Bath Road Master Plan, Wiscasset, Maine

The Wiscasset Bath Road Master Plan seeks to maximize development opportunities along Bath Road through the strategic coordination of traffic infrastructure improvements, land use policies and design standards while maintaining or improving the mobility and safety of U.S. Route 1. By planning for growth, Bath Road will increase safety, reduce congestion and enhance the visual character. Ultimately, this Master Plan is intended to help Wiscasset (the Town) shape a future for Bath Road and surrounding areas that reflects the needs and values of the community and preserves the Midcoast Region’s most important arterial highway. The Plan covers the areas adjacent to U.S. Route 1 from the Woolwich-Wiscasset town line to the northerly intersection of Flood Avenue and Bath Road. Bath Road Master Plan prepared for the town of Wiscasset, Maine. Report funded by Maine Department of Transportation and the town of Wiscasset. Contents include: Part I: 1. Introduction - 4. Public Outreach Part II: 5. Recommendations Part III: Appendix A - B Part IV: Appendix C -

J/Psi Propagation in Hadronic Matter

We study J/$\psi$ propagation in hot hadronic matter using a four-flavor chiral Lagrangian to model the dynamics and using QCD sum rules to model the finite size effects manifested in vertex interactions through form factors. Charmonium breakup due to scattering with light mesons is the primary impediment to continued propagation. Breakup rates introduce nontrivial temperature and momentum dependence into the J/$\psi$ spectral function.Comment: 6 Pages LaTeX, 3 postscript figures. Proceedings for Strangeness in Quark Matter 2003, Atlantic Beach, NC, March 12-17, 2003; minor corrections in version 2, to appear in J. Phys.

Convergence of invariant densities in the small-noise limit

This paper presents a systematic numerical study of the effects of noise on the invariant probability densities of dynamical systems with varying degrees of hyperbolicity. It is found that the rate of convergence of invariant densities in the small-noise limit is frequently governed by power laws. In addition, a simple heuristic is proposed and found to correctly predict the power law exponent in exponentially mixing systems. In systems which are not exponentially mixing, the heuristic provides only an upper bound on the power law exponent. As this numerical study requires the computation of invariant densities across more than 2 decades of noise amplitudes, it also provides an opportunity to discuss and compare standard numerical methods for computing invariant probability densities.Comment: 27 pages, 19 figures, revised with minor correction

Adaptive Evolutionary Clustering

In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naive estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.Comment: To appear in Data Mining and Knowledge Discovery, MATLAB toolbox available at http://tbayes.eecs.umich.edu/xukevin/affec

Genome maps across 26 human populations reveal population-specific patterns of structural variation.

Large structural variants (SVs) in the human genome are difficult to detect and study by conventional sequencing technologies. With long-range genome analysis platforms, such as optical mapping, one can identify large SVs (>2 kb) across the genome in one experiment. Analyzing optical genome maps of 154 individuals from the 26 populations sequenced in the 1000 Genomes Project, we find that phylogenetic population patterns of large SVs are similar to those of single nucleotide variations in 86% of the human genome, while ~2% of the genome has high structural complexity. We are able to characterize SVs in many intractable regions of the genome, including segmental duplications and subtelomeric, pericentromeric, and acrocentric areas. In addition, we discover ~60 Mb of non-redundant genome content missing in the reference genome sequence assembly. Our results highlight the need for a comprehensive set of alternate haplotypes from different populations to represent SV patterns in the genome

Integrated Assessment of Circulating Cell-Free MicroRNA Signatures in Plasma of Patients with Melanoma Brain Metastasis.

Primary cutaneous melanoma frequently metastasizes to distant organs including the brain. Identification of cell-free microRNAs (cfmiRs) found in the blood can be used as potential body fluid biomarkers for detecting and monitoring patients with melanoma brain metastasis (MBM). In this pilot study, we initially aimed to identify cfmiRs in the blood of MBM patients. Normal donors plasma (healthy, n = 48) and pre-operative MBM patients\u27 plasma samples (n = 36) were compared for differences in \u3e2000 microRNAs (miRs) using a next generation sequencing (NGS) probe-based assay. A 74 cfmiR signature was identified in an initial cohort of MBM plasma samples and then verified in a second cohort of MBM plasma samples (n = 24). Of these, only 58 cfmiRs were also detected in MBM tissues (n = 24). CfmiR signatures were also found in patients who have lung and breast cancer brain metastasis (n = 13) and glioblastomas (n = 36) compared to MBM plasma samples. The 74 cfmiR signature and the latter cfmiR signatures were then compared. We found a 6 cfmiR signature that was commonly upregulated in MBM plasma samples in all of the comparisons, and a 29 cfmiR signature that distinguishes MBM patients from normal donors\u27 samples. In addition, we assessed for cfmiRs in plasma (n = 20) and urine (n = 14) samples collected from metastatic melanoma patients receiving checkpoint inhibitor immunotherapy (CII). Pre- and post-treatment samples showed consistent changes in cfmiRs. Analysis of pre- and post-treatment plasma samples showed 8 differentially expressed (DE) cfmiRs that overlapped with the 35 cfmiR signature found in MBM patients. In paired pre-treatment plasma and urine samples receiving CII 8 cfmiRs overlapped. This study identified specific cfmiRs in MBM plasma samples that may potentially allow for assessment of melanoma patients developing MBM. The cfmiR signatures identified in both blood and urine may have potential utility to assess CII responses after further validation