83 research outputs found
A multi-site perfusion monitoring sub-system
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 123-125).by Myev A. Bodenhofer.Ph.D
Learning valued relations from data
Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community. To this end, current approaches typically consider datasets containing crisp relations, so that standard classification methods can be adopted. However, relations between objects like similarities and preferences are in many real-world applications often expressed in a graded manner. A general kernel-based framework for learning relations from data is introduced here. It extends existing approaches because both crisp and valued relations are considered, and it unifies existing approaches because different types of valued relations can be modeled, including symmetric and reciprocal relations. This framework establishes in this way important links between recent developments in fuzzy set theory and machine learning. Its usefulness is demonstrated on a case study in document retrieval
SalmoNet, an integrated network of ten Salmonella enterica strains reveals common and distinct pathways to host adaptation
Salmonella enterica is a prominent bacterial pathogen with implications on human and animal health. Salmonella serovars could be classified as gastro-intestinal or extra-intestinal. Genome-wide comparisons revealed that extra-intestinal strains are closer relatives of gastro-intestinal strains than to each other indicating a parallel evolution of this trait. Given the complexity of the differences, a systems-level comparison could reveal key mechanisms enabling extra-intestinal serovars to cause systemic infections. Accordingly, in this work, we introduce a unique resource, SalmoNet, which combines manual curation, high-throughput data and computational predictions to provide an integrated network for Salmonella at the metabolic, transcriptional regulatory and protein-protein interaction levels. SalmoNet provides the networks separately for five gastro-intestinal and five extra-intestinal strains. As a multi-layered, multi-strain database containing experimental data, SalmoNet is the first dedicated network resource for Salmonella. It comprehensively contains interactions between proteins encoded in Salmonella pathogenicity islands, as well as regulatory mechanisms of metabolic processes with the option to zoom-in and analyze the interactions at specific loci in more detail. Application of SalmoNet is not limited to strain comparisons as it also provides a Salmonella resource for biochemical network modeling, host-pathogen interaction studies, drug discovery, experimental validation of novel interactions, uncovering new pathological mechanisms from emergent properties and epidemiological studies. SalmoNet is available at http://salmonet.org
Contrasting patterns of evolutionary constraint and novelty revealed by comparative sperm proteomic analysis in Lepidoptera
Background: Rapid evolution is a hallmark of reproductive genetic systems and arises through the combined processes of sequence divergence, gene gain and loss, and changes in gene and protein expression. While studies aiming to disentangle the molecular ramifications of these processes are progressing, we still know little about the genetic basis of evolutionary transitions in reproductive systems. Here we conduct the first comparative analysis of sperm proteomes in Lepidoptera, a group that exhibits dichotomous spermatogenesis, in which males produce a functional fertilization-competent sperm (eupyrene) and an incompetent sperm morph lacking nuclear DNA (apyrene). Through the integrated application of evolutionary proteomics and genomics, we characterize the genomic patterns potentially associated with the origination and evolution of this unique spermatogenic process and assess the importance of genetic novelty in Lepidopteran sperm biology.
Results: Comparison of the newly characterized Monarch butterfly (Danaus plexippus) sperm proteome to those of the Carolina sphinx moth (Manduca sexta) and the fruit fly (Drosophila melanogaster) demonstrated conservation at the level of protein abundance and post-translational modification within Lepidoptera. In contrast, comparative genomic analyses across insects reveals significant divergence at two levels that differentiate the genetic architecture of sperm in Lepidoptera from other insects. First, a significant reduction in orthology among Monarch sperm genes relative to the remainder of the genome in non-Lepidopteran insect species was observed. Second, a substantial number of sperm proteins were found to be specific to Lepidoptera, in that they lack detectable homology to the genomes of more distantly related insects. Lastly, the functional importance of Lepidoptera specific sperm proteins is broadly supported by their increased abundance relative to proteins conserved across insects.
Conclusions: Our results identify a burst of genetic novelty amongst sperm proteins that may be associated with the origin of heteromorphic spermatogenesis in ancestral Lepidoptera and/or the subsequent evolution of this system. This pattern of genomic diversification is distinct from the remainder of the genome and thus suggests that this transition has had a marked impact on lepidopteran genome evolution. The identification of abundant sperm proteins unique to Lepidoptera, including proteins distinct between specific lineages, will accelerate future functional studies aiming to understand the developmental origin of dichotomous spermatogenesis and the functional diversification of the fertilization incompetent apyrene sperm morph
Identification of expression patterns in the progression of disease stages by integration of transcriptomic data
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Effective Use of Molecular Recognition in Gas Sensing: Results from Acoustic Wave and In-Situ FTIR Measurements
To probe directly the analyte/film interactions that characterize molecular recognition in gas sensors, we recorded changes to the in-situ surface vibrational spectra of specifically fictionalized surface acoustic wave (SAW) devices concurrently with analyte exposure and SAW measurement of the extent of sorption. Fourier-lmnsform infrared external- reflectance spectra (FTIR-ERS) were collected from operating 97-MH2 SAW delay lines during exposure to a range of analytes as they interacted with thin-film coatings previously shown to be selective: cyclodextrins for chiral recognition, Ni-camphorates for Lewis bases such as pyridine and organophosphonates, and phthalocyanines for aromatic compounds. In most cases where specific chemical interactions-metal coordination, "cage" compound inclusion, or z stacking-were expected, analyte dosing caused distinctive changes in the IR spectr~ together with anomalously large SAW sensor responses. In contrast, control experiments involving the physisorption of the same analytes by conventional organic polymers did not cause similar changes in the IR spectra, and the SAW responses were smaller. For a given conventional polymer, the partition coefficients (or SAW sensor signals) roughly followed the analyte fraction of saturation vapor pressure. These SAW/FTIR results support earlier conclusions derived from thickness-shear mode resonator data
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