Interactive machine learning for soybean seed and seedling quality classification.

New computer vision solutions combined with artificial intelligence algorithms can help recognize patterns in biological images, reducing subjectivity and optimizing the analysis process. The aim of this study was to propose an approach based on interactive and traditional machine learning methods to classify soybean seeds and seedlings according to their appearance and physiological potential. In addition, we correlated the appearance of seeds to their physiological performance.

Analysis of Prognostic Factors in High-Grade Osteosarcoma of the Extremities in Children: A 15-Year Single-Institution Experience.

Background: The aim of this retrospective study was to define clinical and pathological features and prognostic factors among children and adolescents diagnosed with high-grade osteosarcoma of the extremities.

Methods: A total of 73 patients younger than 18 years diagnosed with primary osteosarcoma of the extremities between January 1998 and December 2013 were retrospectively evaluated. Prognostic factors, such as age, gender, primary tumor site, alkaline phosphatase and lactate dehydrogenase levels, metastatic disease, pathological fracture, histological response, and surgery type, were analyzed to evaluate their effects on overall survival (OS) and event-free survival (EFS).

Increased choline kinase activity in human breast carcinomas: clinical evidence for a potential novel antitumor strategy.

Choline kinase (ChoK) and its product, phosphocholine (PCho), have been implicated in human carcinogenesis. Inhibition of this enzyme has been shown to be an efficient antitumor strategy in vivo. The aim of this study was to assess the relationship between the regulation of ChoK and clinical features in patients with breast cancer.