Reproducibility of species lists, visual cover estimates and frequency methods for recording high mountain vegetation

Question: When multiple observers record the same spatial units of alpine vegetation, how much variation is there in the records and what are the consequences of this variation for monitoring schemes to detect change? Location: One test summit in Switzerland (Alps) and one test summit in Scotland (Cairngorm Mountains). Method: Eight observers used the GLORIA protocols for species composition and visual cover estimates in percent on large summit sections (>100 m2) and species composition and frequency in nested quadrats (1 m2). Results: The multiple records from the same spatial unit for species composition and species cover showed considerable variation in the two countries. Estimates of pseudoturnover of composition and coefficients of variation of cover estimates for vascular plant species in 1m x 1m quadrats showed less variation than in previously published reports whereas our results in larger sections were broadly in line with previous reports. In Scotland, estimates for bryophytes and lichens were more variable than for vascular plants. Conclusions: Statistical power calculations indicated that, unless large numbers of plots were used, changes in cover or frequency were only likely to be detected for abundant species (exceeding 10% cover) or if relative changes were large (50% or more). Lower variation could be reached with the point methods and with larger numbers of small plots. However, as summits often strongly differ from each other, supplementary summits cannot be considered as a way of increasing statistical power without introducing a supplementary component of variance into the analysis and hence the power calculations.

Effect of low-dose chest irradiation before chemotherapy on the rate of local failure in small-cell lung cancer.

PURPOSE: To evaluate the rate of tumor recurrence within the irradiated volume after initial low-dose irradiation of limited-stage small-cell lung cancer (SCLC), to assess the tolerance of a sequential combination of low-dose chest irradiation followed by chemotherapy, and to confirm the responsiveness of limited-stage SCLC to low-dose irradiation. METHODS AND MATERIALS: In this pilot study, 26 patients with limited-stage SCLC were treated by first-line 20-Gy thoracic irradiation followed 3 weeks later by chemotherapy (cisplatin, doxorubicin, and etoposide for six cycles). RESULTS: We present our final results with a median follow-up of surviving patients of 7 years. The response rate to this low-dose irradiation was 83%, with an overall response rate to radiochemotherapy of 96% and a median survival of 21 months. No unexpected early or late toxicity was observed. The rate of initial isolated local failure was 8%, which compares favorably with other published series using higher doses of radiochemotherapy. CONCLUSION: An initial chest irradiation of 20 Gy before chemotherapy could be sufficient to reduce the risk of local failure during the time of survival of patients with limited-stage SCLC. Potential advantages of this treatment may be the prevention of resistance mechanisms to radiotherapy induced by preliminary chemotherapy and a reduced radiation-induced toxicity.

Integration of local-scale hydrological and regional-scale geophysical based on a nonlinear Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed a downscaling procedure based on a non-linear Bayesian sequential simulation approach. The basic objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity, which is available throughout the model space. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariate kernel density function. This method is then applied to the stochastic integration of low-resolution, re- gional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this downscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the downscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Methods for Determining Frequency- and Region-Dependent Relationships Between Estimated LFPs and BOLD Responses in Humans

The relationship between electrophysiological and functional magnetic resonance imaging (fMRI) signals remains poorly understood. To date, studies have required invasive methods and have been limited to single functional regions and thus cannot account for possible variations across brain regions. Here we present a method that uses fMRI data and singe-trial electroencephalography (EEG) analyses to assess the spatial and spectral dependencies between the blood-oxygenation-level-dependent (BOLD) responses and the noninvasively estimated local field potentials (eLFPs) over a wide range of frequencies (0-256 Hz) throughout the entire brain volume. This method was applied in a study where human subjects completed separate fMRI and EEG sessions while performing a passive visual task. Intracranial LFPs were estimated from the scalp-recorded data using the ELECTRA source model. We compared statistical images from BOLD signals with statistical images of each frequency of the eLFPs. In agreement with previous studies in animals, we found a significant correspondence between LFP and BOLD statistical images in the gamma band (44-78 Hz) within primary visual cortices. In addition, significant correspondence was observed at low frequencies (<14 Hz) and also at very high frequencies (>100 Hz). Effects within extrastriate visual areas showed a different correspondence that not only included those frequency ranges observed in primary cortices but also additional frequencies. Results therefore suggest that the relationship between electrophysiological and hemodynamic signals thus might vary both as a function of frequency and anatomical region.

Rapid detection of Staphylococcus aureus strains with reduced susceptibility to vancomycin by isothermal microcalorimetry.

Methicillin-resistant Staphylococcus aureus (MRSA) usually harbors a vancomycin-susceptible phenotype (VSSA) but can exhibit reduced vancomycin susceptibility phenotypes that can be heterogeneous-intermediate (hVISA), intermediate (VISA), or fully resistant (VRSA). Current detection techniques (e.g., Etest and population analysis profiles [PAPs]) are slow and time-consuming. We investigated the potential of microcalorimetry to detect reduced susceptibilities to vancomycin in MRSA strains. Representative MSSA, VSSA, hVISA, VISA, and VRSA reference strains, as well as clinical isolates, were used. PAPs were performed by standard methods. Microcalorimetry was performed by inoculating 5 × 10(7) CFU of overnight cultures into 3-ml vials of brain heart infusion broth supplemented with increasing concentrations of vancomycin, and growth-related heat production was measured at 37°C. For the reference strains, no heat production was detected in the VSSA isolates at vancomycin concentrations of >3 μg/ml during the 72 h of incubation. The hVISA and VISA strains showed heat production with concentration-proportional delays of up to 6 μg/ml in 48 h and up to 12 μg/ml in 72 h, respectively. The VRSA strain showed heat production at concentrations up to 16 μg/ml in 12 h. The testing of clinical strains indicated an excellent negative predictive value, allowing us to rule out a decreased vancomycin susceptibility phenotype in <8 h of incubation. Sequential isolates from a patient undergoing vancomycin therapy showed evolving microcalorimetric profiles up to a VISA phenotype. Microcalorimetry was able to detect strains with reduced susceptibilities to vancomycin in <8 h. The measurement of bacterial heat production might represent a simple and rapid method for the detection of reduced susceptibilities to vancomycin in MRSA strains.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Speciation of naturally-accumulated uranium in an organic-rich soil of an alpine region (Switzerland)

Very high concentrations of uranium (up to 4000 ppm) were found in a natural soil in the Dischma valley, an alpine region in the Grisons canton in Switzerland. The goal of this study was to examine the redox state and the nature of uranium binding in the soil matrix in order to understand the accumulation mechanism. Pore water profiles collected from Dischma soil revealed the establishment of anoxic conditions with increasing soil depth. A combination of chemical extraction methods and spectroscopy was applied to characterize the redox state and binding environment of uranium in the soil. Bicarbonate extraction under anoxic conditions released most of the uranium indicating that uranium occurs predominantly in the hexavalent form. Surprisingly, the uranium redox state did not vary greatly as a function of depth. X-ray absorption near edge spectroscopy (XANES), confirmed that uranium was present as a mixture of U(VI) and U(IV) with U(VI) dominating. Sequential extractions of soil samples showed that the dissolution of solid organic matter resulted in the simultaneous release of the majority of the soil uranium content (>95%). Extended X-ray absorption fine structure (EXAFS) spectroscopy also revealed that soil-associated uranium in the soil matrix was mainly octahedrally coordinated, with an average of 1.7 axial (at about 1.76 Å) and 4.6 to 5.3 equatorial oxygen atoms (at about 2.36 Å) indicating the dominance of a uranyl-like (UO22+) structure presumably mixed with some U(IV). An additional EXAFS signal (at about 3.2 Å) identified in some spectra suggested that uranium was also bound (via an oxygen atom) to a light element such as carbon, phosphorus or silicon. Gamma spectrometric measurements of soil profiles failed to identify uranium long-life daughter products in the soil which is an indication that uranium originates elsewhere and was transported to its current location by water. Finally, it was found that the release of uranium from the soil was significantly promoted at very low pH values (pH 2) and increased with increasing pH values (between pH 5 and 9).

Alternate Methods of Bridge Strengthening, HR-302, 1989

The need for upgrading a large number of understrength and obsolete bridges in the United States has been well documented in the literature. Through the performance of several Iowa DOT projects, the concept of strengthening bridges (simple and continuous spans) by post-tensioning has been developed. The purpose of this project was to investigate two additional strengthening alternatives that may be more efficient than post-tensioning in certain situations. The research program for each strengthening scheme included a literature review, laboratory testing of the strengthening scheme, and a finite-element analysis of the scheme. For clarity the two strengthening schemes are presented separately. In Part 1 of this report, the strengthening of existing steel stringers in composite steel beam concrete-deck bridges by providing partial end restraint was shown to be feasible. Part 2 of this report summarizes the research that was undertaken to strengthen the negative moment regions of continuous, composite bridges. Two schemes were investigated: post-compression of stringers and superimposed trusses within the stringers.

Sequential therapist interventions and the therapeutic alliance: a pilot study

(from the journal abstract) Scientific interest for the concept of alliance has been maintained and stimulated by repeated findings that a strong alliance is associated with facilitative treatment process and favourable treatment outcome. However, because the alliance is not in itself a therapeutic technique, these findings were unsuccessful in bringing about significant improvements in clinical practice. An essential issue in modern psychotherapeutic research concerns the relation between common factors which are known to explain great variance in empirical results and the specific therapeutic techniques which are the primary basis of clinical training and practice. This pilot study explored sequences in therapist interventions over four sessions of brief psychodynamic investigation. It aims at determining if patterns of interventions can be found during brief psychodynamic investigation and if these patterns can be associated with differences in the therapeutic alliance. Therapist interventions where coded using the Psychodynamic Intervention Rating Scale (PIRS) which enables the classification of each therapist utterance into one of 9 categories of interpretive interventions (defence interpretation, transference interpretation), supportive interventions (question, clarification, association, reflection, supportive strategy) or interventions about the therapeutic frame (work-enhancing statement, contractual arrangement). Data analysis was done using lag sequential analysis, a statistical procedure which identifies contingent relationships in time among a large number of behaviours. The sample includes N = 20 therapist-patient dyads assigned to three groups with: (1) a high and stable alliance profile, (2) a low and stable alliance profile and (3) an improving alliance profile. Results suggest that therapists most often have one single intention when interacting with patients. Large sequences of questions, associations and clarifications were found, which indicate that if a therapist asks a question, clarifies or associates, there is a significant probability that he will continue doing so. A single theme sequence involving frame interventions was also observed. These sequences were found in all three alliance groups. One exception was found for mixed sequences of interpretations and supportive interventions. The simultaneous use of these two interventions was associated with a high or an improving alliance over the course of treatment, but not with a low and stable alliance where only single theme sequences of interpretations were found. In other words, in this last group, therapists were either supportive or interpretative, whereas with high or improving alliance, interpretations were always given along with supportive interventions. This finding provides evidence that examining therapist interpretation individually can only yield incomplete findings. How interpretations were given is important for alliance building. It also suggests that therapists should carefully dose their interpretations and be supportive when necessary in order to build a strong therapeutic alliance. And from a research point of view, to study technical interventions, we must look into dynamic variables such as dosage, the supportive quality of an intervention, and timing. (PsycINFO Database Record (c) 2005 APA, all rights reserved)