Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Comparing potentially avoidable hospitalization rates related to ambulatory care sensitive conditions in Switzerland: the need to refine the definition of health conditions and to adjust for population health status.

BACKGROUND: Regional rates of hospitalization for ambulatory care sensitive conditions (ACSC) are used to compare the availability and quality of ambulatory care but the risk adjustment for population health status is often minimal. The objectives of the study was to examine the impact of more extensive risk adjustment on regional comparisons and to investigate the relationship between various area-level factors and the properly adjusted rates. METHODS: Our study is an observational study based on routine data of 2 million anonymous insured in 26 Swiss cantons followed over one or two years. A binomial negative regression was modeled with increasingly detailed information on health status (age and gender only, inpatient diagnoses, outpatient conditions inferred from dispensed drugs and frequency of physician visits). Hospitalizations for ACSC were identified from principal diagnoses detecting 19 conditions, with an updated list of ICD-10 diagnostic codes. Co-morbidities and surgical procedures were used as exclusion criteria to improve the specificity of the detection of potentially avoidable hospitalizations. The impact of the adjustment approaches was measured by changes in the standardized ratios calculated with and without other data besides age and gender. RESULTS: 25% of cases identified by inpatient main diagnoses were removed by applying exclusion criteria. Cantonal ACSC hospitalizations rates varied from to 1.4 to 8.9 per 1,000 insured, per year. Morbidity inferred from diagnoses and drugs dramatically increased the predictive performance, the greatest effect found for conditions linked to an ACSC. More visits were associated with fewer PAH although very high users were at greater risk and subjects who had not consulted at negligible risk. By maximizing health status adjustment, two thirds of the cantons changed their adjusted ratio by more than 10 percent. Cantonal variations remained substantial but unexplained by supply or demand. CONCLUSION: Additional adjustment for health status is required when using ACSC to monitor ambulatory care. Drug-inferred morbidities are a promising approach.

Three essays on the determinants of output, inflation and interest rates

Résumé: Output, inflation and interest rates are key macroeconomic variables, in particular for monetary policy. In modern macroeconomic models they are driven by random shocks which feed through the economy in various ways. Models differ in the nature of shocks and their transmission mechanisms. This is the common theme underlying the three essays of this thesis. Each essay takes a different perspective on the subject: First, the thesis shows empirically how different shocks lead to different behavior of interest rates over the business cycle. For commonly analyzed shocks (technology and monetary policy errors), the patterns square with standard models. The big unknown are sources of inflation persistence. Then the thesis presents a theory of monetary policy, when the central bank can better observe structural shocks than the public. The public will then seek to infer the bank's extra knowledge from its policy actions and expectation management becomes a key factor of optimal policy. In a simple New Keynesian model, monetary policy becomes more concerned with inflation persistence than otherwise. Finally, the thesis points to the huge uncertainties involved in estimating the responses to structural shocks with permanent effects.

Heat shock response in yeast involves changes in both transcription rates and mRNA stabilities

We have analyzed the heat stress response in the yeast Saccharomyces cerevisiae by determining mRNA levels and transcription rates for the whole transcriptome after a shift from 25uC to 37uC. Using an established mathematical algorithm, theoretical mRNA decay rates have also been calculated from the experimental data. We have verified the mathematical predictions for selected genes by determining their mRNA decay rates at different times during heat stress response using the regulatable tetO promoter. This study indicates that the yeast response to heat shock is not only due to changes in transcription rates, but also to changes in the mRNA stabilities. mRNA stability is affected in 62% of the yeast genes and it is particularly important in shaping the mRNA profile of the genes belonging to the environmental stress response. In most cases, changes in transcription rates and mRNA stabilities are homodirectional for both parameters, although some interesting cases of antagonist behavior are found. The statistical analysis of gene targets and sequence motifs within the clusters of genes with similar behaviors shows that both transcriptional and post-transcriptional regulons apparently contribute to the general heat stress response by means of transcriptional factors and RNA binding proteins.

Technical Note: Dissolved organic matter fluorescence a finite mixture approach to deconvolve excitation-emission matrices

The analysis of the shape of excitation-emission matrices (EEMs) is a relevant tool for exploring the origin, transport and fate of dissolved organic matter (DOM) in aquatic ecosystems. Within this context, the decomposition of EEMs is acquiring a notable relevance. A simple mathematical algorithm that automatically deconvolves individual EEMs is described, creating new possibilities for the comparison of DOM fluorescence properties and EEMs that are very different from each other. A mixture model approach is adopted to decompose complex surfaces into sub-peaks. The laplacian operator and the Nelder-Mead optimisation algorithm are implemented to individuate and automatically locate potential peaks in the EEM landscape. The EEMs of a simple artificial mixture of fluorophores and DOM samples collected in a Mediterranean river are used to describe the model application and to illustrate a strategy that optimises the search for the optimal output.

Use of GRADE for assessment of evidence about prognosis : rating confidence in estimates of event rates in broad categories of patients.

Main concepts : The Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) approach defines quality of evidence as confidence in effect estimates; this conceptualization can readily be applied to bodies of evidence estimating the risk of future of events (that is, prognosis) in broadly defined populations In the field of prognosis, a body of observational evidence (including single arms of randomized controlled trials) begins as high quality evidence. The five domains GRADE considers in rating down confidence in estimates of treatment effect-that is, risk of bias, imprecision, inconsistency, indirectness, and publication bias-as well as the GRADE criteria for rating up quality, also apply to estimates of the risk of future of events from a body of prognostic studies Applying these concepts to systematic reviews of prognostic studies provides a ful approach to determine confidence in estimates of overall prognosis in broad populations.

Growth Models with Exogenous Saving Rates, Unemployment and Wage Inertia

[cat] El propòsit d'aquest article és introduir una mercat de treball no competitiu i atur en el model de creixement amb taxes d'estalvi exògenes que es pot trobar en els llibres de text de creixement (Sala‐i‐Martín, 2000; Barro and Sala‐i‐Martín, 2003; Romer, 2006). Primer, derivem un marc general amb una funció de producció neoclàssica per analitzar la relació entre creixement i ocupació. Utilitzem aquest marc per estudiar les dinàmiques conjuntes del creixement i l'ocupació sota diferents regles de fixació salarial.

Growth Models with Exogenous Saving Rates, Unemployment and Wage Inertia

[cat] El propòsit d'aquest article és introduir una mercat de treball no competitiu i atur en el model de creixement amb taxes d'estalvi exògenes que es pot trobar en els llibres de text de creixement (Sala‐i‐Martín, 2000; Barro and Sala‐i‐Martín, 2003; Romer, 2006). Primer, derivem un marc general amb una funció de producció neoclàssica per analitzar la relació entre creixement i ocupació. Utilitzem aquest marc per estudiar les dinàmiques conjuntes del creixement i l'ocupació sota diferents regles de fixació salarial.

Identification of Rare High-Avidity, Tumor-Reactive CD8+ T Cells by Monomeric TCR-Ligand Off-Rates Measurements on Living Cells.

The avidity of the T-cell receptor (TCR) for antigenic peptides presented by the peptide-MHC (pMHC) on cells is a key parameter for cell-mediated immunity. Yet a fundamental feature of most tumor antigen-specific CD8(+) T cells is that this avidity is low. In this study, we addressed the need to identify and select tumor-specific CD8(+) T cells of highest avidity, which are of the greatest interest for adoptive cell therapy in patients with cancer. To identify these rare cells, we developed a peptide-MHC multimer technology, which uses reversible Ni(2+)-nitrilotriacetic acid histidine tags (NTAmers). NTAmers are highly stable but upon imidazole addition, they decay rapidly to pMHC monomers, allowing flow-cytometric-based measurements of monomeric TCR-pMHC dissociation rates of living CD8(+) T cells on a wide avidity spectrum. We documented strong correlations between NTAmer kinetic results and those obtained by surface plasmon resonance. Using NTAmers that were deficient for CD8 binding to pMHC, we found that CD8 itself stabilized the TCR-pMHC complex, prolonging the dissociation half-life several fold. Notably, our NTAmer technology accurately predicted the function of large panels of tumor-specific T cells that were isolated prospectively from patients with cancer. Overall, our results demonstrated that NTAmers are effective tools to isolate rare high-avidity cytotoxic T cells from patients for use in adoptive therapies for cancer treatment.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Annonaceae substitution rates: a codon model perspective

The Annonaceae includes cultivated species of economic interest and represents an important source of information for better understanding the evolution of tropical rainforests. In phylogenetic analyses of DNA sequence data that are used to address evolutionary questions, it is imperative to use appropriate statistical models. Annonaceae are cases in point: Two sister clades, the subfamilies Annonoideae and Malmeoideae, contain the majority of Annonaceae species diversity. The Annonoideae generally show a greater degree of sequence divergence compared to the Malmeoideae, resulting in stark differences in branch lengths in phylogenetic trees. Uncertainty in how to interpret and analyse these differences has led to inconsistent results when estimating the ages of clades in Annonaceae using molecular dating techniques. We ask whether these differences may be attributed to inappropriate modelling assumptions in the phylogenetic analyses. Specifically, we test for (clade-specific) differences in rates of non-synonymous and synonymous substitutions. A high ratio of nonsynonymous to synonymous substitutions may lead to similarity of DNA sequences due to convergence instead of common ancestry, and as a result confound phylogenetic analyses. We use a dataset of three chloroplast genes (rbcL, matK, ndhF) for 129 species representative of the family. We find that differences in branch lengths between major clades are not attributable to different rates of non-synonymous and synonymous substitutions. The differences in evolutionary rate between the major clades of Annonaceae pose a challenge for current molecular dating techniques that should be seen as a warning for the interpretation of such results in other organisms.

Nuclear expansion with excitation

The expansion of an isolated hot spherical nucleus with excitation energy and its caloric curve are studied in a thermodynamic model with the SkM∗ force as the nuclear effective two-body inter-action. The calculated results are shown to compare well with the recent experimental data from energetic nuclear collisions. The fluctuations in temperature and density are also studied. They are seen to build up very rapidly beyond an excitation energy of ∼9 MeV/u. Volume-conserving quadrupole deformation in addition to expansion indicates , however, nuclear disassembly above an excitation energy of ∼4 MeV/u.