Separation-survivability - the elusive moral cut-off point?

LetterThe original publication is available at http://www.samj.org.za[No abstract available]Publisher’s versio

Prognostic implications of mean nuclear diameter in breast cancer.

The mean nuclear diameter of 100 breast cancers was measured on tissue sections, to evaluate its importance for early prognosis. The cases were subdivided into 3 subgroups: small (25.5% of cases), medium (63.3%) and large (11.2%) nuclei. Early recurrence and mortality rates were investigated in each of the categories. Increasing nuclear size was shown to be related to mortality from metastatic disease. However, large-nucleus tumours had an inverse relationship with lymphnode involvement and possibly with recurrence rate. Hence, in our material nuclear size as a sole criterion was not a good indicator of the early behaviour of operable breast cancer

Comparative study of deep learning methods for one-shot image classification (abstract)

Training deep learning models for images classification requires large amount of labeled data to overcome the challenges of overfitting and underfitting. Usually, in many practical applications, these labeled data are not available. In an attempt to solve this problem, the one-shot learning paradigm tries to create machine learning models capable to learn well from one or (maximum) few labeled examples per class. To understand better the behavior of various deep learning models and approaches for one-shot learning, in this abstract, we perform a comparative study of the most used ones, on a challenging real-world dataset, i.e Fashion-MNIST

Mu rhythm: State of the art with special focus on cerebral palsy

Various specific early rehabilitation strategies are proposed to decrease functional disabilities in patients with cerebral palsy (CP). These strategies are thought to favour the mechanisms of brain plasticity that take place after brain injury. However, the level of evidence is low. Markers of brain plasticity would favour validation of these rehabilitation programs. In this paper, we consider the study of mu rhythm for this goal by describing the characteristics of mu rhythm in adults and children with typical development, then review the current literature on mu rhythm in CP. Mu rhythm is composed of brain oscillations recorded by electroencephalography (EEG) or magnetoencephalography (MEG) over the sensorimotor areas. The oscillations are characterized by their frequency, topography and modulation. Frequency ranges within the alpha band (∼10Hz, mu alpha) or beta band (∼20Hz, mu beta). Source location analyses suggest that mu alpha reflects somatosensory functions, whereas mu beta reflects motor functions. Event-related desynchronisation (ERD) followed by event-related (re-)synchronisation (ERS) of mu rhythm occur in association with a movement or somatosensory input. Even if the functional role of the different mu rhythm components remains incompletely understood, their maturational trajectory is well described. Increasing age from infancy to adolescence is associated with increasing ERD as well as increasing ERS. A few studies characterised mu rhythm in adolescents with spastic CP and showed atypical patterns of modulation in most of them. The most frequent findings in patients with unilateral CP are decreased ERD and decreased ERS over the central electrodes, but atypical topography may also be found. The patterns of modulations are more variable in bilateral CP. Data in infants and young children with CP are lacking and studies did not address the questions of intra-individual reliability of mu rhythm modulations in patients with CP nor their modification after motor learning. Better characterization of mu rhythm in CP, especially in infants and young children, is warranted before considering this rhythm as a potential neurophysiological marker of brain plasticity