The University of Maine School of Law: An Archival History of Its Founding and Accreditation

When University Trustees appointed Edward S. Godfrey Dean of the new University of Maine School of Law on January 16, 1962, they did so with the expectation that he would lead the school to accreditation by the American Bar Association (ABA) as quickly as possible. Dean Godfrey proceeded with the complete support of University of Maine President Lloyd H. Elliott and senior administrative officials. By the spring of 1962 the new Dean had begun to staff and equip the school. His efforts were well-rewarded. The ABA House of Delegates provisionally approved the School of Law in February 1964. The speed with which the Maine Law School achieved full approval, four years from inception to accreditation, was highly unusual for the time. This Comment is a tribute to Dean Godfrey and to his work in helping to make the University of Maine School of Law a valued influence on the law. Edward Godfrey accepted the deanery of the School with an ambition to make it a place of learning that would contribute to the common good by providing quality legal education at an affordable cost and would emphasize the use of the law in the honorable and effective service of society. This Comment details the history of the founding of the Maine Law School and Dean Godfrey\u27s role in guiding the School to rapid ABA accreditation

BRG1 directly regulates nucleosome structure and chromatin looping of the α globin locus to activate transcription

α globin expression must be regulated properly to prevent the occurrence of α-thalassemias, yet many questions remain unanswered regarding the mechanism of transcriptional activation. Identifying factors that regulate chromatin structure of the endogenous α globin locus in developing erythroblasts will provide important mechanistic insight. Here, we demonstrate that the BRG1 catalytic subunit of SWI/SNF-related complexes co-immunoprecipitates with GATA-1 and EKLF in murine fetal liver cells in vivo and is recruited to the far-upstream major-regulatory element (MRE) and α2 promoter. Furthermore, based on our analysis of Brg1null/ENU1 mutant mice, BRG1 regulates DNase I sensitivity, H3ac, and H3K4me2 but not CpG methylation at both sites. Most importantly, BRG1 is required for chromatin loop formation between the MRE and α2 promoter and for maximal RNA Polymerase II occupancy at the α2 promoter. Consequently, Brg1 mutants express α globin mRNA at only 5–10% of wild-type levels and die at mid-gestation. These data identify BRG1 as a chromatin-modifying factor required for nucleosome remodeling and transcriptional activation of the α globin locus. These data also demonstrate that chromatin looping between the MRE and α2 promoter is required as part of the transcriptional activation mechanism

The University of Maine School of Law: An Archival History of Its Founding and Accreditation

When University Trustees appointed Edward S. Godfrey Dean of the new University of Maine School of Law on January 16, 1962, they did so with the expectation that he would lead the school to accreditation by the American Bar Association (ABA) as quickly as possible. Dean Godfrey proceeded with the complete support of University of Maine President Lloyd H. Elliott and senior administrative officials. By the spring of 1962 the new Dean had begun to staff and equip the school. His efforts were well-rewarded. The ABA House of Delegates provisionally approved the School of Law in February 1964. The speed with which the Maine Law School achieved full approval, four years from inception to accreditation, was highly unusual for the time. This Comment is a tribute to Dean Godfrey and to his work in helping to make the University of Maine School of Law a valued influence on the law. Edward Godfrey accepted the deanery of the School with an ambition to make it a place of learning that would contribute to the common good by providing quality legal education at an affordable cost and would emphasize the use of the law in the honorable and effective service of society. This Comment details the history of the founding of the Maine Law School and Dean Godfrey\u27s role in guiding the School to rapid ABA accreditation

Periodic Lamellipodial Contractions Correlate with Rearward Actin Waves

AbstractCellular lamellipodia bind to the matrix and probe its rigidity through forces generated by rearward F-actin transport. Cells respond to matrix rigidity by moving toward more rigid matrices using an unknown mechanism. In spreading and migrating cells we find local periodic contractions of lamellipodia that depend on matrix rigidity, fibronectin binding and myosin light chain kinase (MLCK). These contractions leave periodic rows of matrix bound β3-integrin and paxillin while generating waves of rearward moving actin bound α-actinin and MLCK. The period between contractions corresponds to the time for F-actin to move across the lamellipodia. Shortening lamellipodial width by activating cofilin decreased this period proportionally. Increasing lamellipodial width by Rac signaling activation increased this period. We propose that an actin bound, contraction-activated signaling complex is transported locally from the tip to the base of the lamellipodium, activating the next contraction/extension cycle

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC–AUC of 0.89 (95% CI: 0.87–0.91), corresponding to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82–85%), but only half the specificity (45–50%, p < 0.001). Our model can also detect the presence of intraretinal fluid (AUC: 0.81; 95% CI: 0.81–0.86) and subretinal fluid (AUC 0.88; 95% CI: 0.85–0.91). Using deep learning to make predictions via simple 2D images without sophisticated 3D-imaging equipment and with better than specialist performance, has broad relevance to many other applications in medical imaging