Unstable Identities: The European Court of Human Rights and the Margin of Appreciation

All legal systems work under a master narrative – the self-conception of most actors of the system itself. A master narrative is a short and simple story and it is the underlying premise upon which any legal system is based. It is a simple story because it paints the system in quick broad brushstrokes and at (most) times is oblivious to the paradoxes within it. Furthermore, a master narrative is important for legitimization purposes because the actors’ legitimacy will depend on their (perceived) conformity with the system’s master narrative. Therefore, legitimacy is self-referential; the yardsticks for a legitimate action are contained within the system’s master narrative, not outside of it. When talking about different international courts it is important to remember that they are embedded within a master narrative that is contextual and contingent and, at different points, more or less contested. This paper explores the question of what happens when the master-narrative is in a period of transition (from a state cantered to a post-national world order) and when the actors’ legitimacy, their interpretative endeavours the very fundamentals are in a state of flux. I use the margin of appreciation discussion as a focal point of describing the conflicting narratives under which the European Court of Human Rights works, narratives in which the different actors (judges, attorneys, NGO activists, government agents) and their consequences in terms of the interpretation of the European Convention on Human Rights

Ultra-Filtration of Human Serum for Improved Quantitative Analysis of Low Molecular Weight Biomarkers using ATR-IR Spectroscopy

Infrared spectroscopy is a reliable, rapid and cost effective characterisation technique, delivering a molecular finger print of the sample. It is expected that its sensitivity would enable detection of small chemical variations in biological samples associated with disease. ATR-IR is particularly suitable for liquid sample analysis and, although air drying is commonly performed before data collection, just a drop of human serum is enough for screening and early diagnosis. However, the dynamic range of constituent biochemical concentrations in the serum composition remains a limiting factor to the reliability of the technique. Using glucose as a model spike in human serum, it has been demonstrated in the present study that fractionating the serum prior to spectroscopic analysis can considerably improve the precision and accuracy of quantitative models based on the Partial Least Squares Regression algorithm. By depleting the abundant high molecular weight proteins, which otherwise dominate the spectral signatures collected, the ability to monitor changes in the concentrations of the low molecular weight constituents is enhanced. The Root Mean Square Error for the Validation set (RMSEV) has been improved by a factor of 5 following human serum processing with an average relative error in the predictive values below 1% is achieved. Moreover, the approach is easily transferable to different bodily fluids, which would support the development of more efficient and suitable clinical protocols for exploration of vibrational spectroscopy based ex-vivo diagnostic tools

Exchange of coordinated solvent during crystallisation of a metal-organic framework observed by in situ high energy X-ray diffraction

Using time-resolved monochromatic high energy X-ray diffraction, we present an in situ study of the solvothermal crystallisation of a new MOF [Yb2(BDC)3(DMF)2]⋅H2O (BDC=benzene-1,4-dicarboxylate and DMF=N,N-dimethylformamide) under solvothermal conditions, from mixed water/DMF solvent. Analysis of high resolution powder patterns obtained reveals an evolution of lattice parameters and electron density during the crystallisation process and Rietveld analysis shows that this is due to a gradual topochemical replacement of coordinated solvent molecules. The water initially coordinated to Yb3+ is replaced by DMF as the reaction progresses

Analysis of Bodily Fluids Using Vibrational Spectroscopy: a Direct Comparison of Raman Scattering and Infrared Absorption Techniques for the Case of Glucose in Blood Serum

Analysis of biomarkers present in the blood stream can potentially deliver crucial information on patient health and indicate the presence of numerous pathologies. The potential of vibrational spectroscopic analysis of human serum for diagnostic purposes has been widely investigated and, in recent times, infrared absorption spectroscopy, coupled with ultra-filtration and multivariate analysis techniques, has attracted increasing attention, both clinical and commercial. However, such methods commonly employ a drying step, which may hinder the clinical work flow and thus hamper their clinical deployment. As an alternative, this study explores the use of Raman spectroscopy, similarly coupled with ultra-filtration and multivariate analysis techniques, to quantitatively monitor diagnostically relevant changes of glucose in liquid serum samples, and compares the results with similar analysis protocols using infrared spectroscopy of dried samples. The analysis protocols to detect the imbalances in glucose using Raman spectroscopy are first demonstrated for aqueous solutions and spiked serum samples. As in the case of infrared absorption studies, centrifugal filtration is utilised to deplete abundant analytes and to reveal the spectral features of Low Molecular Weight Fraction analytes in order to improve spectral sensitivity and detection limits. Improved Root Mean Square Error of Cross Validation (RMSECV) was observed for Raman prediction models, whereas slightly higher R2 values were reported for infrared absorption prediction models. Summarising, it is demonstrated that the Raman analysis protocol can yield accuracies which are comparable with those reported using infrared absorption based measurements of dried serum, without the need for additional drying steps

Non-Deterministic Kleene Coalgebras

In this paper, we present a systematic way of deriving (1) languages of (generalised) regular expressions, and (2) sound and complete axiomatizations thereof, for a wide variety of systems. This generalizes both the results of Kleene (on regular languages and deterministic finite automata) and Milner (on regular behaviours and finite labelled transition systems), and includes many other systems such as Mealy and Moore machines

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p