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

Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks

Transoesophageal echocardiography (TEE) is a valuable diagnostic and monitoring imaging modality. Proper image acquisition is essential for diagnosis, yet current assessment techniques are solely based on manual expert review. This paper presents a supervised deep learning framework for automatically evaluating and grading the quality of TEE images. To obtain the necessary dataset, 38 participants of varied experience performed TEE exams with a high-fidelity virtual reality (VR) platform. Two Convolutional Neural Network (CNN) architectures, AlexNet and VGG, structured to perform regression, were finetuned and validated on manually graded images from three evaluators. Two different scoring strategies, a criteria-based percentage and an overall general impression, were used. The developed CNN models estimate the average score with a root mean square accuracy ranging between 84% − 93%, indicating the ability to replicate expert valuation. Proposed strategies for automated TEE assessment can have a significant impact on the training process of new TEE operators, providing direct feedback and facilitating the development of the necessary dexterous skills

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

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

No association between anti-thyroidperoxidase antibodies and bipolar disorder: A study in the Dutch Bipolar Cohort and a meta-analysis

BACKGROUND: Thyroid autoimmunity has been associated with bipolar disorder (BD). However, results from previous studies on the seroprevalence of anti-thyroid peroxidase antibodies (TPO-abs) in BD are inconsistent. OBJECTIVES: The aim of the present study is to investigate whether the seroprevalence and titer levels of TPO-abs are related to BD. METHOD: TPO-abs were measured in plasma samples of 760 patients with bipolar disorder, 261 first-degree relatives and 363 controls by enzyme-linked immunosorbent assay (ELISA). To address methodological limitations of previous studies, we assessed clinical characteristics with several (self-reported) questionnaires to investigate whether TPO-abs positivity is related to particular clinical subgroups of BD patients. We performed an additional meta-analysis of seroprevalences of TPO-abs in BD patients including data from present and previous studies. RESULTS: Seroprevalence or titer levels of TPO-abs did not significantly differ between patients with BD, their first-degree relatives, and controls. In BD patients, the prevalence of TPO-abs was unrelated to specific clinical factors, including lithium use. Our meta-analysis of twelve studies showed an overall odds ratio of 1.3 (CI 95 %: 0.7-2.3; p = 0.30), reaffirming the absence of an association of BD with TPO-abs. CONCLUSIONS: In the largest study of TPO-abs in BD to date, our findings indicate that TPO-abs are not associated with (the risk for) bipolar disorder

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided

The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma

Five years after the last prostatic carcinoma grading consensus conference of the International Society of Urological Pathology (ISUP), accrual of new data and modification of clinical practice require an update of current pathologic grading guidelines. This manuscript summarizes the proceedings of the ISUP consensus meeting for grading of prostatic carcinoma held in September 2019, in Nice, France. Topics brought to consensus included the following: (1) approaches to reporting of Gleason patterns 4 and 5 quantities, and minor/tertiary patterns, (2) an agreement to report the presence of invasive cribriform carcinoma, (3) an agreement to incorporate intraductal carcinoma into grading, and (4) individual versus aggregate grading of systematic and multiparametric magnetic resonance imaging-targeted biopsies. Finally, developments in the field of artificial intelligence in the grading of prostatic carcinoma and future research perspectives were discussed

Hemodialysis Affects Phenotype and Proliferation of CD4-Positive T Lymphocytes

CD4+ T lymphocytes of patients with chronic kidney disease (CKD) are characterized by reduced levels of crucial surface antigens and changes in the cell cycle parameters. Recombinant human erythropoietin (rhEPO) normalizes their altered phenotype and proliferative capacity. Mechanisms leading to the deficient responses of T lymphocytes are still not clear but it is postulated that immunological changes are deepened by hemodialysis (HD). Study of activation parameters of CD4+ T lymphocytes in hemodialyzed and predialysis CKD patients could bring insight into this problem. Two groups of patients, treated conservatively (predialysis, PD) and hemodialyzed (HD), as well as healthy controls, were included into the study; neither had received rhEPO. Proportions of main CD4+CD28+, CD4+CD25+, CD4+CD69+, CD4+CD95+, and CD4+HLA-DR+ lymphocyte subpopulations and proliferation kinetic parameters were measured with flow cytometry, both ex vivo and in vitro. No differences were seen in the proportions of main CD4+ lymphocyte subpopulations (CD4+CD28+, CD4+CD25+, CD4+HLA-DR+, CD4+CD69+, CD4+CD95+) between all examined groups ex vivo. CD4+ T lymphocytes of HD patients exhibited significantly decreased expression of co-stimulatory molecule CD28 and activation markers CD25 and CD69 after stimulation in vitro when compared with PD patients and healthy controls. HD patients showed also decreased percentage of CD4+CD28+ lymphocytes proliferating in vitro; these cells presented decreased numbers of finished divisions after 72 h of stimulation in vitro and had longer G0→G1 time when compared to healthy controls. CD4+ T lymphocytes of PD patients and healthy controls were characterized by similar cell cycle parameters. Our study shows that repeated hemodialysis procedure influences phenotype and proliferation parameters of CD4+ T lymphocytes

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