Unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment

The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data. To solve this issue, a commonly exploited workaround is to use synthetic data for training, but deep networks show a critical performance drop when analyzing data with slightly different statistical properties with respect to the training set. In this work, we propose a novel Unsupervised Domain Adaptation (UDA) strategy to address the domain shift issue between real world and synthetic representations. An adversarial model, based on the cycle consistency framework, performs the mapping between the synthetic and real domain. The data is then fed to a MobileNet-v2 architecture that performs the semantic segmentation task. An additional couple of discriminators, working at the feature level of the MobileNet-v2, allows to better align the features of the two domain distributions and to further improve the performance. Finally, the consistency of the semantic maps is exploited. After an initial supervised training on synthetic data, the whole UDA architecture is trained end-to-end considering all its components at once. Experimental results show how the proposed strategy is able to obtain impressive performance in adapting a segmentation network trained on synthetic data to real world scenarios. The usage of the lightweight MobileNet-v2 architecture allows its deployment on devices with limited computational resources as the ones employed in autonomous vehicles

γ-Herpesvirus load as surrogate marker of early death in HIV-1 lymphoma patients submitted to high dose chemotherapy and autologous peripheral blood stem cell transplantation

Autologous stem cell transplantation (ASCT) is a feasible procedure for human immunodeficiency virus-1 (HIV-1) lymphoma patients, whose underlying disease and intrinsic HIV-1-and ASCT-associated immunodeficiency might increase the risk for \u3b3-herpesvirus load persistence and/or reactivation. We evaluated this hypothesis by investigating the levels of Epstein-Barr virus (EBV)- and Kaposi sarcoma-associated herpesvirus (KSHV)-DNA levels in the peripheral blood of 22 HIV-1-associated lymphoma patients during ASCT, highlighting their relationship with \u3b3-herpesvirus lymphoma status, immunological parameters, and clinical events. EBV-DNA was detected in the pre-treatment plasma and peripheral blood mononuclear cells (PBMCs) of 12 (median 12135 copies/mL) and 18 patients (median 417 copies/106 PBMCs), respectively; the values in the two compartments were correlated (r = 0.77, p = 0.0001). Only EBV-positive lymphomas showed detectable levels of plasma EBV-DNA. After debulking chemotherapy, plasma EBV-DNA was associated with lymphoma chemosensitivity (p = 0.03) and a significant higher mortality risk by multivariate Cox analysis adjusted for EBV-lymphoma status (HR, 10.46, 95% CI, 1.11-98.32, p = 0.04). After infusion, EBV-DNA was detectable in five EBV-positive lymphoma patients who died within six months. KSHV-DNA load was positive in only one patient, who died from primary effusion lymphoma. Fluctuations in levels of KSHV-DNA reflected the patient's therapy and evolution of his underlying lymphoma. Other \u3b3-herpesvirus-associated malignancies, such as multicentric Castleman disease and Kaposi sarcoma, or end-organ complications after salvage treatment were not found. Overall, these findings suggest a prognostic and predictive value of EBV-DNA and KSHV-DNA, the monitoring of which could be a simple, complementary tool for the management of \u3b3-herpesvirus-positive lymphomas in HIV-1 patients submitted to ASCT

Induction of MDR1 gene expression by anthracycline analogues in a human drug resistant leukaemia cell line

The effects of 4-demethoxydaunorubicin (idarubicin, IDA) and MX2, a new morpholino-anthracycline, on up-regulation of the MDR1 gene in the low-level multidrug resistant (MDR) cell line CEM/A7R were compared at similar concentrations (IC10, IC50and IC90) over a short time exposure (4 and 24 h). The chemosensitivity of each drug was determined by a 3-day cell growth inhibition assay. Compared with epirubicin (EPI), IDA and MX2 were 17- and eightfold more effective in the CEM/A7R line respectively. No cross-resistance to 5-FU was seen in the CEM/A7R line. Verapamil (5 μM) and PSC 833 (1 μM), which dramatically reversed resistance to EPI in the CEM/A7R line, had no sensitizing effect on the resistance of this line to MX2, but slightly decreased resistance to IDA. The sensitivity to 5-FU was unchanged by these modulators. The induction of MDR1 mRNA expression by IDA, MX2 and 5-FU was analysed by Northern blotting and semiquantitatively assessed by scanning Northern blots on a phosphorimager. The relative level of MDR1 expression was expressed as a ratio of MDR1 mRNA to the internal RNA control glyceraldehyde-3-phosphate dehydrogenase (GAPDH). IDA, MX2 and 5-FU differentially up-regulated MDR1 mRNA in the CEM/A7R line in a dose-dependent manner. Both IDA and MX2 induced MDR1 expression within 4 h. 5-FU up-regulated MDR1 expression only when drug exposure was prolonged to 24 h. Based on MRK 16 binding, flow cytometric analysis of P-glycoprotein (Pgp) expression paralleled the increase in MDR1 mRNA levels. For the three anthracyclines, the increase in MDR1 expression was stable in cells grown in the absence of drug for more than 3 weeks after drug treatment. The induction of MDR1 expression by 5-FU was transient, associated with a rapid decrease in the increased Pgp levels which returned to baseline 72 h after the removal of 5-FU. This study demonstrates that MDR1 expression can be induced by analogues of anthracyclies not pumped by Pgp, and that this induction appears to be stable despite a 3-week drug-free period. © 1999 Cancer Research Campaig

Neuropilin-1 Modulates p53/Caspases Axis to Promote Endothelial Cell Survival

Vascular permeability factor/vascular endothelial growth factor (VPF/VEGF), one of the crucial pro-angiogenic factors, functions as a potent inhibitor of endothelial cell (EC) apoptosis. Previous progress has been made towards delineating the VPF/VEGF survival signaling downstream of the activation of VEGFR-2. Here, we seek to define the function of NRP-1 in VPF/VEGF-induced survival signaling in EC and to elucidate the concomitant molecular signaling events that are pivotal for our understanding of the signaling of VPF/VEGF. Utilizing two different in vitro cell culture systems and an in vivo zebrafish model, we demonstrate that NRP-1 mediates VPF/VEGF-induced EC survival independent of VEGFR-2. Furthermore, we show here a novel mechanism for NRP-1-specific control of the anti-apoptotic pathway in EC through involvement of the NRP-1-interacting protein (NIP/GIPC) in the activation of PI-3K/Akt and subsequent inactivation of p53 pathways and FoxOs, as well as activation of p21. This study, by elucidating the mechanisms that govern VPF/VEGF-induced EC survival signaling via NRP-1, contributes to a better understanding of molecular mechanisms of cardiovascular development and disease and widens the possibilities for better therapeutic targets

MeMoVolc report on classification and dynamics of volcanic explosive eruptions

Classifications of volcanic eruptions were first introduced in the early twentieth century mostly based on qualitative observations of eruptive activity, and over time, they have gradually been developed to incorporate more quantitative descriptions of the eruptive products from both deposits and observations of active volcanoes. Progress in physical volcanology, and increased capability in monitoring, measuring and modelling of explosive eruptions, has highlighted shortcomings in the way we classify eruptions and triggered a debate around the need for eruption classification and the advantages and disadvantages of existing classification schemes. Here, we (i) review and assess existing classification schemes, focussing on subaerial eruptions; (ii) summarize the fundamental processes that drive and parameters that characterize explosive volcanism; (iii) identify and prioritize the main research that will improve the understanding, characterization and classification of volcanic eruptions and (iv) provide a roadmap for producing a rational and comprehensive classification scheme. In particular, classification schemes need to be objective-driven and simple enough to permit scientific exchange and promote transfer of knowledge beyond the scientific community. Schemes should be comprehensive and encompass a variety of products, eruptive styles and processes, including for example, lava flows, pyroclastic density currents, gas emissions and cinder cone or caldera formation. Open questions, processes and parameters that need to be addressed and better characterized in order to develop more comprehensive classification schemes and to advance our understanding of volcanic eruptions include conduit processes and dynamics, abrupt transitions in eruption regime, unsteadiness, eruption energy and energy balance

Edge-Aware Graph Matching Network for Part-Based Semantic Segmentation

Semantic segmentation of parts of objects is a marginally explored and challenging task in which multiple instances of objects and multiple parts within those objects must be recognized in an image. We introduce a novel approach (GMENet) for this task combining object-level context conditioning, part-level spatial relationships, and shape contour information. The first target is achieved by introducing a class-conditioning module that enforces class-level semantics when learning the part-level ones. Thus, intermediate-level features carry object-level prior to the decoding stage. To tackle part-level ambiguity and spatial relationships among parts we exploit an adjacency graph-based module that aims at matching the spatial relationships between parts in the ground truth and predicted maps. Last, we introduce an additional module to further leverage edges localization. Besides testing our framework on the already used Pascal-Part-58 and Pascal-Person-Part benchmarks, we further introduce two novel benchmarks for large-scale part parsing, i.e., a more challenging version of Pascal-Part with 108 classes and the ADE20K-Part benchmark with 544 parts. GMENet achieves state-of-the-art results in all the considered tasks and furthermore allows to improve object-level segmentation accuracy

Incremental Learning Techniques for Semantic Segmentation

Deep learning architectures exhibit a critical drop of performance due to catastrophic forgetting when they are required to incrementally learn new tasks. Contemporary incremental learning frameworks focus on image classification and object detection while in this work we formally introduce the incremental learning problem for semantic segmentation in which a pixel-wise labeling is considered. To tackle this task we propose to distill the knowledge of the previous model to retain the information about previously learned classes, whilst updating the current model to learn the new ones. We propose various approaches working both on the output logits and on intermediate features. In opposition to some recent frameworks, we do not store any image from previously learned classes and only the last model is needed to preserve high accuracy on these classes. The experimental evaluation on the Pascal VOC2012 dataset shows the effectiveness of the proposed approaches

Salt Lake City, 1950: Sheet 212

Major; State; 200 East; Second East; 1700 South; Seventeenth South; 11th South; Eleventh South; Edison; Rice; Bryan; Wood; Wilson; Wabas