The Rocky Hill Dinosaurs

Guidebook for field trips in Connecticut: New England Intercollegiate Geological Conference 60th annual meeting, Yale University, New Haven, Connecticut, October 25-27, 1968: Trip C-

Mineral Deposits of the Central Connecticut Pegmatite District

Guidebook for field trips in Connecticut: New England Intercollegiate Geological Conference 60th annual meeting, Yale University, New Haven, Connecticut, October 25-27, 1968: Trip F-

Guidebook for field trips in Connecticut and south central Massachusetts: New England Intercollegiate Geological Conference 74th annual meeting, University of Connecticut, Storrs Connecticut , October 2 and 3, 1982: title pages, table of contents, foreword

front matte

Map Showing the Distribution of Surficial Sediments in Fishers Island Sound, New York, Connecticut, and Rhode Island

The data presented on this map were collected as part of a State of Connecticut and U.S. Geological Survey (USGS) cooperative program intended to further understand the marine geology of Connecticut and Long Island Sound. The purpose of this cooperative program is (1) to resolve sedimentologic and oceanographic problems and data gaps in Long Island Sound, (2) to integrate these findings with terrestrial data and the Pleistocene histories of Long Island and Connecticut, and (3) to initiate investigations of offshore resources that are keyed to the better management of Long Island Sound. With this in mind, the fundamental objectives of this study were to determine the distribution of surficial sediments in Fishers Island Sound and to describe the active sedimentary processes

Guidebook for field trips in Connecticut and south central Massachusetts: New England Intercollegiate Geological Conference 74th annual meeting, University of Connecticut, Storrs Connecticut , October 2 and 3, 1982

17 trips throughout Connecticu

Disease-Associated Mutations That Alter the RNA Structural Ensemble

Genome-wide association studies (GWAS) often identify disease-associated mutations in intergenic and non-coding regions of the genome. Given the high percentage of the human genome that is transcribed, we postulate that for some observed associations the disease phenotype is caused by a structural rearrangement in a regulatory region of the RNA transcript. To identify such mutations, we have performed a genome-wide analysis of all known disease-associated Single Nucleotide Polymorphisms (SNPs) from the Human Gene Mutation Database (HGMD) that map to the untranslated regions (UTRs) of a gene. Rather than using minimum free energy approaches (e.g. mFold), we use a partition function calculation that takes into consideration the ensemble of possible RNA conformations for a given sequence. We identified in the human genome disease-associated SNPs that significantly alter the global conformation of the UTR to which they map. For six disease-states (Hyperferritinemia Cataract Syndrome, β-Thalassemia, Cartilage-Hair Hypoplasia, Retinoblastoma, Chronic Obstructive Pulmonary Disease (COPD), and Hypertension), we identified multiple SNPs in UTRs that alter the mRNA structural ensemble of the associated genes. Using a Boltzmann sampling procedure for sub-optimal RNA structures, we are able to characterize and visualize the nature of the conformational changes induced by the disease-associated mutations in the structural ensemble. We observe in several cases (specifically the 5′ UTRs of FTL and RB1) SNP–induced conformational changes analogous to those observed in bacterial regulatory Riboswitches when specific ligands bind. We propose that the UTR and SNP combinations we identify constitute a “RiboSNitch,” that is a regulatory RNA in which a specific SNP has a structural consequence that results in a disease phenotype. Our SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble

Deciphering the universe of RNA structures and trans RNA-RNA interactions of transcriptomes in vivo: from experimental protocols to computational analyses

The last few years have seen an explosion of experimental and computational methods for investigating RNA structures of entire transcriptomes in vivo. Very recent experimental protocols now also allow trans RNA–RNA interactions to be probed in a transcriptome-wide manner. All of the experimental strategies require comprehensive computational pipelines for analysing the raw data and converting it back into actual RNA structure features or trans RNA–RNA interactions. The overall performance of these methods thus strongly depends on the experimental and the computational protocols employed. In order to get the best out of both worlds, both aspects need to be optimised simultaneously. This review introduced the methods and proposes ideas how they could be further improved

Evaluation of the information content of RNA structure mapping data for secondary structure prediction

Structure mapping experiments (using probes such as dimethyl sulfate [DMS], kethoxal, and T1 and V1 RNases) are used to determine the secondary structures of RNA molecules. The process is iterative, combining the results of several probes with constrained minimum free-energy calculations to produce a model of the structure. We aim to evaluate whether particular probes provide more structural information, and specifically, how noise in the data affects the predictions. Our approach involves generating “decoy” RNA structures (using the sFold Boltzmann sampling procedure) and evaluating whether we are able to identify the correct structure from this ensemble of structures. We show that with perfect information, we are always able to identify the optimal structure for five RNAs of known structure. We then collected orthogonal structure mapping data (DMS and RNase T1 digest) under several solution conditions using our high-throughput capillary automated footprinting analysis (CAFA) technique on two group I introns of known structure. Analysis of these data reveals the error rates in the data under optimal (low salt) and suboptimal solution conditions (high MgCl2). We show that despite these errors, our computational approach is less sensitive to experimental noise than traditional constraint-based structure prediction algorithms. Finally, we propose a novel approach for visualizing the interaction of chemical and enzymatic mapping data with RNA structure. We project the data onto the first two dimensions of a multidimensional scaling of the sFold-generated decoy structures. We are able to directly visualize the structural information content of structure mapping data and reconcile multiple data sets