376 research outputs found
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
End stage renal disease patients have a skewed T cell receptor Vβ repertoire
BACKGROUND: End stage renal disease (ESRD) is associated with defective T-cell mediated immunity. A diverse T-cell receptor (TCR) Vβ repertoire is central to effective T-cell mediated immune responses to foreign antigens. In this study, the effect of ESRD on TCR Vβ repertoire was assessed. RESULTS: A higher proportion of ESRD patients (68.9 %) had a skewed TCR Vβ repertoire compared to age and cytomegalovirus (CMV) – IgG serostatus matched healthy individuals (31.4 %, P < 0.001). Age, CMV serostatus and ESRD were independently associated with an increase in shifting of the TCR Vβ repertoire. More differentiated CD8(+) T cells were observed in young ESRD patients with a shifted TCR Vβ repertoire. CD31-expressing naive T cells and relative telomere length of T cells were not significantly related to TCR Vβ skewing. CONCLUSIONS: ESRD significantly skewed the TCR Vβ repertoire particularly in the elderly population, which may contribute to the uremia-associated defect in T-cell mediated immunity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12979-015-0055-7) contains supplementary material, which is available to authorized users
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
Deep Placental Vessel Segmentation for Fetoscopic Mosaicking
During fetoscopic laser photocoagulation, a treatment for twin-to-twin
transfusion syndrome (TTTS), the clinician first identifies abnormal placental
vascular connections and laser ablates them to regulate blood flow in both
fetuses. The procedure is challenging due to the mobility of the environment,
poor visibility in amniotic fluid, occasional bleeding, and limitations in the
fetoscopic field-of-view and image quality. Ideally, anastomotic placental
vessels would be automatically identified, segmented and registered to create
expanded vessel maps to guide laser ablation, however, such methods have yet to
be clinically adopted. We propose a solution utilising the U-Net architecture
for performing placental vessel segmentation in fetoscopic videos. The obtained
vessel probability maps provide sufficient cues for mosaicking alignment by
registering consecutive vessel maps using the direct intensity-based technique.
Experiments on 6 different in vivo fetoscopic videos demonstrate that the
vessel intensity-based registration outperformed image intensity-based
registration approaches showing better robustness in qualitative and
quantitative comparison. We additionally reduce drift accumulation to
negligible even for sequences with up to 400 frames and we incorporate a scheme
for quantifying drift error in the absence of the ground-truth. Our paper
provides a benchmark for fetoscopy placental vessel segmentation and
registration by contributing the first in vivo vessel segmentation and
fetoscopic videos dataset.Comment: Accepted at MICCAI 202
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
An Elastic Interaction-Based Loss Function for Medical Image Segmentation
Deep learning techniques have shown their success in medical image
segmentation since they are easy to manipulate and robust to various types of
datasets. The commonly used loss functions in the deep segmentation task are
pixel-wise loss functions. This results in a bottleneck for these models to
achieve high precision for complicated structures in biomedical images. For
example, the predicted small blood vessels in retinal images are often
disconnected or even missed under the supervision of the pixel-wise losses.
This paper addresses this problem by introducing a long-range elastic
interaction-based training strategy. In this strategy, convolutional neural
network (CNN) learns the target region under the guidance of the elastic
interaction energy between the boundary of the predicted region and that of the
actual object. Under the supervision of the proposed loss, the boundary of the
predicted region is attracted strongly by the object boundary and tends to stay
connected. Experimental results show that our method is able to achieve
considerable improvements compared to commonly used pixel-wise loss functions
(cross entropy and dice Loss) and other recent loss functions on three retinal
vessel segmentation datasets, DRIVE, STARE and CHASEDB1
Differential effects of age, cytomegalovirus-seropositivity and end-stage renal disease (ESRD) on circulating T lymphocyte subsets
The age- and cytomegalovirus (CMV)-seropositivity-related changes in subsets and differentiation of circulating T cells were investigated in end-stage renal disease (ESRD) patients (n = 139) and age-matched healthy individuals. The results show that CMV-seropositivity is associated with expansion of both CD4+ and CD8+ memory T cells which is already observed in young healthy individuals. In addition, CMV-seropositive healthy individuals have a more differentiated memory T cell profile. Only CMV-seropositive healthy individuals showed an age-dependent decrease in CD4+ naïve T cells. The age-related decrease in the number of CD8+ naïve T cells was CMV-independent. In contrast, all ESRD patients showed a profound naïve T-cell lymphopenia at every decade. CMV-seropositivity aggravated the contraction of CD4+ naïve T cells and increased the number of differentiated CD4+ and CD8+ memory T cells. In conclusion, CMV-seropositivity markedly alters the homeostasis of circulating T cells in healthy individuals and aggravates the T cell dysregulation observed in ESRD patients
Discriminative Localized Sparse Representations for Breast Cancer Screening
Breast cancer is the most common cancer among women both in developed and
developing countries. Early detection and diagnosis of breast cancer may reduce
its mortality and improve the quality of life. Computer-aided detection (CADx)
and computer-aided diagnosis (CAD) techniques have shown promise for reducing
the burden of human expert reading and improve the accuracy and reproducibility
of results. Sparse analysis techniques have produced relevant results for
representing and recognizing imaging patterns. In this work we propose a method
for Label Consistent Spatially Localized Ensemble Sparse Analysis (LC-SLESA).
In this work we apply dictionary learning to our block based sparse analysis
method to classify breast lesions as benign or malignant. The performance of
our method in conjunction with LC-KSVD dictionary learning is evaluated using
10-, 20-, and 30-fold cross validation on the MIAS dataset. Our results
indicate that the proposed sparse analyses may be a useful component for breast
cancer screening applications
Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning
The crucial components of a conventional image registration method are the
choice of the right feature representations and similarity measures. These two
components, although elaborately designed, are somewhat handcrafted using human
knowledge. To this end, these two components are tackled in an end-to-end
manner via reinforcement learning in this work. Specifically, an artificial
agent, which is composed of a combined policy and value network, is trained to
adjust the moving image toward the right direction. We train this network using
an asynchronous reinforcement learning algorithm, where a customized reward
function is also leveraged to encourage robust image registration. This trained
network is further incorporated with a lookahead inference to improve the
registration capability. The advantage of this algorithm is fully demonstrated
by our superior performance on clinical MR and CT image pairs to other
state-of-the-art medical image registration methods
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