Interoperability in the OpenDreamKit project : the Math-in-the-Middle approach

OpenDreamKit - "Open Digital Research Environment Toolkit for the Advancement of Mathematics" - is an H2020 EU Research Infrastructure project that aims at supporting, over the period 2015-2019, the ecosystem of open-source mathematical software systems. OpenDreamKit will deliver a flexible toolkit enabling research groups to set up Virtual Research Environments, customised to meet the varied needs of research projects in pure mathematics and applications. An important step in the OpenDreamKit endeavor is to foster the interoperability between a variety of systems, ranging from computer algebra systems over mathematical databases to front-ends. This is the mission of the integration work package. We report on experiments and future plans with the Math-in-the-Middle approach. This architecture consists of a central mathematical ontology that documents the domain and xes a joint vocabulary, or even a language, going beyond existing systems such as OpenMath, combined with specifications of the functionalities of the various systems. Interaction between systems can then be enriched by pivoting around this architecture.Postprin

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

Background: Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. Methods: Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naive Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. Results: Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 +/- 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 +/- 0.03) or SERS data (AUC = 0.84 +/- 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 +/- 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 +/- 0.04) or SERS (AUC = 0.92 +/- 0.05) individually, although SERS alone performed better in terms of classification accuracy. Conclusion: miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged

Importing the OEIS library into OMDoc

Abstract. The On-line Encyclopedia of Integer Sequences (OEIS) is the largest database of its kind and an important resource for mathematicians. The database is well-structured and rich in mathematical content but is informal in nature so knowledge management services are not directly applicable. In this paper we provide a partial parser for the OEIS that leverages the fact that, in practice, the syntax used in its formulas is fairly regular. Then, we import the result into OMD to make the OEIS accessible to OMD -based knowledge management applications. We exemplify this with a formula search application based on the M W S system

The Mizar Mathematical Library in OMDoc: Translation and Applications

Contains fulltext : 111268.pdf (preprint version ) (Open Access

A Glimpse at the Size of the Fetal Liver—Is It Connected with the Evolution of Gestational Diabetes?

Gestational diabetes mellitus (GDM) is defined as an impairment of glucose tolerance, manifested by hyperglycemia, which occurs at any stage of pregnancy. GDM is more common in the third trimester of pregnancy and usually disappears after birth. It was hypothesized that the glycemic status of the mother can modulate liver development and growth early during the pregnancy. The simplest modality to monitor the evolution of GDM employs noninvasive techniques. In this category, routinely obstetrical ultrasound (OUS) examinations (simple or 2D/3D) can be employed for specific fetal measurements, such as fetal liver length (FLL) or volume (FLV). FLL and FLV may emerge as possible predictors of GDM as they positively relate to the maternal glycated hemoglobin (HbA1c) levels and to the results of the oral glucose tolerance test. The aim of this review is to offer insight into the relationship between GDM and fetal nutritional status. Risk factors for GDM and the short- and long-term outcomes of GDM pregnancies are also discussed, as well as the significance of different dietary patterns. Moreover, the review aims to fill one gap in the literature, investigating whether fetal liver growth can be used as a predictor of GDM evolution. To conclude, although studies pointed out a connection between fetal indices and GDM as useful tools in the early detection of GDM (before 23 weeks of gestation), additional research is needed to properly manage GDM and offspring health

Extended panel of biomarkers for long term monitoring of effectiveness of 3 direct antiviral regimen in HCV genotype 1b infection: results from a Romanian infectious disease hospital

Background: Hepatitis C virus can be eradicated with antiviral therapy, thus reducing the risk of disease progression and death associated with the final stage of liver disease

SERS Liquid Biopsy Profiling of Serum for the Diagnosis of Kidney Cancer

Renal cancer (RC) represents 3% of all cancers, with a 2% annual increase in incidence worldwide, opening the discussion about the need for screening. However, no established screening tool currently exists for RC. To tackle this issue, we assessed surface-enhanced Raman scattering (SERS) profiling of serum as a liquid biopsy strategy to detect renal cell carcinoma (RCC), the most prevalent histologic subtype of RC. Thus, serum samples were collected from 23 patients with RCC and 27 controls (CTRL) presenting with a benign urological pathology such as lithiasis or benign prostatic hypertrophy. SERS profiling of deproteinized serum yielded SERS band spectra attributed mainly to purine metabolites, which exhibited higher intensities in the RCC group, and Raman bands of carotenoids, which exhibited lower intensities in the RCC group. Principal component analysis (PCA) of the SERS spectra showed a tendency for the unsupervised clustering of the two groups. Next, three machine learning algorithms (random forest, kNN, naïve Bayes) were implemented as supervised classification algorithms for achieving discrimination between the RCC and CTRL groups, yielding an AUC of 0.78 for random forest, 0.78 for kNN, and 0.76 for naïve Bayes (average AUC 0.77 ± 0.01). The present study highlights the potential of SERS liquid biopsy as a diagnostic and screening strategy for RCC. Further studies involving large cohorts and other urologic malignancies as controls are needed to validate the proposed SERS approach