Rosette-forming glioneuronal tumor of the cerebellum in statu nascendi: an incidentally detected diminutive example indicates derivation from the internal granule cell layer

Rosette-forming glioneuronal tumor (RGNT) is a recently introduced, indolent neoplasm composed of diminutive circular aggregates of neurocytic-like cells on a noninfiltrative astrocytic background, typically located in the cerebellar midline The traded concept of RGNT being derived from site-specific periventricular precursors may be questioned in the face of extracerebellar examples as well as ones occurring in combination with other representatives of the glioneuronal family. We describe a hitherto not documented example of asymptomatic RGNT discovered during autopsy of a 74-year-old male. Located in the tuberal vermis, this lesion of 6 mm diameter consisted of several microscopic nests of what were felt to represent nascent stages of RGNT, all of them centered on the internal granular layer, and ranging from mucoid dehiscences thereof to fully evolved - if small - tumor foci. Molecular genetic analysis revealed a missense mutation in Exon 20 of the PIK3CA gene involving an A→G transition at Nucleotide 3140. On the other hand, neither codeletion of chromosomes 1p/19q nor pathogenic mutations of IDH1/2 were detected. By analogy with in situ paradigms in other organs, we propose that this tumor is likely to have arisen from the internal granular layer, rather than the plate of the 4th ventricle. A suggestive departure from the wholesale argument of "undifferentiated precursors", this finding also indirectly indicates that a subset of non-classical RGNTs - in particular extracerebellar examples, whose origin cannot be mechanistically accounted for by either of the above structures - may possibly reflect an instance of phenotypic convergence, rather than a lineage-restricted entity.

Intraoral hyperplastic Fordyce granule with destruction of bone: a case presentation.

Fordyce granules of the oral mucosa are often discovered during routine dental examinations. They are considered anatomic variations and are typically seen on the labial and buccal mucosa in adults. The present case report describes for the first time in the literature an atypical location of an enlarged Fordyce granule with local bone destruction. The diagnostic process, surgical treatment, and follow-up are presented and discussed.

Mining free-text medical records for companion animal enteric syndrome surveillance.

Large amounts of animal health care data are present in veterinary electronic medical records (EMR) and they present an opportunity for companion animal disease surveillance. Veterinary patient records are largely in free-text without clinical coding or fixed vocabulary. Text-mining, a computer and information technology application, is needed to identify cases of interest and to add structure to the otherwise unstructured data. In this study EMR's were extracted from veterinary management programs of 12 participating veterinary practices and stored in a data warehouse. Using commercially available text-mining software (WordStat™), we developed a categorization dictionary that could be used to automatically classify and extract enteric syndrome cases from the warehoused electronic medical records. The diagnostic accuracy of the text-miner for retrieving cases of enteric syndrome was measured against human reviewers who independently categorized a random sample of 2500 cases as enteric syndrome positive or negative. Compared to the reviewers, the text-miner retrieved cases with enteric signs with a sensitivity of 87.6% (95%CI, 80.4-92.9%) and a specificity of 99.3% (95%CI, 98.9-99.6%). Automatic and accurate detection of enteric syndrome cases provides an opportunity for community surveillance of enteric pathogens in companion animals.

Mining frequent bug-fix code changes

Detecting bugs as early as possible plays an important role in ensuring software quality before shipping. We argue that mining previous bug fixes can produce good knowledge about why bugs happen and how they are fixed. In this paper, we mine the change history of 717 open source projects to extract bug-fix patterns. We also manually inspect many of the bugs we found to get insights into the contexts and reasons behind those bugs. For instance, we found out that missing null checks and missing initializations are very recurrent and we believe that they can be automatically detected and fixed.

Mining the Ecosystem to Improve Type Inference For Dynamically Typed Languages

Dynamically typed languages lack information about the types of variables in the source code. Developers care about this information as it supports program comprehension. Ba- sic type inference techniques are helpful, but may yield many false positives or negatives. We propose to mine information from the software ecosys- tem on how frequently given types are inferred unambigu- ously to improve the quality of type inference for a single system. This paper presents an approach to augment existing type inference techniques by supplementing the informa- tion available in the source code of a project with data from other projects written in the same language. For all available projects, we track how often messages are sent to instance variables throughout the source code. Predictions for the type of a variable are made based on the messages sent to it. The evaluation of a proof-of-concept prototype shows that this approach works well for types that are sufficiently popular, like those from the standard librarie, and tends to create false positives for unpopular or domain specific types. The false positives are, in most cases, fairly easily identifiable. Also, the evaluation data shows a substantial increase in the number of correctly inferred types when compared to the non-augmented type inference.

Mining microsatellite markers from public expressed sequence tags databases for the study of threatened plants

Background Simple Sequence Repeats (SSRs) are widely used in population genetic studies but their classical development is costly and time-consuming. The ever-increasing available DNA datasets generated by high-throughput techniques offer an inexpensive alternative for SSRs discovery. Expressed Sequence Tags (ESTs) have been widely used as SSR source for plants of economic relevance but their application to non-model species is still modest. Methods Here, we explored the use of publicly available ESTs (GenBank at the National Center for Biotechnology Information-NCBI) for SSRs development in non-model plants, focusing on genera listed by the International Union for the Conservation of Nature (IUCN). We also search two model genera with fully annotated genomes for EST-SSRs, Arabidopsis and Oryza, and used them as controls for genome distribution analyses. Overall, we downloaded 16 031 555 sequences for 258 plant genera which were mined for SSRsand their primers with the help of QDD1. Genome distribution analyses in Oryza and Arabidopsis were done by blasting the sequences with SSR against the Oryza sativa and Arabidopsis thaliana reference genomes implemented in the Basal Local Alignment Tool (BLAST) of the NCBI website. Finally, we performed an empirical test to determine the performance of our EST-SSRs in a few individuals from four species of two eudicot genera, Trifolium and Centaurea. Results We explored a total of 14 498 726 EST sequences from the dbEST database (NCBI) in 257 plant genera from the IUCN Red List. We identify a very large number (17 102) of ready-to-test EST-SSRs in most plant genera (193) at no cost. Overall, dinucleotide and trinucleotide repeats were the prevalent types but the abundance of the various types of repeat differed between taxonomic groups. Control genomes revealed that trinucleotide repeats were mostly located in coding regions while dinucleotide repeats were largely associated with untranslated regions. Our results from the empirical test revealed considerable amplification success and transferability between congenerics. Conclusions The present work represents the first large-scale study developing SSRs by utilizing publicly accessible EST databases in threatened plants. Here we provide a very large number of ready-to-test EST-SSR (17 102) for 193 genera. The cross-species transferability suggests that the number of possible target species would be large. Since trinucleotide repeats are abundant and mainly linked to exons they might be useful in evolutionary and conservation studies. Altogether, our study highly supports the use of EST databases as an extremely affordable and fast alternative for SSR developing in threatened plants.

The open cut: Mining, transnational corporations and local populations

The rapid expansion of the mineral and metal mining sector in the past decade was accompanied by an increase in social conflicts. What are the impacts of large-scale mining operations? What are the strategies used by transnational corporations to gain access to underground resources and legitimize their activities? And how do local and indigenous communities confronted with mining react to, negotiate with and resist these activities? This book covers 13 case studies of copper, gold, uranium and other mining operations, situated in Latin America, Africa, Asia, Australia and Switzerland. With an extensive introduction to the subject and a systematic comparison across mining operations in different phases of development and social contexts, it serves as a primer and reference book for activists, students and researchers alike.

Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.