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.

Valoración experiencial del aprendizaje tras formación virtual.

[eng] An observable study on the behaviour in the laboratory of Pharmaceutical Technology II (pharmaceutical pilot plant), with two groups of students of 3rd course (proactive aptitude and valuation of the"knowledge to do acquired") is carried out. The variable between groups is that one has realised a training (by means of self-study of an interactive application multimedia, especially designed), without tutorship on behalf the teacher in front of the other group that carried out the same training with the same material but with the guide of the teacher in the computer lab. On the one hand it is valued if there is some effect differentiator between both types of training, by means of the accounting of the correct results obtained in a previous test to the entrance in the pilot plant and by another one the valuation that the professors made responsible for the groups of practices in situ. The results demonstrate the previous hypothesis that there must not be significant differences between the groups, beyond the inherent ones to personality of the students itself (interest and personal implication by the subject). On the other hand the test for the observation in the laboratory did not facilitate the distinction of objective differences. [spa] Se lleva a cabo un estudio observacional sobre el comportamiento de los estudiantes (aptitud pro-activa y valoración del “saber hacer adquirido”) en el laboratorio de Tecnología Farmacéutica II (planta piloto farmacéutica) de dos grupos de estudiantes de 3er curso. La variable entre los dos grupos es que uno ha realizado una formación no presencial (mediante el autoestudio de una aplicación interactiva multimedia, especialmente diseñada), sin tutoría por parte del profesor, frente al otro grupo que llevó a cabo la misma formación con el mismo material pero con la guía del profesor en el aula de informática. Por una parte se valora si hay algún efecto diferenciador entre ambos tipos de formación, mediante la contabilización de los resultados correctos obtenidos en un test previo a la entrada en la planta piloto y por otro la valoración que hicieron los profesores responsables de los grupos de prácticas in situ. Los resultados demuestran la hipótesis previa de que no debían existir diferencias significativas entre los grupos, más allá de las inherentes a la propia personalidad de los estudiantes (interés e implicación personal por el tema). Por otra parte el test para la observación en el laboratorio no facilitó la distinción de diferencias objetivas.

Cours d'étude pour l'instruction du prince de Parme, aujourd'hui S. A. R. l'infant D. Ferdinand,... ([Reprod.]) / par M. l'abbé de Condillac,...

Collection : Archives de la linguistique française ; 88

How Accurate Are Blood (or Breath) Tests for Identifying Self-Reported Heavy Drinking Among People with Alcohol Dependence?

AIMS: Managing patients with alcohol dependence includes assessment for heavy drinking, typically by asking patients. Some recommend biomarkers to detect heavy drinking but evidence of accuracy is limited. METHODS: Among people with dependence, we assessed the performance of disialo-carbohydrate-deficient transferrin (%dCDT, ≥1.7%), gamma-glutamyltransferase (GGT, ≥66 U/l), either %dCDT or GGT positive, and breath alcohol (> 0) for identifying 3 self-reported heavy drinking levels: any heavy drinking (≥4 drinks/day or >7 drinks/week for women, ≥5 drinks/day or >14 drinks/week for men), recurrent (≥5 drinks/day on ≥5 days) and persistent heavy drinking (≥5 drinks/day on ≥7 consecutive days). Subjects (n = 402) with dependence and current heavy drinking were referred to primary care and assessed 6 months later with biomarkers and validated self-reported calendar method assessment of past 30-day alcohol use. RESULTS: The self-reported prevalence of any, recurrent and persistent heavy drinking was 54, 34 and 17%. Sensitivity of %dCDT for detecting any, recurrent and persistent self-reported heavy drinking was 41, 53 and 66%. Specificity was 96, 90 and 84%, respectively. %dCDT had higher sensitivity than GGT and breath test for each alcohol use level but was not adequately sensitive to detect heavy drinking (missing 34-59% of the cases). Either %dCDT or GGT positive improved sensitivity but not to satisfactory levels, and specificity decreased. Neither a breath test nor GGT was sufficiently sensitive (both tests missed 70-80% of cases). CONCLUSIONS: Although biomarkers may provide some useful information, their sensitivity is low the incremental value over self-report in clinical settings is questionable.

Impact of orthologous melan-A peptide immunizations on the anti-self melan-A/HLA-A2 T cell cross-reactivity.

In HLA-A2 individuals, the CD8 T cell response against the differentiation Ag Melan-A is mainly directed toward the peptide Melan-A26-35. The murine Melan-A24-33 sequence encodes a peptide that is identical with the human Melan-A26-35 decamer, except for a Thr-to-Ile substitution at the penultimate position. Here, we show that the murine Melan-A24-33 is naturally processed and presented by HLA-A2 molecules. Based on these findings, we compared the CD8 T cell response to human and murine Melan-A peptide by immunizing HLA-A2 transgenic mice. Even though the magnitude of the CTL response elicited by the murine Melan-A peptide was lower than the one elicited by the human Melan-A peptide, both populations of CTL recognized the corresponding immunizing peptide with the same functional avidity. Interestingly, CTL specific for the murine Melan-A peptide were completely cross-reactive against the orthologous human peptide, whereas anti-human Melan-A CTL recognized the murine Melan-A peptide with lower avidity. Structurally, this discrepancy could be explained by the fact that Ile32 of murine Melan-A24-33 created a larger TCR contact area than Thr34 of human Melan-A26-35. These data indicate that, even if immunizations with orthologous peptides can induce strong specific T cell responses, the quality of this response against syngeneic targets might be suboptimal due to the structure of the peptide-TCR contact surface.