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

Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets

High availability of data is responsible for the current trends in Artificial Intelligence (AI) and Machine Learning (ML). However, high-grade datasets are reluctantly shared between actors because of lacking trust and fear of losing control. Provenance tracing systems are a possible measure to build trust by improving transparency. Especially the tracing of AI assets along complete AI value chains bears various challenges such as trust, privacy, confidentiality, traceability, and fair remuneration. In this paper we design a graph-based provenance model for AI assets and their relations within an AI value chain. Moreover, we propose a protocol to exchange AI assets securely to selected parties. The provenance model and exchange protocol are then combined and implemented as a smart contract on a permission-less blockchain. We show how the smart contract enables the tracing of AI assets in an existing industry use case while solving all challenges. Consequently, our smart contract helps to increase traceability and transparency, encourages trust between actors and thus fosters collaboration between them

Single-Cell RNA Sequencing of Donor-Reactive T Cells Reveals Role of Apoptosis in Donor-Specific Hyporesponsiveness of Kidney Transplant Recipients

After kidney transplantation (KT), donor-specific hyporesponsiveness (DSH) of recipient T cells develops over time. Recently, apoptosis was identified as a possible underlying mechanism. In this study, both transcriptomic profiles and complete V(D)J variable regions of TR transcripts from individual alloreactive T cells of kidney transplant recipients were determined with single-cell RNA sequencing. Alloreactive T cells were identified by CD137 expression after stimulation of peripheral blood mononuclear cells obtained from KT recipients (N = 7) prior to and 3–5 years after transplantation with cells of their donor or a third party control. The alloreactive T cells were sorted, sequenced and the transcriptome and T cell receptor profiles were analyzed using unsupervised clustering. Alloreactive T cells retain a highly polyclonal T Cell Receptor Alpha/Beta repertoire over time. Post transplantation, donor-reactive CD4+ T cells had a specific downregulation of genes involved in T cell cytokine-mediated pathways and apoptosis. The CD8+ donor-reactive T cell profile did not change significantly over time. Single-cell expression profiling shows that activated and pro-apoptotic donor-reactive CD4+ T cell clones are preferentially lost after transplantation in stable kidney transplant recipients.</p

Learning to Segment Microscopy Images with Lazy Labels

The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for segmenting bioimages that can contain numerous object instances with thin separations. In this paper, we introduce a deep convolutional neural network for microscopy image segmentation. Annotation issues are circumvented by letting the network being trainable on coarse labels combined with only a very small number of images with pixel-wise annotations. We call this new labelling strategy `lazy' labels. Image segmentation is stratified into three connected tasks: rough inner region detection, object separation and pixel-wise segmentation. These tasks are learned in an end-to-end multi-task learning framework. The method is demonstrated on two microscopy datasets, where we show that the model gives accurate segmentation results even if exact boundary labels are missing for a majority of annotated data. It brings more flexibility and efficiency for training deep neural networks that are data hungry and is applicable to biomedical images with poor contrast at the object boundaries or with diverse textures and repeated patterns

Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction

There is a rising need for computational models that can complementarily leverage data of different modalities while investigating associations between subjects for population-based disease analysis. Despite the success of convolutional neural networks in representation learning for imaging data, it is still a very challenging task. In this paper, we propose a generalizable framework that can automatically integrate imaging data with non-imaging data in populations for uncertainty-aware disease prediction. At its core is a learnable adaptive population graph with variational edges, which we mathematically prove that it is optimizable in conjunction with graph convolutional neural networks. To estimate the predictive uncertainty related to the graph topology, we propose the novel concept of Monte-Carlo edge dropout. Experimental results on four databases show that our method can consistently and significantly improve the diagnostic accuracy for Autism spectrum disorder, Alzheimer's disease, and ocular diseases, indicating its generalizability in leveraging multimodal data for computer-aided diagnosis.Comment: Accepted to MICCAI 202

Machine Learning Approach for the Early Prediction of the Risk of Overweight and Obesity in Young People

Obesity is a major global concern with more than 2.1 billion people overweight or obese worldwide which amounts to almost 30% of the global population. If the current trend continues, the overweight and obese population is likely to increase to 41% by 2030. Individuals developing signs of weight gain or obesity are also at a risk of developing serious illnesses such as type 2 diabetes, respiratory problems, heart disease and stroke. Some intervention measures such as physical activity and healthy eating can be a fundamental component to maintain a healthy lifestyle. Therefore, it is absolutely essential to detect childhood obesity as early as possible. This paper utilises the vast amount of data available via UK’s millennium cohort study in order to construct a machine learning driven model to predict young people at the risk of becoming overweight or obese. The childhood BMI values from the ages 3, 5, 7 and 11 are used to predict adolescents of age 14 at the risk of becoming overweight or obese. There is an inherent imbalance in the dataset of individuals with normal BMI and the ones at risk. The results obtained are encouraging and a prediction accuracy of over 90% for the target class has been achieved. Various issues relating to data preprocessing and prediction accuracy are addressed and discussed

Mucopolysaccharidosis VI

Mucopolysaccharidosis VI (MPS VI) is a lysosomal storage disease with progressive multisystem involvement, associated with a deficiency of arylsulfatase B leading to the accumulation of dermatan sulfate. Birth prevalence is between 1 in 43,261 and 1 in 1,505,160 live births. The disorder shows a wide spectrum of symptoms from slowly to rapidly progressing forms. The characteristic skeletal dysplasia includes short stature, dysostosis multiplex and degenerative joint disease. Rapidly progressing forms may have onset from birth, elevated urinary glycosaminoglycans (generally >100 μg/mg creatinine), severe dysostosis multiplex, short stature, and death before the 2nd or 3rd decades. A more slowly progressing form has been described as having later onset, mildly elevated glycosaminoglycans (generally <100 μg/mg creatinine), mild dysostosis multiplex, with death in the 4th or 5th decades. Other clinical findings may include cardiac valve disease, reduced pulmonary function, hepatosplenomegaly, sinusitis, otitis media, hearing loss, sleep apnea, corneal clouding, carpal tunnel disease, and inguinal or umbilical hernia. Although intellectual deficit is generally absent in MPS VI, central nervous system findings may include cervical cord compression caused by cervical spinal instability, meningeal thickening and/or bony stenosis, communicating hydrocephalus, optic nerve atrophy and blindness. The disorder is transmitted in an autosomal recessive manner and is caused by mutations in the ARSB gene, located in chromosome 5 (5q13-5q14). Over 130 ARSB mutations have been reported, causing absent or reduced arylsulfatase B (N-acetylgalactosamine 4-sulfatase) activity and interrupted dermatan sulfate and chondroitin sulfate degradation. Diagnosis generally requires evidence of clinical phenotype, arylsulfatase B enzyme activity <10% of the lower limit of normal in cultured fibroblasts or isolated leukocytes, and demonstration of a normal activity of a different sulfatase enzyme (to exclude multiple sulfatase deficiency). The finding of elevated urinary dermatan sulfate with the absence of heparan sulfate is supportive. In addition to multiple sulfatase deficiency, the differential diagnosis should also include other forms of MPS (MPS I, II IVA, VII), sialidosis and mucolipidosis. Before enzyme replacement therapy (ERT) with galsulfase (Naglazyme®), clinical management was limited to supportive care and hematopoietic stem cell transplantation. Galsulfase is now widely available and is a specific therapy providing improved endurance with an acceptable safety profile. Prognosis is variable depending on the age of onset, rate of disease progression, age at initiation of ERT and on the quality of the medical care provided

Decreased antigen-specific T-cell proliferation by moDC among hepatitis B vaccine non-responders on haemodialysis

Patients with end-stage kidney disease, whether or not on renal replacement therapy, have an impaired immune system. This is clinically manifested by a large percentage of patients unresponsive to the standard vaccination procedure for hepatitis B virus (HBV). In this study, the immune response to HBV vaccination is related to the in vitro function of monocyte-derived dendritic cells (moDC). We demonstrate that mature moDC from nonresponders to HBV vaccination have a less mature phenotype, compared to responders and healthy volunteers, although this did not affect their allostimulatory capacity. However, proliferation of autologous T cells in the presence of tetanus toxoid and candida antigen was decreased in non-responders. Also, HLA-matched CD4+ hsp65-specific human T-cell clones showed markedly decreased proliferation in the group of non-responders. Our results indicate that impairment of moDC to stimulate antigen-specific T cells provides an explanation for the clinical immunodeficiency of patients with end-stage kidney disease