Jodi Anderson at West Point Women's Pentathlon

General note: Title and date provided by Bettye Lane.

Bishop Horace Donegan ordains Rev. Carol Anderson, the first Episcopal woman ordained priest

General note: Title and date provided by Bettye Lane.

El discreto encanto de Wes Anderson

Este artículo hace parte de la revista Papel de colgadura de la Facultad de Derecho y Ciencias Sociales de la Universidad Icesi de Cali, es una publicación de difusión y agitación cultural. La revista nace de la pasión por la música, los libros, las ilustraciones, el graffiti, los cómics, la web, la fiesta, el cine, la cafeína y de las tardes de tertulia con empanadas y cerveza, que circula en versión impresa dos veces al año, pero su versión digital se actualiza con mayor frecuencia.

Correlations between cardiac magnetic resonance and histologic findings in Anderson Fabry disease

ABSTRACT Background Cardiac magnetic resonance (CMR) has been shown as promising diagnostic tool in Anderson-Fabry disease (AFD) cardiomyopathy due to its ability to detect fat deposits through lower native T1 values. However no histological validation has been provided to date. Objectives To correlate CMR and histologic findings in different cardiac stages of AFD focusing on T1 mapping. Methods Fifteen AFD patients (49 years [IQR 39-63], 60% females) undergoing CMR (cines, native T1 and T2 mapping, LGE and post-contrast T1 imaging) and endomyocardial biopsy (EMB, n=11) or septal myectomy (n=4), were retrospectively evaluated. Tissue specimens were analyzed with light/electron microscopy and vacuolization amount calculated as percentages of vacuolated myocytes and vacuolated myocyte area (%VMA) through a quantitative histomorphometric color-based analysis. Results In patients without increased indexed left ventricular mass (LVMi) at CMR (67%), T1 fell as %VMA increased (r= -0.883; p<0.001), whereas no clear relationship was evident once increased LVMi occurred (r= -0.501; p=0.389). At least 45% of vacuolized myocytes and 10% of VMA were needed for low T1 to occur. %VMA positively correlate with maximal wall thickness (MWT, r=0.860, p<0.0001) and LVMi (r= 0.762; p<0.001). Increased MWT and LVMi were present with at least 45% and 80% of vacuolated myocytes, respectively, and 18% and 22% of VMA. Conclusions This study demonstrated an inverse correlation between native T1 and the vacuolization amount in patients without increased LVMi at CMR, providing a histological validation of low native T1 in AFD. Importantly, a significant vacuolization burden was needed before low T1 and left ventricle hypertrophy occurred.

Il problema della Nearest Correlation Matrix: il metodo delle proiezioni alternate e l'accelerazione di Anderson

Un problema frequente nell'analisi dei dati del mondo reale è quello di lavorare con dati "non coerenti", poiché raccolti a tempi asincroni o a causa di una successiva modifica "manuale" per ragioni che esulano da quelle matematiche. In particolare l'indagine dell'elaborato nasce da motivazioni di tipo finanziario, dove strumenti semplici come le matrici di correlazione, che sono utilizzate per capire le relazioni tra vari titoli o strategie, non rispettano delle caratteristiche cruciali a causa dell'incoerenza dei dati. A partire da queste matrici "invalide" si cerca la matrice di correlazione più vicina in norma, in modo da mantenere più informazioni originali possibili. Caratterizzando la soluzione del problema tramite analisi convessa, si utilizza il metodo delle proiezioni alternate, largamente utilizzato per la sua flessibilità anche se penalizzato dalla velocità di convergenza lineare. Viene quindi proposto l'utilizzo dell'accelerazione di Anderson, una tecnica per accelerare la convergenza dei metodi di punto fisso che, applicata al metodo di proiezione alternata, porta significativi miglioramenti in termini di tempo computazionale e numero di iterazioni. Si mostra inoltre come, nel caso di varianti del problema, l'applicazione dell'accelerazione di Anderson abbia un effetto maggiore rispetto al caso del problema "classico".

Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.