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Computed tomography (CT) screening of lung cancer allows the detection of early tumors. The objective of our study was to verify whether initial asymptomatic lung cancers, identified by high-resolution low-dose CT (LD-CT) on a high-risk population, show genetic abnormalities that could be indicative of the early events of lung carcinogenesis. We analyzed 78 tumor samples: 21 (pilot population) from heavy smokers with asymptomatic non-screening detected early-stage lung cancers and 57 from 5203 asymptomatic heavy smoker volunteers, who underwent a LD-CT screening study. During surgical resection of the detected tumors, tissue samples were collected and short-term cultures were started for karyotype evaluation. Samples were classified according to the normal (NK) or aneuploid (AK) karyotype. The NK samples were further analyzed by the Affymetrix single-nucleotide polymorphisms (SNPs) technology. Metaphase spreads were obtained in 73.0% of the selected samples: 80.7% showed an AK. A statistically significant correlation was found between presence of vascular invasion and abnormal karyotype. A total of 10 NK samples were suitable for SNPs analysis. Subtle genomic alterations were found in eight tumors, the remaining two showing no evidence to date of chromosomal aberrations anywhere in the genome. Two common regions of amplification were identified at 5p and 8p11. Mutation analysis by direct sequencing was conducted for the K-RAS , TP53 and EGFR genes, confirming data already described for heavy smokers. We show that: (i) the majority of screening-detected tumors are aneuploid; (ii) early-stage tumors tend to harbor a less abnormal karyotype; (iii) whole genome analysis of NK tumors allows for the detection of common regions of copy number variation (such as amplifications at 5p and 8p11), highlighting genes that might be considered candidate markers of early events in lung carcinogenesis.

We report a case of a 62-year-old man affected by Pancoast's tumor who developed pneumocephalus 17 days after right upper lobectomy with en bloc resection of the first three ribs and C8-D1 branches of the brachial plexus. The patient complained of aphasia, disorientation and sphincterial release. A chest and brain-CT scan showed a right apical pneumothorax associated with a massive pneumocephalus of the ventricles and of the subarachnoidal spaces. A pneumoperitoneum was also seen. The patient was treated using pleural drainages, Trendelenburg's position and antibiotic therapy. Clinical and radiological remission was achieved after 12 days of additional hospital stay.

For decomposable score-based structure learning of Bayesian networks, existing approaches first compute a collection of candidate parent sets for each variable and then optimize over this collection by choosing one parent set for each variable without creating directed cycles while maximizing the total score. We target the task of constructing the collection of candidate parent sets when the score of choice is the Bayesian Information Criterion (BIC). We provide new non-trivial results that can be used to prune the search space of candidate parent sets of each node. We analyze how these new results relate to previous ideas in the literature both theoretically and empirically. We show in experiments with UCI data sets that gains can be significant. Since the new pruning rules are easy to implement and have low computational costs, they can be promptly integrated into all state-of-the-art methods for structure learning of Bayesian networks.

We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. We propose a novel algorithm for this task, able to scale to large domains and large treewidths. Our novel approach consistently outperforms the state of the art on data sets with up to ten thousand variables.