Mechanisms of perceptual learning of depth discrimination in random dot stereograms.

Perceptual learning is a training induced improvement in performance. Mechanisms underlying the perceptual learning of depth discrimination in dynamic random dot stereograms were examined by assessing stereothresholds as a function of decorrelation. The inflection point of the decorrelation function was defined as the level of decorrelation corresponding to 1.4 times the threshold when decorrelation is 0%. In general, stereothresholds increased with increasing decorrelation. Following training, stereothresholds and standard errors of measurement decreased systematically for all tested decorrelation values. Post training decorrelation functions were reduced by a multiplicative constant (approximately 5), exhibiting changes in stereothresholds without changes in the inflection points. Disparity energy model simulations indicate that a post-training reduction in neuronal noise can sufficiently account for the perceptual learning effects. In two subjects, learning effects were retained over a period of six months, which may have application for training stereo deficient subjects.

Streptococcus pneumoniae detects and responds to foreign bacterial peptide fragments in its environment

Streptococcus pneumoniae is an important cause of bacterial meningitis and pneumonia but usually colonizes the human nasopharynx harmlessly. As this niche is simultaneously populated by other bacterial species, we looked for a role and pathway of communication between pneumococci and other species. This paper shows that two proteins of non-encapsulated S. pneumoniae, AliB-like ORF 1 and ORF 2, bind specifically to peptides matching other species resulting in changes in the pneumococci. AliB-like ORF 1 binds specifically peptide SETTFGRDFN, matching 50S ribosomal subunit protein L4 of Enterobacteriaceae, and facilitates upregulation of competence for genetic transformation. AliB-like ORF 2 binds specifically peptides containing sequence FPPQS, matching proteins of Prevotella species common in healthy human nasopharyngeal microbiota. We found that AliB-like ORF 2 mediates the early phase of nasopharyngeal colonization in vivo. The ability of S. pneumoniae to bind and respond to peptides of other bacterial species occupying the same host niche may play a key role in adaptation to its environment and in interspecies communication. These findings reveal a completely new concept of pneumococcal interspecies communication which may have implications for communication between other bacterial species and for future interventional therapeutics.

THEODORE, a two-step heating system for the EC/OC determination of radiocarbon (14C) in the environment

Measurements of 14C in the organic carbon (OC) and elemental carbon (EC) fractions, respectively, of fine aerosol particles bear the potential to apportion anthropogenic and biogenic emission sources. For this purpose, the system THEODORE (two-step heating system for the EC/OC determination of radiocarbon in the environment) was developed. In this device, OC and EC are transformed into carbon dioxide in a stream of oxygen at 340 and 650 �C, respectively, and reduced to filamentous carbon. This is the target material for subsequent accelerator mass spectrometry (AMS) 14C measurements, which were performed on sub-milligram carbon samples at the PSI/ETH compact 500 kV AMS system. Quality assurance measurements of SRM 1649a, Urban Dust, yielded a fraction of modern fM in total carbon (TC) of 0.522 ±0.018 (n ¼ 5, 95% confidence level) in agreement with reported values. The results for OC and EC are 0.70± 0.05 (n ¼ 3) and 0.066 ± 0.020 (n ¼ 4), respectively.

Supramolecular Polymers in DNA Nanotechnology

Creation of biocompatible functional materials is an important task in supramolecular chemistry. In this contribution, we report on noncovalent synthesis of DNA-grafted supramolecular polymers (SPs). DNA-grafted SPs enable programmed arrangement of oligonucleotides in a regular, tightly packed one-dimensional array. Further interactions of DNA-grafted SPs with complementary DNA strands leads to the formation of networks through highly cooperative G-C blunt-end stacking interactions. The structural changes in the polymeric core enable to monitor spectroscopically the stepwise formation of networks. Such stimuli-responsive supramolecular networks may lead to the development of DNA-based smart materials.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

(Table 1) Metal contents in marine green algae (Chlorophyta) with background and enhanced element concentrations in the environment

Character of metal accumulation in fractions of thalli of four species of marine green benthos algae under background and enhanced (0.3 mg/l) element concentrations in the environment was studied in short-term 24-hour experiments. Algae were shown to hold polysaccharide and protein mechanisms of metal accumulation. Variance analysis was applied to evaluate taxonomic and ecological features of metal distribution in fractions of thalli.

Packing of atermal polymers in the bulk and under confinement

Polymers constitute a distinct class of anisotropic particles with unique dynamical, rheological and mechanical properties.

CD68+ cells of monocyte/macrophage lineage in the environment of AIDS-associated and classic-sporadic Kaposi sarcoma are singly or doubly infected with human herpesviruses 7 and 6B

Earlier studies have shown that Kaposi sarcomas contain cells infected with human herpesvirus (HHV) 6B, and in current studies we report that both AIDS-associated and classic-sporadic Kaposi sarcoma contain HHV-7 genome sequences detectable by PCR. To determine the distribution of HHV-7-infected cells relative to those infected with HHV-6, sections from paraffin-embedded tissues were allowed to react with antibodies to HHV-7 virion tegument phosphoprotein pp85 and to HHV-6B protein p101. The antibodies are specific for HHV-7 and HHV-6B, respectively, and they retained reactivity for antigens contained in formalin-fixed, paraffin-embedded tissue samples. We report that (i) HHV-7 pp85 was present in 9 of 32 AIDS-associated Kaposi sarcomas, and in 1 of 7 classical-sporadic HIV-negative Kaposi sarcomas; (ii) HHV-7 pp85 was detected primarily in cells bearing the CD68 marker characteristic of the monocyte/macrophage lineage present in or surrounding the Kaposi sarcoma lesions; and (iii) in a number of Kaposi sarcoma specimens, tumor-associated CD68+ monocytes/macrophages expressed simultaneously antigens from both HHV-7 and HHV-6B, and therefore appeared to be doubly infected with the two viruses. CD68+ monocytes/macrophages infected with HHV-7 were readily detectable in Kaposi sarcoma, but virtually absent from other normal or pathological tissues that harbor macrophages. Because all of the available data indicate that HHV-7 infects CD4+ T lymphocytes, these results suggest that the environment of the Kaposi sarcoma (i) attracts circulating peripheral lymphocytes and monocytes, triggers the replication of latent viruses, and thereby increases the local concentration of viruses, (ii) renders CD68+ monocytes/macrophages susceptible to infection with HHV-7, and (iii) the combination of both events enables double infections of cells with both HHV-6B and HHV-7.

Advances in random matrix theory, zeta functions, and sphere packing

Over four hundred years ago, Sir Walter Raleigh asked his mathematical assistant to find formulas for the number of cannonballs in regularly stacked piles. These investigations aroused the curiosity of the astronomer Johannes Kepler and led to a problem that has gone centuries without a solution: why is the familiar cannonball stack the most efficient arrangement possible? Here we discuss the solution that Hales found in 1998. Almost every part of the 282-page proof relies on long computer verifications. Random matrix theory was developed by physicists to describe the spectra of complex nuclei. In particular, the statistical fluctuations of the eigenvalues (“the energy levels”) follow certain universal laws based on symmetry types. We describe these and then discuss the remarkable appearance of these laws for zeros of the Riemann zeta function (which is the generating function for prime numbers and is the last special function from the last century that is not understood today.) Explaining this phenomenon is a central problem. These topics are distinct, so we present them separately with their own introductory remarks.

Nodulating strains of Rhizobium loti arise through chromosomal symbiotic gene transfer in the environment.

Rhizobia were isolated from nodules off a stand of Lotus corniculatus established with a single inoculant strain, ICMP3153, 7 years earlier in an area devoid of naturalized Rhizobium loti. The isolates showed diversity in growth rate, Spe I fingerprint of genomic DNA, and hybridization pattern to genomic DNA probes. The 19% of isolates that grew at the same rate as strain ICMP3153 were the only isolates that had the same fingerprint as strain ICMP3153. Sequencing of part of the 16S rRNA gene of several diverse isolates confirmed that they were not derived from the inoculant strain. Nevertheless, all non-ICMP3153 strains gave EcoRI and Spe I hybridization patterns identical to ICMP3153 when hybridized to nodulation gene cosmids. Hybridization of digests generated by the very rare cutting enzyme Swa I revealed that the symbiotic DNA region (at least 105 kb) was chromosomally integrated in the strains. The results suggest that the diverse strains arose by transfer of chromosomal symbiotic genes from ICMP3153 to nonsymbiotic rhizobia in the environment.