Identification of a biomarker panel for colorectal cancer diagnosis

Background: Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries. Methods: A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60- mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables. Results: After an exhaustive process of pre-processing to ensure data quality–lost values imputation, probes quality, data smoothing and intraclass variability filtering–the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples. Conclusions: We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955).

Long Term Dynamics in CO2 Allowance Prices and Carbon Capture Investments

In this paper we analyse the behaviour of the EU market for CO2 emission allowances; specifically, we focus on the contracts maturing in the Kyoto Protocol's second period of application (2008 to 2012). We calibrate the underlying parameters for the allowance price in the long run and we also calibrate those from the Spanish wholesale electricity market. This information is then used to assess the option to install a carbon capture and storage (CCS) unit in a coal-fired power plant. We use a two-dimensional binomial lattice where costs and profits are valued and the optimal investment time is determined. In other words, we study the trigger allowance prices above which it is optimal to install the capture unit immediately. We further analyse the impact of several variables on the critical prices, among them allowance price volatility and a hypothetical government subsidy. We conclude that, at current permit prices, from a financial point of view, immediate installation does not seem justified. This need not be the case, though, if carbon market parameters change dramatically and/or a specific policy to promote these units is adopted.

Gipuzkoako Udalen aurrekontuen analisia: 2008-2012 epealdia

Jada sei urte igaro dira azkeneko hamarkadetan pairatu dugun krisialdi ekonomiko gogorrena iritsi zela eta, kimuak kimu, oraindik ere egundoko sarraskiak sorrarazten dihardu. Urakan bat bailitzan agertu zen gure artean eta guztiongan aparteko eragina izan du: gure kontzientzia ekonomikoa astintzeaz gain orain arte normaltzat ematen genituen hainbat portaera kolokan jarri ditu. Kaltetuen zerrenda luze horretan herritarrengandik gertuen dauden erakunde publikoak agertzen dira: udalak. Artikulu honetan krisiak Gipuzkoako udalen finantza-egoeretan izan duen eragina aztertuko da, eragin negatiboa pairatu zuten lehendabiziko urtetik abiatu (2008) eta aurrekontu likidatuak dauzkagun azkeneko urtera (2012) iritsi arte. Udalen egoera ekonomiko-finantzarioa zein den jakiteko baliagarriak diren, transferentzia arruntekiko menpekotasuna, funtzionamendu gastuak, aurrezki gordina, aurrezki garbia, legezko zorpetze maila, zor bizia eta gastu orokorretarako diruzaintza-gerakina adierazleen egoera eta bilakaerak aurkeztuko dira.

El tratamiento de la muerte en el álbum ilustrado infantil. Obras publicadas en castellano (1980-2008)

617 p.: il.col., graf.

Body and Grotesque as Self-Disruption in Kerstin Hensel’s Gothic East(ern) German Novel Lärchenau (2008)

[EN] By analysing the novel Lärchenau and its -to a certain point- gothic features, this article interprets the elaboration of body in this novel as a site of the expression of power, but also as an alternative language. The grotesque dimension and the representation of the bodily numbness and pain as projections of historical awareness are key elements for the interpretation of Lärchenau in the context of Post-Unification Germany.

A High Degree of LINE-1 Hypomethylation Is a Unique Feature of Early-Onset Colorectal Cancer

12 p.

Prioritization of candidate cancer genes-an aid to oncogenomic studies

The development of techniques for oncogenomic analyses such as array comparative genomic hybridization, messenger RNA expression arrays and mutational screens have come to the fore in modern cancer research. Studies utilizing these techniques are able to highlight panels of genes that are altered in cancer. However, these candidate cancer genes must then be scrutinized to reveal whether they contribute to oncogenesis or are coincidental and non-causative. We present a computational method for the prioritization of candidate (i) proto-oncogenes and (ii) tumour suppressor genes from oncogenomic experiments. We constructed computational classifiers using different combinations of sequence and functional data including sequence conservation, protein domains and interactions, and regulatory data. We found that these classifiers are able to distinguish between known cancer genes and other human genes. Furthermore, the classifiers also discriminate candidate cancer genes from a recent mutational screen from other human genes. We provide a web-based facility through which cancer biologists may access our results and we propose computational cancer gene classification as a useful method of prioritizing candidate cancer genes identified in oncogenomic studies.