haplotyyppien käyttökelpoisuutta sairastumisriskin tarkempaa ennustamista varten. Ensimmäisessä kytkentätutkimuksessa ei löytynyt koko genomin tasolla merkitsevää tai viitteellistä kytkentää tutkituista HLA-alueen ulkopuolisista lokuksista. Voimakkain havaittu nimellisen merkitsevyyden tavoittava kytkentä nähtiin IDDM9-alueen markkerilla D3S3576 (MLS=1.05). Tutkimuksessa ei kyetty varmistamaan tai sulkemaan pois aiempia kytkentähavaintoja tutkituilla lokuksilla, mutta IDDM9-alueen jatkotutkimuksessa havaittu voimakas kytkentä (MLS=3.4) ja merkitsevä assosiaatio (TDT p=0.0002) viittaa vahvasti siihen, että 3q21-alueella sijaitsee todellinen tyypin 1 diabeteksen alttiusgeeni, jolloin alueen kattava assosiaatiotutkimus olisi perusteltu jatkotoimenpide. Sairauteen altistava IDDM2-alueen MspI-2221 genotyyppi CC oli nimellisesti yleisempi matalan tai kohtalaisen HLA-sairastumisriskin diabeetikoilla, verrattuna korkean HLA-riskin potilaisiin (p=0.05). Myös genotyyppijakauman vertailu osoitti merkitsevää eroa ryhmien välillä (p=0.01). VNTR-haplotyyppitutkimus osoitti, että IIIA/IIIA-homotsygootin sairaudelta suojaava vaikutus on merkitsevästi voimakkaampi kuin muiden luokka III:n genotyypeillä. Nämä tulokset viittaavat IDDM2-HLA -vuorovaikutukseen sekä siihen että IDDM2-alueen haplotyyppien välillä esiintyy etiologista heterogeniaa. Tämän johdosta IDDM2-alueen haplotyyppien tarkempi määrittäminen voisi tehostaa tyypin 1 diabeteksen riskiarviointia.

Pankkien vakavaraisuussäännöstön uudistus

Tutkielman tavoitteena on kuvata pankkien vakavaraisuusuudistuksen eri osa-alueita. Tarkempi analyysi rajautuu uudistuksen tuomiin muutoksiin luotto- ja operatiivisen riskin pääomavaateissa. Tutkielman empiirisen osuuden tavoitteena on perehtyä vakavaraisuussäännöstön uudistusten vaikutuksiin Nordeassa. Tutkimusmetodologiaksi on valittu normatiivinen tutkimusote. Lisäksi tutkielma sisältää deskriptiivisiä ja positivistisia osia. Lähdeaineisto koostuu Baselin pankkivalvontakomitean ja Suomen Pankin julkaisemista tutkimuksista ja dokumenteista sekä alan julkaisuissa ilmestyneistä artikkeleista. Pankkien vakavaraisuussäännöstöuudistuksen tavoitteena on lisätä rahoitusmarkkinoiden vakautta. Sääntelyn kautta pyritään turvaamaan pankkien varojen riittävyys suhteessa niiden riskien ottoon. Vakavaraisuussäännöstön uudistus muodostuu kolmesta pilarista: (1) minimipääomavaatimuksista, (2) pankkivalvonnan vahvistamisesta ja (3) markkinakurin hyödyntämisestä luottolaitosten toiminnan julkistamisvaatimuksia lisäämällä. Pankkivalvonnan harmonisoinnista vallitsee kansainvälinen yhteisymmärrys, mutta ennen kuin Basel II voi astua voimaan on useita ongelmia ratkaisematta. Baselin vakavaraisuuskehikko ei ole ainut lähitulevaisuudessa pankkitoimialaa koetteleva uudistus. Kansainväliset tilinpäätösstandardit; International Accounting Standards ja erityisesti IAS 39 sekä International Financial Reporting Standards, lyhyemmin IFRS tulevat muuttamaan merkittävästi pankkien tilinpäätöskäyttäytymistä. Epäselvää on vielä kuitenkin tukevatko uudistukset toisiaan ja missä määrin pankkien tulosvolatiliteetin odotetaan kasvavan. Tutkielmassa pohditaan vakavaraisuussäännöstön uudistuksen hyötyjä kansainvälisen kilpailuneutraliteetin osalta, sillä Yhdysvalloissa uudistus koskee vain suurimpia pankkeja. Tutkielmassa paneudutaan lisäksi uudistuksen mahdolliseen talouden syklejä voimistavaan vaikutukseen ja tarkastellaan parannusehdotuksia prosyklisyyden hillitsemiseksi. Yksi vakavaraisuusuudistuksen tärkeimmistä tehtävistä on luoda pankeille kannustin kehittää omia riskienhallinta malleja. Kannustin ongelma on pyritty ratkaisemaan vapaampien sisäisten mallien menetelmien avulla. Ongelmaa ei ole pystytty kuitenkaan ratkaisemaan aivan täysin, sillä luottoriskien osalta pankkien lainaportfolioiden rakenne määrittää sen, hyötyvätkö pankit siirtymisestä sisäisten mallien menetelmän käyttöön. Tutkielma sisältää myös Nordean arvion vakavaraisuusuudistuksen vaikutuksista pankkitoimialaan.

Un modelo de predicción de la insolvencia empresarial basado en variables financieras. Su aplicación al caso textil catalán (1994-1997)

En este documento se formula un modelo de predicción de la insolvencia a través de la combinación de diferentes variables cuantitativas extraídas de los estados contables de una muestra de empresas para los años 1994-1997. Partiendo del modelo de flexibilidad financiera de Donaldson, que es adaptado por Van Frederikslust a la predicción de la insolvencia, lo que aquí se expone es una aplicación a una muestra de empresas de los sectores textil y confección. Aunque los resultados no son alentadores, lo más importante es destacar cómo a través de una modelización de este tipo, probamos una formulación teórica del problema.

ESTRO-HERO survey: Radiotherapy staffing in the European countries: Final results from the ESTRO-HERO survey

Background: The ESTRO Health Economics in Radiation Oncology (HERO) project has the overall aim to develop a knowledge base of the provision of radiotherapy in Europe and build a model for health economic evaluation of radiation treatments at the European level. The first milestone was to assess the availability of radiotherapy resources within Europe. This paper presents the personnel data collected in the ESTRO HERO database. Materials and methods: An 84-item questionnaire was sent out to European countries, through their national scientific and professional radiotherapy societies. The current report includes a detailed analysis of radiotherapy staffing (questionnaire items 4760), analysed in relation to the annual number of treatment courses and the socio-economic status of the countries. The analysis was conducted between February and July 2014, and is based on validated responses from 24 of the 40 European countries defined by the European Cancer Observatory (ECO). Results: A large variation between countries was found for most parameters studied. Averages and ranges for personnel numbers per million inhabitants are 12.8 (2.530.9) for radiation oncologists, 7.6 (019.7) for medical physicists, 3.5 (012.6) for dosimetrists, 26.6 (1.978) for RTTs and 14.8 (0.461.0) for radiotherapy nurses. The combined average for physicists and dosimetrists is 9.8 per million inhabitants and 36.9 for RTT and nurses. Radiation oncologists on average treat 208.9 courses per year (range: 99.9348.8), physicists and dosimetrists conjointly treat 303.3 courses (range: 85757.7) and RTT and nurses 76.8 (range: 25.7156.8). In countries with higher GNI per capita, all personnel categories treat fewer courses per annum than in less affluent countries. This relationship is most evident for RTTs and nurses. Different clusters of countries can be distinguished on the basis of available personnel resources and socio-economic status. Conclusions: The average personnel figures in Europe are now consistent with, or even more favourable than the QUARTS recommendations, probably reflecting a combination of better availability as such, in parallel with the current use of more complex treatments than a decade ago. A considerable variation in available personnel and delivered courses per year however persists among the highest and lowest staffing levels. This not only reflects the variation in cancer incidence and socio-economic determinants, but also the stage in technology adoption along with treatment complexity and the different professional roles and responsibilities within each country. Our data underpin the need for accurate prediction models and long-term education and training programmes

Effect of population densities of Heterodera glycines race 3 on leaf area, photosynthesis and yield of soybean

The effect of Heterodera glycines on photosynthesis, leaf area and yield of soybean (Glycine max) was studied in two experiments carried out under greenhouse condition. Soybean seeds were sown in 1.5 l (Experiment 1) or 5.0 l (Experiment 2) clay pots filled with a mixture of field soil + sand (1:1) sterilized with methyl bromide. Eight days after sowing, seedlings were thinned to one per pot, and one day later inoculated with 0; 1.200; 3.600; 10.800; 32.400 or 97.200 J2 juveniles of H. glycines. Experiment 1 was carried out during the first 45 days of the inoculation while Experiment 2 was conducted during the whole cycle of the crop. Measurements of photosynthetic rate, stomatic conductance, chlorophyll fluorescence, leaf color, leaf area, and chlorophyll leaf content were taken at ten-day intervals throughout the experiments. Data on fresh root weight, top dry weight, grain yield, number of eggs/gram of roots, and nematode reproduction factor were obtained at the end of the trials. Each treatment was replicated ten times. There was a marked reduction in both photosynthetic rate and chlorophyll content, as well as an evident yellowing of the leaves of the infected plants. Even at the lowest Pi, the effects of H. glycines on the top dry weight or grain yield were quite severe. Despite the parasitism, soybean yield was highly correlated with the integrated leaf area and, accordingly, the use of this parameter was suggested for the design of potential damage prediction models that include physiological aspects of nematode-diseased plants.

The use of ALOS/PALSAR data for estimating sugarcane productivity

Some models have been developed using agrometeorological and remote sensing data to estimate agriculture production. However, it is expected that the use of SAR images can improve their performance. The main objective of this study was to estimate the sugarcane production using a multiple linear regression model which considers agronomic data and ALOS/PALSAR images obtained from 2007/08, 2008/09 and 2009/10 cropping seasons. The performance of models was evaluated by coefficient of determination, t-test, Willmott agreement index (d), random error and standard error. The model was able to explain 79%, 12% and 74% of the variation in the observed productions of the 2007/08, 2008/09 and 2009/10 cropping seasons, respectively. Performance of the model for the 2008/09 cropping season was poor because of the occurrence of a long period of drought in that season. When the three seasons were considered all together, the model explained 66% of the variation. Results showed that SAR-based yield prediction models can contribute and assist sugar mill technicians to improve such estimates.