Comparative Analyses of the Bacterial Microbiota of the Human Nostril and Oropharynx

The nose and throat are important sites of pathogen colonization, yet the microbiota of both is relatively unexplored by culture-independent approaches. We examined the bacterial microbiota of the nostril and posterior wall of the oropharynx from seven healthy adults using two culture-independent methods, a 16S rRNA gene microarray (PhyloChip) and 16S rRNA gene clone libraries. While the bacterial microbiota of the oropharynx was richer than that of the nostril, the oropharyngeal microbiota varied less among participants than did nostril microbiota. A few phyla accounted for the majority of the bacteria detected at each site: Firmicutes and Actinobacteria in the nostril and Firmicutes, Proteobacteria, and Bacteroidetes in the oropharynx. Compared to culture-independent surveys of microbiota from other body sites, the microbiota of the nostril and oropharynx show distinct phylum-level distribution patterns, supporting niche-specific colonization at discrete anatomical sites. In the nostril, the distribution of Actinobacteria and Firmicutes was reminiscent of that of skin, though Proteobacteria were much less prevalent. The distribution of Firmicutes, Proteobacteria, and Bacteroidetes in the oropharynx was most similar to that in saliva, with more Proteobacteria than in the distal esophagus or mouth. While Firmicutes were prevalent at both sites, distinct families within this phylum dominated numerically in each. At both sites there was an inverse correlation between the prevalences of Firmicutes and another phylum: in the oropharynx, Firmicutes and Proteobacteria, and in the nostril, Firmicutes and Actinobacteria. In the nostril, this inverse correlation existed between the Firmicutes family Staphylococcaceae and Actinobacteria families, suggesting potential antagonism between these groups

Preparation of anti-vicinal amino alcohols: asymmetric synthesis of D-erythro-Sphinganine, (+)-spisulosine and D-ribo-phytosphingosine

Two variations of the Overman rearrangement have been developed for the highly selective synthesis of anti-vicinal amino alcohol natural products. A MOM-ether directed palladium(II)-catalyzed rearrangement of an allylic trichloroacetimidate was used as the key step for the preparation of the protein kinase C inhibitor D-erythro-sphinganine and the antitumor agent (+)-spisulosine, while the Overman rearrangement of chiral allylic trichloroacetimidates generated by asymmetric reduction of an alpha,beta-unsaturated methyl ketone allowed rapid access to both D-ribo-phytosphingosine and L-arabino-phytosphingosine

The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

Detecting change via competence model

In real world applications, interested concepts are more likely to change rather than remain stable, which is known as concept drift. This situation causes problems on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine "when" and "how" the concept changes. In this paper, we developed a competence-based empirical distance between case chunks and then proposed a change detection method based on it. As a main contribution of our work, the change detection method provides an approach to measure the distribution change of cases of an infinite domain through finite samples and requires no prior knowledge about the case distribution, which makes it more practical in real world applications. Also, different from many other change detection methods, we not only detect the change of concepts but also quantify and describe this change. © 2010 Springer-Verlag

Taiwanese Preservice Teachers’ Science, Technology, Engineering, and Mathematics Teaching Intention

This study applies the theory of planned behavior as a basis for exploring the impact of knowledge, values, subjective norms, perceived behavioral controls, and attitudes on the behavioral intention toward science, technology, engineering, and mathematics (STEM) education among Taiwanese preservice science teachers. Questionnaires (N = 139) collected information on the behavioral intention of preservice science teachers engaging in STEM education. Data were analyzed using descriptive statistics, path analysis, and analysis of variance. Results revealed that, in terms of direct effects, higher perceived behavioral control and subjective norms were associated with stronger STEM teaching intention. More positive attitude and greater knowledge were indirectly associated with higher subjective norms and perceived behavioral control, which resulted in stronger STEM teaching intention. Additionally, gender did not affect preservice teachers’ intention to adopt STEM teaching approaches. However, preservice teachers whose specialization was in different fields tended to influence their knowledge and perceived behavioral control; these issues require further investigation

Genetic Determinants of Circulating Sphingolipid Concentrations in European Populations

Sphingolipids have essential roles as structural components of cell membranes and in cell signalling, and disruption of their metabolism causes several diseases, with diverse neurological, psychiatric, and metabolic consequences. Increasingly, variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes. Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic beta-cell failure, insulin resistance, and type 2 diabetes mellitus. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, yet little is known about the major genetic variants that influence their circulating levels in the general population. We performed a genome-wide association study (GWAS) between 318,237 single-nucleotide polymorphisms (SNPs) and levels of circulating sphingomyelin (SM), dihydrosphingomyelin (Dih-SM), ceramide (Cer), and glucosylceramide (GluCer) single lipid species (33 traits); and 43 matched metabolite ratios measured in 4,400 subjects from five diverse European populations. Associated variants (32) in five genomic regions were identified with genome-wide significant corrected p-values ranging down to 9.08 x 10(-66). The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3, LASS4, SGPP1, ATP10D, and FADS1-3. Variants in 3 loci (ATP10D, FADS3, and SPTLC3) associate with MI in a series of three German MI studies. An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association (p = 10(-4) or less). Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control, and variants in these loci can be tested for a role in the development of common cardiovascular, metabolic, neurological, and psychiatric diseases

Ensemble Learning for Low-Level Hardware-Supported Malware Detection

Abstract. Recent work demonstrated hardware-based online malware detection using only low-level features. This detector is envisioned as a first line of defense that prioritizes the application of more expensive and more accurate software detectors. Critical to such a framework is the detection performance of the hardware detector. In this paper, we explore the use of both specialized detectors and ensemble learning tech-niques to improve performance of the hardware detector. The proposed detectors reduce the false positive rate by more than half compared to a single detector, while increasing the detection rate. We also contribute approximate metrics to quantify the detection overhead, and show that the proposed detectors achieve more than 11x reduction in overhead compared to a software only detector (1.87x compared to prior work), while improving detection time. Finally, we characterize the hardware complexity by extending an open core and synthesizing it on an FPGA platform, showing that the overhead is minimal.

Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined