Paradigms and Controversies in the Treatment of HIV-Related Burkitt Lymphoma

Burkitt lymphoma (BL) is a very aggressive subtype of non-Hodgkin's lymphoma that occurs with higher frequency in patients with HIV/AIDS. Patients with HIV-related BL (HIV-BL) are usually treated with high-intensity, multi-agent chemotherapy regimens. The addition of the monoclonal antibody Rituximab to chemotherapy has also been studied in this setting. The potential risks and benefits of commonly used regimens are reviewed herein, along with a discussion of controversial issues in the practical management of HIV-BL, including concurrent anti-retroviral therapy, treatment of relapsed and/or refractory disease, and the role of stem cell transplantation

A talin mutant that impairs talin-integrin binding in platelets decelerates αIIbβ3 activation without pathological bleeding

Tight regulation of integrin affinity is critical for hemostasis. A final step of integrin activation is talin binding to 2 sites within the integrin β cytoplasmic domain. Binding of talin to a membrane-distal NPxY sequence facilitates a second, weaker interaction of talin with an integrin membrane-proximal region (MPR) that is critical for integrin activation. To test the functional significance of these distinct interactions on platelet function in vivo, we generated knock-in mice expressing talin1 mutants with impaired capacity to interact with the β3 integrin MPR (L325R) or NPLY sequence (W359A). Both talin1(L325R) and talin1(W359A) mice were protected from experimental thrombosis. Talin1(L325R) mice, but not talin(W359A) mice, exhibited a severe bleeding phenotype. Activation of αIIbβ3 was completely blocked in talin1(L325R) platelets, whereas activation was reduced by approximately 50% in talin1(W359A) platelets. Quantitative biochemical measurements detected talin1(W359A) binding to β3 integrin, albeit with a 2.9-fold lower affinity than wild-type talin1. The rate of αIIbβ3 activation was slower in talin1(W359A) platelets, which consequently delayed aggregation under static conditions and reduced thrombus formation under physiological flow conditions. Together our data indicate that reduction of talin-β3 integrin binding affinity results in decelerated αIIbβ3 integrin activation and protection from arterial thrombosis without pathological bleeding

On the Importance of 3D Surface Information for Remote Sensing Classification Tasks

There has been a surge in remote sensing machine learning applications that operate on data from active or passive sensors as well as multi-sensor combinations (Ma et al. (2019)). Despite this surge, however, there has been relatively little study on the comparative value of 3D surface information for machine learning classification tasks. Adding 3D surface information to RGB imagery can provide crucial geometric information for semantic classes such as buildings, and can thus improve out-of-sample predictive performance. In this paper, we examine in-sample and out-of-sample classification performance of Fully Convolutional Neural Networks (FCNNs) and Support Vector Machines (SVMs) trained with and without 3D normalized digital surface model (nDSM) information. We assess classification performance using multispectral imagery from the International Society for Photogrammetry and Remote Sensing (ISPRS) 2D Semantic Labeling contest and the United States Special Operations Command (USSOCOM) Urban 3D Challenge. We find that providing RGB classifiers with additional 3D nDSM information results in little increase in in-sample classification performance, suggesting that spectral information alone may be sufficient for the given classification tasks. However, we observe that providing these RGB classifiers with additional nDSM information leads to significant gains in out-of-sample predictive performance. Specifically, we observe an average improvement in out-of-sample all-class accuracy of 14.4% on the ISPRS dataset and an average improvement in out-of-sample F1 score of 8.6% on the USSOCOM dataset. In addition, the experiments establish that nDSM information is critical in machine learning and classification settings that face training sample scarcity

Exploring new physics frontiers through numerical relativity

The demand to obtain answers to highly complex problems within strong-field gravity has been met with significant progress in the numerical solution of Einstein's equations - along with some spectacular results - in various setups. We review techniques for solving Einstein's equations in generic spacetimes, focusing on fully nonlinear evolutions but also on how to benchmark those results with perturbative approaches. The results address problems in high-energy physics, holography, mathematical physics, fundamental physics, astrophysics and cosmology