4 research outputs found
Wind Farm Layout Optimization Using Approximate Inference in Graphical Models
Wind farm layout optimization (WFLO) determines the optimal location of wind turbines within a fixed geographical area to maximize the total power capacity of the wind farm, under stochastic wind conditions and non-linear aerodynamic interferences between turbines. This thesis develops optimization approaches to fast approximate (sub-optimal) turbine layouts to aide engineers make design decisions. Building on previous work in discrete quadratic WFLO models, we recast the program as a probabilistic graphical model incorporating spatial dependencies (i.e., aerodynamic interferences, proximity constraints, and maximum number of turbines) between the variables. Turbine layouts are estimated using message passing inference (BP, TRW-S), which exploit the problem's graph-theoretic structure using decomposition and factorization. We perform an exhaustive computational study comparing TRW-S with branch-and-cut algorithms under varying wind-regime complexity and problem resolutions. We demonstrate the broad applicability of techniques we develop by solving a suite of benchmark quadratic knapsack problems, a general class of problems that arise in many settings.M.A.S
Contained Rupture of a Posterior Communicating Artery Aneurysm in a Patient With a Third Nerve Palsy
It is recommended that every patient with a new third nerve palsy undergo urgent neuroimaging (computed tomography angiography or magnetic resonance angiography) to exclude a posterior communicating artery aneurysm. Because of the novel coronavirus (COVID-19) pandemic, our institution noted a significant decline in the number of patients with aneurysmal subarachnoid hemorrhage present- ing to the hospital. We report one such example of a patient who developed new-onset severe headache and vomiting and did not seek medical attention because of COVID-19. Two months later, she was noted to have ptosis during a routine follow-up and was found to have a complete, pupil-involving third nerve palsy. Computed tomography angiography was performed and revealed an irregular bilobed saccular anerysm (7 · 9 · 5 mm) of the right posterior communicating (PComm) artery, but no acute hemorrhage was visible on CT. On MRI, immediately adjacent to the aneurysm, there was a small subacute hematoma in the right medial temporal lobe with surrounding vasogenic edema. This case had a fortunate and unique outcome as she had a contained hematoma adjacent to the ruptured PComm aneurysm and did not expe- rience severe morbidity from the subarachnoid hemorrhage nor did she rebleed in the interval in which she did not seek care. This case highlights the importance of providing neuro- ophthalmic care even during a pandemic
AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis
Abstract While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, segmentation, and classification and the utility of this pipeline for nuclear morphologic biomarker discovery. Manually-collected nucleus annotations were used to train an object detection and segmentation model for identifying nuclei, which was deployed to segment nuclei in H&E-stained slides from the BRCA, LUAD, and PRAD TCGA cohorts. Interpretable features describing the shape, size, color, and texture of each nucleus were extracted from segmented nuclei and compared to measurements of genomic instability, gene expression, and prognosis. The nuclear segmentation and classification model trained herein performed comparably to previously reported models. Features extracted from the model revealed differences sufficient to distinguish between BRCA, LUAD, and PRAD. Furthermore, cancer cell nuclear area was associated with increased aneuploidy score and homologous recombination deficiency. In BRCA, increased fibroblast nuclear area was indicative of poor progression-free and overall survival and was associated with gene expression signatures related to extracellular matrix remodeling and anti-tumor immunity. Thus, we developed a powerful pan-tissue approach for nucleus segmentation and featurization, enabling the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types