Union Men\u27s Banquet of the Harvard Square Churches at the Epworth Methodist Church, Cambridge, Mass., January 30, 1940

This is one of six menus in this collection from the New York World\u27s Fair, 1939

A qualitative exploration of the barriers asylum-seeking students face when accessing higher education in the UK

Since 2015, there has been a sharp upward trajectory in the number of applications for refugee status. This raises many questions for how education will assist refugee inclusion. However, accessing education after the age of 18 has been under-researched. This project looks to add to the body of knowledge as to why those from an asylum-seeker background are underrepresented in Higher Education (HE). Three key themes emerged through focus groups and semi-structured interviews of this group's experience of attempting to access HE. Firstly, a lack of knowledge as to their rights to HE; secondly, poor and inaccurate information being circulated by gatekeepers to HE; thirdly, the timings of offers of places being uncoordinated with access to scholarships. In conclusion, this study uncovered several barriers for refugees accessing HE in the UK, some of which are systemic. It is suggested that further research is conducted from the perspective of university admissions and widening participation departments. It is recommended that there is an official, reputable source of information for those from asylum-seeking backgrounds wishing to apply for HE.Action West Londo

National lottery, national cinema : the Arts Councils and the UK Film Industry 1995-2000

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

wrmxpress GUI: A User-Friendly Interface For High-Throughput Analysis of Parasitic Worms

Color poster with text, images, and charts.As antiparasitic research moves towards high-content and high-throughput phenotypic screening, computation of large imaging datasets will play a significant role. The data generated by automated microscopy is invaluable to researchers as it is highly detailed and at an unprecedented scale. However, processing these data is a challenging task, requiring researchers to develop custom tools, use closed-source proprietary code integrated into the microscope’s software, or adapt publicly available open-source software. However, creating custom tools and working in the command line poses a barrier to entry for many researchers. With these considerations in mind, we have developed an open-source tool with a user-friendly GUI to facilitate the analysis and interpretation of high-content imaging data across diverse worm species. wrmXpress is an open-source tool designed to quickly analyze the data received from automated microscopy imaging with a special focus on parasitic worms. The new wrmXpress GUI incorporated all previously published modules, and we also introduce a new module for tracking schistosome miracidia in a high-throughput context.University of Wisconsin--Eau Claire Office of Research and Sponsored Program

Comparing the Accuracy of FTIR Imaging and QCL Technology for the Differentiation Between Chromophobe Renal Cell Carcinoma and Oncocytoma

Color poster with text, images, charts, and graphs.Renal Cell Carcinoma (RCC) is the deadliest urological cancer. Chromophobe RCC makes up approximately 5% of all renal tumors. This leads to improper filtration of the blood which causes symptoms such as blood in the urine, back or side pain, loss of appetite, weight loss, fatigue, and fever. Renal Oncocytoma is a common benign renal neoplasm but shares many characteristics with Chromophobe RCC making them difficult to distinguish from one another. Use of Fourier Transform Infrared (FTIR) Spectroscopy and Quantum Cascade Laser (QCL) technology have been shown to be accurate at identifying a neoplasm and determining the malignancy of it.University of Wisconsin--Eau Claire Office of Research and Sponsored Program

A Machine Learning Approach Towards Prediction of Pancreatic Cancer Using Gene Expression and DNA Methylation

Color poster with text, charts, and graphs.DNA methylation is a process that can affect gene accessibility and therefore gene expression. Methylation can affect genes that are associated with suppressing or contributing to tumor growth and progression. In this research, we examined the potential of developing a scalable feature selection and deep learning framework capable of processing high dimensional genomic datasets to identify methylation sites in the human genome responsible for pancreatic ductal adenocarcinoma (PDAC). The methylation data being analyzed consisted of almost 485,578 CPG markers. Feature selection using Random Forest and Anova was performed along with random undersampling. Selected markers and gene expression data was analyzed to identify enriched pathways. Genes identified in pathways were studied for tumor progression. When compared with a list of 339 genes that were identified from literature, our results returned 98 common tumor genes including “KRAS", "BRCA1", "BRCA2", "PALB2", "CDKN2A", "TP53", "SMAD4". This reveals that the stratified feature selection technique is indeed useful at identifying important features. Finally a deep learning classifier was developed for identifying patients with PDAC. Data for this research was obtained from TCGA-PAAD project. Current work is focused on analyzing gene expression data and combine outcomes with methylation dataset to get deeper insights into the disease mechanisms. PDAC, or pancreatic ductal adenocarcinoma, is the most prevalent kind of pancreatic cancer, making up over 90% of cases. Aggressive tumor growth, early metastasis, and resistance to therapy are characteristics of this highly fatal cancer. Improving diagnosis, prognosis, and treatment outcomes require a thorough understanding of the molecular pathways underlying PDAC development and progression. The proposed research outcomes could aid in the development of non-invasive diagnostic tests and personalized treatment strategies.National Institute of General Medical Sciences of the National Institutes of Health, under NDSU COBRE Award Number 1P20GM109024; NIH grant P30 CA77598; National Center for Advancing Translational Sciences of the National Institutes of Health Award Number UL1TR002494; University of Wisconsin--Eau Claire Office of Research and Sponsored Program

Deep Learning Segmentation of Kidney Tissue Microarrays Using Infrared Spectral Imaging

Color poster with text, images, charts, and graphs.Renal function is an essential marker in the classification of renal disease and clinical symptoms of renal failure develop when there is 15% renal function. In this study, we used infrared spectroscopic (IR) imaging to investigate biomolecular markers from renal transplant biopsies. These images are used for the classification of regions of fibrosis from biopsies containing renal cell carcinoma (chromophobe and oncocytoma) and the prediction of fibrotic proliferation using biochemical signatures. IR spectroscopy is a diagnostic approach utilizing human tissue to label biochemical signatures. Images are captured in several hundred wavelengths in the infrared region of the electromagnetic spectrum giving researchers access to more information than traditional RGB images captured by a microscope. While images captured in several bands are great for disease diagnosis, it poses significant challenges for manual cell review by a pathologist. Our project goals are to apply feature selection to remove data with less importance and reduce dimensionality. We also hope to apply a deep learning model on filtered dataset for identification of fibrosis.University of Wisconsin--Eau Claire Office of Research and Sponsored Program