Performance analysis of wireless LANs: an integrated packet/flow level approach

In this paper we present an integrated packet/flow level modelling approach for analysing flow throughputs and transfer times in IEEE 802.11 WLANs. The packet level model captures the statistical characteristics of the transmission of individual packets at the MAC layer, while the flow level model takes into account the system dynamics due to the initiation and completion of data flow transfers. The latter model is a processor sharing type of queueing model reflecting the IEEE 802.11 MAC design principle of distributing the transmission capacity fairly among the active flows. The resulting integrated packet/flow level model is analytically tractable and yields a simple approximation for the throughput and flow transfer time. Extensive simulations show that the approximation is very accurate for a wide range of parameter settings. In addition, the simulation study confirms the attractive property following from our approximation that the expected flow transfer delay is insensitive to the flow size distribution (apart from its mean)

Potential beneficial effects of cytomegalovirus infection after transplantation

Cytomegalovirus (CMV) infection can cause significant complications after transplantation, but recent emerging data suggest that CMV may paradoxically also exert beneficial effects in two specific allogeneic transplant settings. These potential benefits have been underappreciated and are therefore highlighted in this review. First, after allogeneic hematopoietic stem cell transplantation (HSCT) for acute myeloid leukemia (AML) using T-cell and natural killer (NK) cell-replete grafts, CMV reactivation is associated with protection from leukemic relapse. This association was not observed for other hematologic malignancies. This anti-leukemic effect might be mediated by CMV-driven expansion of donor-derived memory-like NKG2C+ NK and Vδ2negγδ T-cells. Donor-derived NK cells probably recognize recipient leukemic blasts by engagement of NKG2C with HLA-E and/or by the lack of donor (self) HLA molecules. Vδ2negγδ T cells probably recognize as yet unidentified antigens on leukemic blasts via their TCR. Second, immunological imprints of CMV infection, such as expanded numbers of Vδ2negγδ T cells and terminally differentiated TCRαβ+ T cells, as well as enhanced NKG2C gene expression in peripheral blood of operationally tolerant liver transplant patients, suggest that CMV infection or reactivation may be associated with liver graft acceptance. Mechanistically, poor alloreactivity of CMV-induced terminally differentiated TCRαβ+ T cells and CMV-induced IFN-driven adaptive immune resistance mechanisms in liver grafts may be involved. In conclusion, direct associations indicate that CMV reactivation may protect against AML relapse after allogeneic HSCT, and indirect associations suggest that CMV infection may promote allograft acceptance after liver transplantation. The causative mechanisms need further investigations, but are probably related to the profound and sustained imprint of CMV infection on the immune system

Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201

Interaction of Polysialic Acid with CCL21 Regulates the Migratory Capacity of Human Dendritic Cells

Dendritic cells (DCs) are the most potent antigen-presenting cells (APCs). Immature DCs (iDCs) are situated in the periphery where they capture pathogen. Subsequently, they migrate as mature DCs (mDCs) to draining lymph nodes to activate T cells. CCR7 and CCL21 contribute to the migratory capacity of the DC, but it is not completely understood what molecular requirements are involved. Here we demonstrate that monocyte-derived DCs dramatically change ST8Sia IV expression during maturation, leading to the generation of polysialic acid (polySia). PolySia expression is highly upregulated after 2 days Toll-like receptor-4 (TLR4) triggering. Surprisingly, only immunogenic and not tolerogenic mDCs upregulated polySia expression. Furthermore, we show that polySia expression on DCs is required for CCL21-directed migration, whereby polySia directly captures CCL21. Corresponding to polySia, the expression level of CCR7 is maximal two days after TLR4 triggering. In contrast, although TLR agonists other than LPS induce upregulation of CCR7, they achieve only a moderate polySia expression. In situ we could detect polySia-expressing APCs in the T cell zone of the lymph node and in the deep dermis. Together our results indicate that prolonged TLR4 engagement is required for the generation of polySia-expressing DCs that facilitate CCL21 capture and subsequent CCL21-directed migration

A low-cost HPV immunochromatographic assay to detect high-grade cervical intraepithelial neoplasia

Objective To evaluate the reproducibility and accuracy of the HPV16/18-E6 test. Methods The study population was comprised of 448 women with a previously abnormal Pap who were referred to the Barretos Cancer Hospital (Brazil) for diagnosis and treatment. Two cervical samples were collected immediately before colposcopy, one for the hr-HPV-DNA test and cytology and the other for the HPV16/18-E6 test using high-affinity monoclonal antibodies (mAb). Women with a histologic diagnosis of cervical intraepithelial neoplasia grade 2 or 3 were considered to be positive cases. Different strategies using a combination of screening methods (HPV-DNA) and triage tests (cytology and HPV16/18-E6) were also examined and compared. Results The HPV16/18-E6 test exhibited a lower positivity rate compared with the HPV-DNA test (19.0% vs. 29.3%, p<0.001) and a moderate/high agreement (kappa = 0.68, 95% CI: 0.60-0.75). It also exhibited a significantly lower sensitivity for CIN2+ and CIN3+ detection compared to the HPV-DNA test and a significantly higher specificity. The HPV16/18-E6 test was no different from cytology in terms of sensitivity, but it exhibited a significantly higher specificity in comparison to ASCH+. A triage test after HPV-DNA detection using the HPV16/18-E6 test exhibited a significantly higher specificity compared with a triage test of ASCH+ to CIN2+ (91.8% vs. 87.4%, p = 0.04) and CIN3+ (88.6% vs. 84.0%, p = 0.05). Conclusion The HPV16/18-E6 test exhibited moderate/high agreement with the HPV-DNA test but lower sensitivity and higher specificity for the detection of CIN2+ and CIN3+. In addition, its performance was quite similar to cytology, but because of the structural design addressed for the detection of HPV16/18-E6 protein, the test can miss some CIN2/3+ lesions caused by other high-risk HPV types.Cancer Prevention Department, Center for the Researcher Support and Pathology Department of the Barretos Cancer Hospital. This study was supported by CNPq 573799/2008-3 and FAPESP 2008/57889-1info:eu-repo/semantics/publishedVersio

Application-level performance of cross-layer scheduling for social VR in 5G

Social VR aims at enabling people located at different places to communicate and interact with each other in a natural way. It poses extremely strong throughput and latency requirements on the underlying communication networks. This paper investigates the potential of using cross-layer design approaches for radio access scheduling in order to realize these challenging requirements in (beyond) 5G networks. In particular, we provide an in-depth simulation study of the performance/capacity gains that can be achieved by exploiting the end-to-end latency budget and/or video frame type as cross-layer information in the scheduling decisions, and show how the benefits depend on the actual social VR scenario. This study further reveals the importance of using application-level metrics such as PSNR or SSIM rather than traditional network-level metrics like the packet drop rate in the performance assessment.</p