Winning on Climate Change: How Philanthropy Can Spur Major Progress over the Next Decade

Over the next 10 years, major progress against climate change is entirely possible, and philanthropy has an important role to play. Through interviews with experts and building on previous work with actors in the field, this report identifies three climate philanthropy practices that will be especially important in the decade ahead.

Автоматизация планово-аналитической деятельности

Издание направлено на формирование и развитие у студентов нового типа экономического мышления, интереса и способностей к творческому решению задач, стоящих перед различными отраслями экономики на современном этапе с использованием новейших информационных технологий. Практикум предназначен для студентов специальности 1-25 01 07 "Экономика и управление на предприятии" специализаций 1-25 01 07 11 "Экономика и управление на предприятии промышленности", 1-25 01 07 20 "Экономика и управление на предприятии услуг"

Reviewer’s comment concerning ‘‘Abnormal activation of the motor cortical network in idiopathic scoliosis demonstrated by functional MRI’’ (doi:10.1007/s00586-011-1776-8) by J. Domenech et al.

Abstract not availableBrian J. C. Freema

The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa.

Incomplete surgical resection of head and neck squamous cell carcinoma (HNSCC) is the most common cause of local HNSCC recurrence. Currently, surgeons rely on preoperative imaging, direct visualization, palpation and frozen section to determine the extent of tissue resection. It has been demonstrated that optical coherence tomography (OCT), a minimally invasive, nonionizing near infrared mesoscopic imaging modality can resolve subsurface differences between normal and abnormal head and neck mucosa. Previous work has utilized two-dimensional OCT imaging which is limited to the evaluation of small regions of interest generated frame by frame. OCT technology is capable of performing rapid volumetric imaging, but the capacity and expertise to analyze this massive amount of image data is lacking. In this study, we evaluate the ability of a retrained convolutional neural network to classify three-dimensional OCT images of head and neck mucosa to differentiate normal and abnormal tissues with sensitivity and specificity of 100% and 70%, respectively. This method has the potential to serve as a real-time analytic tool in the assessment of surgical margins