Making Connections - Envisioning Springfield\u27s North End

This work explores a service learning strategy in the context of the senior Urban Design Studio taught in the Department of Landscape Architecture and Regional Planning at the University of Massachusetts Amherst. The primary goal of this project is to stimulate a conversation in the neighborhoods of the North End, to develop green design strategies, to improve services and businesses for residents and the employees of local businesses, and to foster cultural engagement and interaction in the North End that will enhance the vibrancy, resilience, and quality of life of this urban community. Making connections - Envisioning Springfield\u27s North End proposes improved connectivity in a physical, cultural, and social sense will be key to attaining these goals and to engaging and synergizing individuals and community groups in the North End - residents, businesses, schools, churches, employers, and employees. Six sustainable learning and planning principles have emerged from this studio: 1. Input and interaction – Visioning workshops connect campus and community 2. Community-building art - Expression of place and people 3. Healthy living - Urban agriculture and education 4. Urban greenways – Abandoned railways and urban rivers and streams 5. Green infrastructure - Green streets as networks and structural framework 6. Sustainable urban form – Mixed use and pedestrian friendly neighborhood

Multi-Glycomics Platform Approach for Cancer

Diseases as diverse as infection and cancer are known to involve changes in glycosylation. Therefore, systematic approach to monitor glycosylation based on specific glycan types are necessary for reliable biomarker discovery and better understanding of biological function implicated with glycans. In this study, we developed the method to enrich a specific class of glycans such as mannose and sialic acid and monitor the changes in cancers. Several glycans are identified as cancer specific

Detecting glycan cancer biomarkers in serum samples using MALDI FT-ICR mass spectrometry data

Motivation: The development of better tests to detect cancer in its earliest stages is one of the most sought-after goals in medicine. Especially important are minimally invasive tests that require only blood or urine samples. By profiling oligosaccharides cleaved from glycosylated proteins shed by tumor cells into the blood stream, we hope to determine glycan profiles that will help identify cancer patients using a simple blood test. The data in this article were generated using matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI FT-ICR MS). We have developed novel methods for analyzing this type of mass spectrometry data and applied it to eight datasets from three different types of cancer (breast, ovarian and prostate)

Automated Assignments of N- and O‑Site Specific Glycosylation with Extensive Glycan Heterogeneity of Glycoprotein Mixtures

Site-specific glycosylation (SSG) of glycoproteins remains a considerable challenge and limits further progress in the areas of proteomics and glycomics. Effective methods require new approaches in sample preparation, detection, and data analysis. While the field has advanced in sample preparation and detection, automated data analysis remains an important goal. A new bioinformatics approach implemented in software called GP Finder automatically distinguishes correct assignments from random matches and complements experimental techniques that are optimal for glycopeptides, including nonspecific proteolysis and high mass resolution liquid chromatography/tandem mass spectrometry (LC/MS/MS). SSG for multiple N- and O-glycosylation sites, including extensive glycan heterogeneity, was annotated for single proteins and protein mixtures with a 5% false-discovery rate, generating hundreds of nonrandom glycopeptide matches and demonstrating the proof-of-concept for a self-consistency scoring algorithm shown to be compliant with the target-decoy approach (TDA). The approach was further applied to a mixture of N-glycoproteins from unprocessed human milk and O-glycoproteins from very-low-density-lipoprotein (vLDL) particles