Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving

Tissue engineering in pediatric urology – a critical appraisal

Tissue engineering is defined as the combination of biomaterials and bioengineering principles together with cell transplantation or directed growth of host cells to develop a biological replacement tissue or organ that can be a substitute for normal tissue both in structure and function. Despite early promising preclinical studies, clinical translation of tissue engineering in pediatric urology into humans has been unsuccessful both for cell-seeded and acellular scaffolds. This can be ascribed to various factors, including the use of only non-diseased models that inaccurately describe the structural and functional modifications of diseased tissue. The paper addresses potential future strategies to overcome the limitations experienced in clinical applications so far. This includes the use of stem cells of various origins (mesenchymal stem cells, hematopoietic stem/progenitor cells, urine-derived stem cells, and progenitor cells of the urothelium) as well as the need for a deeper understanding of signaling pathways and directing tissue ingrowth and differentiation through the concept of dynamic reciprocity. The development of smart scaffolds that release trophic factors in a set and timely manner will probably improve regeneration. Modulation of innate immune response as a major contributor to tissue regeneration outcome is also addressed. It is unlikely that only one of these strategies alone will lead to clinically applicable tissue engineering strategies in pediatric urology. In the meanwhile, the fundamental new insights into regenerative processes already obtained in the attempts of tissue engineering of the lower urogenital tract remain our greatest gain

Selective methylation of CpGs at regulatory binding sites controls NNAT expression in Wilms tumors.

Aberrant expression of imprinted genes, such as those coding for the insulin-like growth factor 2 (IGF2) and neuronatin (NNAT), is a characteristic of a variety of embryonic neoplasms, including Wilms tumor (WT). In case of IGF2, it is generally accepted that loss of imprinting in a differentially methylated region of the IGF2/H19 locus results in biallelic expression and, thus, upregulation of the gene. In this study we examined methylation pattern at potential regulatory elements of the paternally expressed NNAT gene in a cohort of WT patients in order to further characterize the molecular mechanism causing overexpression of this regulatory gene. We demonstrate that transcriptional upregulation of NNAT in WT is grossly independent of the bladder cancer-associated protein (BLCAP) gene, an imprinted gene within the imprinted domain of the NNAT locus. However, expression of the BLCAP transcript isoform v2a formerly known to be selectively expressed from the paternal allele in brain was associated with high expression of NNAT. This contrasts the situation we found at the IGF2/H19 locus, which shows high overexpression of IGF2 and inversely correlated expression of the H19 gene in WT. An analysis of DNA methylation in two potential regulatory regions of the NNAT locus by pyrosequencing revealed significant hypomethylation of the tumors compared to normal kidney tissue. Interestingly, the difference in DNA methylation was highest at CpGs that were observed within three putative binding sites of the CCCTC-binding factor CTCF. Most importantly, hypomethylation of both NNAT regulatory regions is significantly associated with the upregulation of NNAT expression and the BLCAP_v2a transcript. Our data indicate that the methylation status of a not-yet-described regulatory element within the NNAT locus that contains four potential CTCF binding sites determines the expression level of NNAT and the nearby located BLCAP_v2a transcript, thereby suggesting a functional role in the aberrant upregulation of NNAT in WT

Role of KEAP1/NFE2L2 Mutations in the Chemotherapeutic Response of Patients with Non-Small Cell Lung Cancer.

Purpose: Activation of NFE2L2 has been linked to chemoresistance in cell line models. Recently, somatic mutations that activate NFE2L2, including mutations in NFE2L2, KEAP1, or CUL3, have been found to be associated with poor outcomes in patients with non–small cell lung cancer (NSCLC). However, the impact of these mutations on chemoresistance remains incompletely explored. Experimental Design: We investigated the effect of Keap1 deletion on chemoresistance in cell lines from Trp53-based mouse models of lung squamous cell carcinoma (LSCC) and lung adenocarcinoma (LUAD). Separately, we identified 51 patients with stage IV NSCLC with KEAP1, NFE2L2, or CUL3 mutations and a matched cohort of 52 wild-type patients. Time to treatment failure after first-line platinum doublet chemotherapy and overall survival was compared between the two groups. Results: Deletion of Keap1 in Trp53-null murine LUAD and LSCC resulted in increased clonogenic survival upon treatment with diverse cytotoxic chemotherapies. In patients with NSCLC, median time to treatment failure (TTF) after first-line chemotherapy for the KEAP1/NFE2L2/CUL3-mutant cohort was 2.8 months compared with 8.3 months in the control group (P < 0.0001). Median overall survival (OS) was 11.2 months in the KEAP1/NFE2L2/CUL3-mutant group and 36.8 months in the control group (P ¼ 0.006). Conclusions: Keap1 deletion confers chemoresistance in murine lung cancer cells. Patients with metastatic NSCLC with mutations in KEAP1, NFE2L2, or CUL3 have shorter TTF and OS after first-line platinum doublet chemotherapy compared with matched controls. Novel approaches for improving outcomes in this subset of patients with NSCLC are therefore needed. ©2019 American Association for Cancer Research.1

Mean DNA methylation levels at the A) H19DMR, B) the distal NNAT-CTCF binding site, and C) the NNAT promoter in normal kidney and tumor tissues.

<p>Red horizontal lines either depict two standard deviations above or below the mean of the normal controls (A) or the median DNA methylation values (B+C).</p

Relative expression of the genes A) IGF2, B) H19, D) NNAT E) BLCAP, G) BLCAP_v1a, and H) BLCAP_v2a in 45 Wilms tumors and 11 normal kidney tissues, as determined by real-time PCR.

<p>The dots represent relative candidate gene expression in relation to the house-keeping gene TBP. Median expression values are given as red horizontal lines. Association of C) IGF2 and H19, F) NNAT and BLCAP, as well as I) NNAT and BLCAP_v2a gene expression in log2 scale using Spearman’s rank correlation. Data from the tumor and normal kidney cases are depicted as black and orange diamonds, respectively.</p

Mean DNA methylation levels delineated for each CpG at A) and D) the H19DMR, B) and E) the distal NNAT-CTCF binding site, C) and F) the NNAT promoter, and G) the BLCAP promoter in normal kidney (red squares) and tumor tissues (blue diamonds).

<p>A) to C) depict the DNA methylation levels for all tumor and normal kidney cases, whereas D) to F) depict only tumors with >10-fold mean expression of normal kidney for D) IGF2 (n = 22) and E) to F) NNAT (n = 33). Error bars represent the standard deviation of all tumor or kidney sample measurements.</p