Biomedical subjects

Giovanni Soda

Publications and source records attributed to Giovanni Soda.

4 recordsLinked to original sources

PubMed2006

Determination of Cd, Cu, Pb and Zn in neoplastic kidneys and in renal tissue of fetuses, newborns and corpses.

The incidence of kidney tumors in USA and Europe (in particular, Central Europe and Italy) has been dramatically increasing since the 1970s, possibly as a consequence of ongoing environmental pollution. Environmental factors have been considered responsible for at least 80% of the incidence of neoplastic diseases. To shed some light on this issue, the amounts of Cd and Pb were measured in neoplastic tissue and adjacent normal part of kidney excised for carcinoma and compared with those in renal tissues of fetuses, newborns and subjects that died of non-neoplastic diseases. Cd and Pb were determined by Inductively Coupled Plasma Atomic Emission Spectrometry and Atomic Absorption Spectrometry with Electrothermal Atomization. Metallothionein immunoperoxidase staining technique was used to localize the accumulation of Cd and Zn in the nephrons. Content of Cd and Pb in kidneys of fetuses and newborns was extremely low. However, it was significantly increased in adjacent-normal tissues of kidneys with carcinomas, and significantly higher compared to kidneys of individuals that died of non-neoplastic diseases. In tumoral tissues of the excised kidneys, Cd content was very low, while that of Pb significantly elevated. High amounts of Cd and Pb in the adjacent-normal parts of kidneys with carcinomas are suggestive of possible, individual or synergistic, effects of these pollutants on enzymatic systems, priming an oncogenic pathway. Detection of metallothioneins, primary ligands of Cd, exclusively in the cells of proximal tubuli, i.e. wherein renal carcinoma develops in over 80% of cases, strongly supports the assumption that Cd exerts a carcinogenic effect.

Cadaver↗

PubMed2006

Font adaptive word indexing of modern printed documents.

We propose an approach for the word-level indexing of modern printed documents which are difficult to recognize using current OCR engines. By means of word-level indexing, it is possible to retrieve the position of words in a document, enabling queries involving proximity of terms. Web search engines implement this kind of indexing, allowing users to retrieve Web pages on the basis of their textual content. Nowadays, digital libraries hold collections of digitized documents that can be retrieved either by browsing the document images or relying on appropriate metadata assembled by domain experts. Word indexing tools would therefore increase the access to these collections. The proposed system is designed to index homogeneous document collections by automatically adapting to different languages and font styles without relying on OCR engines for character recognition. The approach is based on three main ideas: the use of Self Organizing Maps (SOM) to perform unsupervised character clustering, the definition of one suitable vector-based word representation whose size depends on the word aspect-ratio, and the run-time alignment of the query word with indexed words to deal with broken and touching characters. The most appropriate applications are for processing modern printed documents (17th to 19th centuries) where current OCR engines are less accurate. Our experimental analysis addresses six data sets containing documents ranging from books of the 17th century to contemporary journals.

Abstracting and Indexing↗

PubMed2005

Ambiguity resolution analysis in incremental parsing of natural language.

Incremental parsing gains its importance in natural language processing and psycholinguistics because of its cognitive plausibility. Modeling the associated cognitive data structures, and their dynamics, can lead to a better understanding of the human parser. In earlier work, we have introduced a recursive neural network (RNN) capable of performing syntactic ambiguity resolution in incremental parsing. In this paper, we report a systematic analysis of the behavior of the network that allows us to gain important insights about the kind of information that is exploited to resolve different forms of ambiguity. In attachment ambiguities, in which a new phrase can be attached at more than one point in the syntactic left context, we found that learning from examples allows us to predict the location of the attachment point with high accuracy, while the discrimination amongst alternative syntactic structures with the same attachment point is slightly better than making a decision purely based on frequencies. We also introduce several new ideas to enhance the architectural design, obtaining significant improvements of prediction accuracy, up to 25% error reduction on the same dataset used in previous work. Finally, we report large scale experiments on the entire Wall Street Journal section of the Penn Treebank. The best prediction accuracy of the model on this large dataset is 87.6%, a relative error reduction larger than 50% compared to previous results.

Algorithms↗

PubMed2005

Artificial neural networks for document analysis and recognition.

Artificial neural networks have been extensively applied to document analysis and recognition. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document processing tasks, like preprocessing, layout analysis, character segmentation, word recognition, and signature verification, have been effectively faced with very promising results. This paper surveys the most significant problems in the area of offline document image processing, where connectionist-based approaches have been applied. Similarities and differences between approaches belonging to different categories are discussed. A particular emphasis is given on the crucial role of prior knowledge for the conception of both appropriate architectures and learning algorithms. Finally, the paper provides a critical analysis on the reviewed approaches and depicts the most promising research guidelines in the field. In particular, a second generation of connectionist-based models are foreseen which are based on appropriate graphical representations of the learning environment.

Algorithms↗