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Pattern matching is a common and fundamental operation of many applications, such as expert systems (ES). With continued growth the knowledge bases of such systems will need database management systems (DBMS) support. Providing this support will require extending DBMS to meet the needs of these systems. We have developed an operator that extends the relational data model to do pattern matching with very complex stored patterns. [ABSTRACT FROM AUTHOR]

Researchers are increasingly turning to label-free MS1 intensity-based quantification strategies within HPLC–ESI–MS/MS workflows to reveal biological variation at the molecule level. Unfortunately, HPLC–ESI–MS/MS workflows using these strategies produce results with poor repeatability and reproducibility, primarily due to systematic bias and complex variability. While current global normalization strategies can mitigate systematic bias, they fail when faced with complex variability stemming from transient stochastic events during HPLC–ESI–MS/MS analysis. To address these problems, we developed a novel local normalization method, proximity-based intensity normalization (PIN), based on the analysis of compositional data. We evaluated PIN against common normalization strategies. PIN outperforms them in dramatically reducing variance and in identifying 20% more proteins with statistically significant abundance differences that other strategies missed. Our results show the PIN enables the discovery of statistically significant biological variation that otherwise is falsely reported or missed.

Whole human saliva possesses tremendous potential in clinical diagnostics, particularly for conditions within the oral cavity such as oral cancer. Although many have studied the soluble fraction of whole saliva, few have taken advantage of the diagnostic potential of the cells present in saliva, and none have taken advantage of proteomics capabilities for their study. We report on a novel proteomics method with which we characterized for the first time cells contained in whole saliva from patients diagnosed with oral squamous cell carcinoma. Our method uses three dimensions of peptide fractionation, combining the following steps: preparative IEF using free flow electrophoresis, strong cation exchange step gradient chromatography, and microcapillary reverse-phase liquid chromatography. We determined that the whole saliva samples contained enough cells, mostly exfoliated epithelial cells, providing adequate amounts of total protein for proteomics analysis. From a mixture of four oral cancer patient samples, the analysis resulted in a catalogue of over 1000 human proteins, each identified from at least two peptides, including numerous proteins with a role in oral squamous cell carcinoma signaling and tumorigenesis pathways. Additionally proteins from over 30 different bacteria were identified, some of which putatively contribute to cancer development. The combination of preparative IEF followed by strong cation exchange chromatography effectively fractionated the complex peptide mixtures despite the closely related physiochemical peptide properties of these separations (pI and solution phase charge, respectively). Furthermore compared with our two-step method combining preparative IEF and reverse-phase liquid chromatography, our three-step method identified significantly more cellular proteins while retaining higher confidence protein identification enabled by peptide pI information gained through IEF. Thus, for detecting salivary markers of oral cancer and possibly other conditions of the oral cavity, the results confirm both the potential of analyzing the cells in whole saliva and doing so with our proteomics method.

Data from agroecological experiments are typically stored in a collection of minimally documented computer files, with additional information entered into field or lab books. As a result of this fragmentation of data and lack of proper documentation, information may be lost and data manipulation is generally cumbersome, error‐prone, and hard to automate. Modern database technology has the potential to resolve these issues. Storing experiment data in a database solves the problem of fragmentation because all data are in the database; the problem of documentation is solved by making the relations between different items of information explicit during the design of the database; and the problem of manipulation is solved by the powerful query languages available with modern database management systems. As a first step in the construction of a generally applicable database for use in agroecological research, we used a formal method to design a data model that explicitly describes the types of information (‘entities’) one may want to remember about experiments and the relationships between these entities. The data model described here consists of 40 entities and 54 relationships. The entities are classified in five categories: (i) experiments, including statistical design; (ii) objects on which measurements are made; (iii) measurement protocol and equipment; (iv) measurements; and (v) field operations. We describe in detail how the information from several common types of measurements is stored using the proposed data model and conclude that the data model adequately describes the information that scientists in agroecological disciplines need to remember about their experiments.

With the goal of creating a generally applicable database for agroecological research data, we developed and described a data model. The objective of the present work was to determine the practical usefulness of a database implemented from that data model. We used a commercially available relational database management system and mapped the entities of the logical data structure to tables, entity attributes to table columns, and entity relationships to foreign keys. The attributes of the entity representing measurement values were distributed over two tables, both to reduce the size of the database and to reduce the response time of queries involving this entity. We found that loading data into the database was the most significant hurdle to its use, and so developed a set of stored procedures that function as a data input language. The input language and other methods were used to load the data from an intensively monitored, multisite, multiyear experiment into the database. The database was used to manage, explore, and analyze the data from these experiments, as well as to share the data between collaborators and with others in an effective way. We conclude that a database implemented from the previously designed data model is a practical and extremely useful tool for research.