Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time: Implementazione e analisi di un algoritmo autoadattivo per la ricerca di frequent patterns su macchine automatiche.

Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time. Implementazione e analisi di un algoritmo autoadattivo per la ricerca di frequent patterns su macchine automatiche.

Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time: Adattamento di un algoritmo di apprendimento automatico per la caratterizzazione e la ricerca di frequent patterns su macchine automatiche

La tesi da me svolta durante questi ultimi sei mesi è stata sviluppata presso i laboratori di ricerca di IMA S.p.a.. IMA (Industria Macchine Automatiche) è una azienda italiana che naque nel 1961 a Bologna ed oggi riveste il ruolo di leader mondiale nella produzione di macchine automatiche per il packaging di medicinali. Vorrei subito mettere in luce che in tale contesto applicativo l’utilizzo di algoritmi di data-mining risulta essere ostico a causa dei due ambienti in cui mi trovo. Il primo è quello delle macchine automatiche che operano con sistemi in tempo reale dato che non presentano a pieno le risorse di cui necessitano tali algoritmi. Il secondo è relativo alla produzione di farmaci in quanto vige una normativa internazionale molto restrittiva che impone il tracciamento di tutti gli eventi trascorsi durante l’impacchettamento ma che non permette la visione al mondo esterno di questi dati sensibili. Emerge immediatamente l’interesse nell’utilizzo di tali informazioni che potrebbero far affiorare degli eventi riconducibili a un problema della macchina o a un qualche tipo di errore al fine di migliorare l’efficacia e l’efficienza dei prodotti IMA. Lo sforzo maggiore per riuscire ad ideare una strategia applicativa è stata nella comprensione ed interpretazione dei messaggi relativi agli aspetti software. Essendo i dati molti, chiusi, e le macchine con scarse risorse per poter applicare a dovere gli algoritmi di data mining ho provveduto ad adottare diversi approcci in diversi contesti applicativi: • Sistema di identificazione automatica di errore al fine di aumentare di diminuire i tempi di correzione di essi. • Modifica di un algoritmo di letteratura per la caratterizzazione della macchina. La trattazione è così strutturata: • Capitolo 1: descrive la macchina automatica IMA Adapta della quale ci sono stati forniti i vari file di log. Essendo lei l’oggetto di analisi per questo lavoro verranno anche riportati quali sono i flussi di informazioni che essa genera. • Capitolo 2: verranno riportati degli screenshoot dei dati in mio possesso al fine di, tramite un’analisi esplorativa, interpretarli e produrre una formulazione di idee/proposte applicabili agli algoritmi di Machine Learning noti in letteratura. • Capitolo 3 (identificazione di errore): in questo capitolo vengono riportati i contesti applicativi da me progettati al fine di implementare una infrastruttura che possa soddisfare il requisito, titolo di questo capitolo. • Capitolo 4 (caratterizzazione della macchina): definirò l’algoritmo utilizzato, FP-Growth, e mostrerò le modifiche effettuate al fine di poterlo impiegare all’interno di macchine automatiche rispettando i limiti stringenti di: tempo di cpu, memoria, operazioni di I/O e soprattutto la non possibilità di aver a disposizione l’intero dataset ma solamente delle sottoporzioni. Inoltre verranno generati dei DataSet per il testing di dell’algoritmo FP-Growth modificato.

Mining tissue microarray data to uncover combinations of biomarker expression patterns that improve intermediate staging and grading of clear cell renal cell cancer

PURPOSE: Tumor stage and nuclear grade are the most important prognostic parameters of clear cell renal cell carcinoma (ccRCC). The progression risk of ccRCC remains difficult to predict particularly for tumors with organ-confined stage and intermediate differentiation grade. Elucidating molecular pathways deregulated in ccRCC may point to novel prognostic parameters that facilitate planning of therapeutic approaches. EXPERIMENTAL DESIGN: Using tissue microarrays, expression patterns of 15 different proteins were evaluated in over 800 ccRCC patients to analyze pathways reported to be physiologically controlled by the tumor suppressors von Hippel-Lindau protein and phosphatase and tensin homologue (PTEN). Tumor staging and grading were improved by performing variable selection using Cox regression and a recursive bootstrap elimination scheme. RESULTS: Patients with pT2 and pT3 tumors that were p27 and CAIX positive had a better outcome than those with all remaining marker combinations. A prolonged survival among patients with intermediate grade (grade 2) correlated with both nuclear p27 and cytoplasmic PTEN expression, as well as with inactive, nonphosphorylated ribosomal protein S6. By applying graphical log-linear modeling for over 700 ccRCC for which the molecular parameters were available, only a weak conditional dependence existed between the expression of p27, PTEN, CAIX, and p-S6, suggesting that the dysregulation of several independent pathways are crucial for tumor progression. CONCLUSIONS: The use of recursive bootstrap elimination, as well as graphical log-linear modeling for comprehensive tissue microarray (TMA) data analysis allows the unraveling of complex molecular contexts and may improve predictive evaluations for patients with advanced renal cancer.

Comparison of real-time polymerase chain reaction and DNA-strip technology in microbiological evaluation of periodontitis treatment

The impact of a semiquantitative commercially available test based on DNA-strip technology (microIDent®, Hain Lifescience, Nehren, Germany) on diagnosis and treatment of severe chronic periodontitis of 25 periodontitis patients was evaluated in comparison with a quantitative in-house real-time PCR. Subgingival plaque samples were collected at baseline as well as at 3, 6, and 12 months later. After extracting DNA, Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola, and several other periodontopathogens were determined by both methods. The results obtained by DNA-strip technology were analyzed semiquantitatively and additionally quantitatively by densitometry. The results for the 4 major periodontopathogenic bacterial species correlated significantly between the 2 methods. Samples detecting a high bacterial load by one method and negative by the other were always found in less than 2% of the total samples. Both technologies showed the impact of treatment on microflora. Especially the semiquantitative DNA-strip technology clearly analyzed the different loads of periodontopathogens after therapy and is useful in microbial diagnostics for patients in dental practices.

Mining for Class-Specific Motifs in Protein Sequence Classification

Background: In protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class. Results: We present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as Wordspy. We have validated our enriched set of class-specific motifs against the functionally important motifs obtained from the NLSdb, Prosite and ELM databases. We demonstrate that this method is very generic; thus can be widely applied to detect class-specific motifs in many protein sequence classification tasks. Conclusion: The proposed scoring function and methodology is able to identify class-specific motifs using discriminative n-grams derived from the protein sequences. The implementation of amino acid substitution scores for similarity detection, and the dampening factor to normalize the unbalanced datasets have significant effect on the performance of the scoring function. Our multipronged validation tests demonstrate that this method can detect class-specific motifs from a wide variety of protein sequence classes with a potential application to detecting proteome-specific motifs of different organisms.

Genome sequence of Desulfosporosinus sp. OT, an acidophilic sulfate-reducing bacterium from copper mining waste in Norilsk, Northern Siberia

We have sequenced the genome of Desulfosporosinus sp. OT, a Gram-positive, acidophilic sulfate-reducing Firmicute isolated from copper tailing sediment in the Norilsk mining-smelting area in Northern Siberia, Russia. This represents the first sequenced genome of a Desulfosporosinus species. The genome has a size of 5.7 Mb and encodes 6,222 putative proteins.

Comparison of three strip-type tests and two laboratory methods for salivary buffering analysis

This study evaluated the correlation between three strip-type, colorimetric tests and two laboratory methods with respect to the analysis of salivary buffering. The strip-type tests were saliva-check buffer, Dentobuff strip and CRT(®) Buffer test. The laboratory methods included Ericsson's laboratory method and a monotone acid/base titration to create a reference scale for the salivary titratable acidity. Additionally, defined buffer solutions were prepared and tested to simulate the carbonate, phosphate and protein buffer systems of saliva. The correlation between the methods was analysed by the Spearman's rank test. Disagreement was detected between buffering capacity values obtained with three strip-type tests that was more pronounced in case of saliva samples with medium and low buffering capacities. All strip-type tests were able to assign the hydrogencarbonate, di-hydrogenphosphate and 0.1% protein buffer solutions to the correct buffer categories. However, at 0.6% total protein concentrations, none of the test systems worked accurately. Improvements are necessary for strip-type tests because of certain disagreement with the Ericsson's laboratory method and dependence on the protein content of saliva.

Uranium mining industry views on ICRP statement on radon

In 2009, the International Commission on Radiological Protection issued a statement on radon which stated that the dose conversion factor for radon progeny would likely double, and the calculation of risk from radon should move to a dosimetric approach, rather than the longstanding epidemiological approach. Through the World Nuclear Association, whose members represent over 90% of the world's uranium production, industry has been examining this issue with a goal of offering expertise and knowledge to assist with the practical implementation of these evolutionary changes to evaluating the risk from radon progeny. Industry supports the continuing use of the most current epidemiological data as a basis for risk calculation, but believes that further examination of these results is needed to better understand the level of conservatism in the potential epidemiological-based risk models. With regard to adoption of the dosimetric approach, industry believes that further work is needed before this is a practical option. In particular, this work should include a clear demonstration of the validation of the dosimetric model which includes how smoking is handled, the establishment of a practical measurement protocol, and the collection of relevant data for modern workplaces. Industry is actively working to address the latter two items.

Alteration of host cell phenotype by Theileria annulata and Theileria parva: mining for manipulators in the parasite genomes

The apicomplexan parasites Theileria annulata and Theileria parva cause severe lymphoproliferative disorders in cattle. Disease pathogenesis is linked to the ability of the parasite to transform the infected host cell (leukocyte) and induce uncontrolled proliferation. It is known that transformation involves parasite dependent perturbation of leukocyte signal transduction pathways that regulate apoptosis, division and gene expression, and there is evidence for the translocation of Theileria DNA binding proteins to the host cell nucleus. However, the parasite factors responsible for the inhibition of host cell apoptosis, or induction of host cell proliferation are unknown. The recent derivation of the complete genome sequence for both T. annulata and T. parva has provided a wealth of information that can be searched to identify molecules with the potential to subvert host cell regulatory pathways. This review summarizes current knowledge of the mechanisms used by Theileria parasites to transform the host cell, and highlights recent work that has mined the Theileria genomes to identify candidate manipulators of host cell phenotype.