Tetrafurcation of the subscapular artery. Anatomical and clinical implications

Anatomic variations of axillary artery branches are commonly encountered during radiological investigation and surgical operations. Their existence can confuse interpretation of radiological results and lead to undesired complications during surgery. In this report authors describe a rare case of a subscapular arterial trunk that gave origin to thoracodorsal, circumflex scapular, posterior humeral circumflex, and lateral thoracic artery. Such a variation might cause undesired sequelae during trauma management and a variety of common flap harvesting operations including latissimus dorsi, scapular and parascapular flaps. Furthermore it presents embryological interest as it gives insight to embryologic development of axillary area

The conceptual design of SeamFrame

This project deliverable provides the underlying architecture of a concept for linking models and databases and it provides the design of SeamFrame, delivering its architecture to provide an integration framework for models and simulation algorithms, supported by procedures for data handling and spatial representation, quality control, output visualization and documentatio

Improving predictive performance on survival in dairy cattle using an ensemble learning approach

Cow survival is a complex trait that combines traits like milk production, fertility, health and environmental factors such as farm management. This complexity makes survival difficult to predict accurately. This is probably the reason why few studies attempted to address this problem and no studies are published that use ensemble methods for this purpose. We explored if we could improve prediction of cow survival to second lactation, when predicted at five different moments in a cow's life, by combining the predictions of multiple (weak) methods in an ensemble method. We tested four ensemble methods: majority voting rule, multiple logistic regression, random forest and naive Bayes. Precision, recall, balanced accuracy, area under the curve (AUC) and gains in proportion of surviving cows in a scenario where the best 50% were selected were used to evaluate the ensemble model performance. We also calculated correlations between the ensemble models and obtained McNemar's test statistics. We compared the performance of the ensemble methods against those of the individual methods. We also tested if there was a difference in performance metrics when continuous (from 0 to 1) and binary (0 or 1) prediction outcomes were used. In general, using continuous prediction output resulted in higher performance metrics than binary ones. AUCs for models ranged from 0.561 to 0.731, with generally increasing performance at moments later in life. Precision, AUC and balanced accuracy values improved significantly for the naive Bayes and multiple logistic regression ensembles in at least one data set, although performance metrics did remain low overall. The multiple logistic regression ensemble method resulted in equal or better precision, AUC, balanced accuracy and proportion of animals surviving on all datasets and was significantly different from the other ensembles in three out of five moments. The random forest ensemble method resulted in the least significant improvement over the individual methods

Affine and toric hyperplane arrangements

We extend the Billera-Ehrenborg-Readdy map between the intersection lattice and face lattice of a central hyperplane arrangement to affine and toric hyperplane arrangements. For arrangements on the torus, we also generalize Zaslavsky's fundamental results on the number of regions.Comment: 32 pages, 4 figure