Blue Gold – The Utilisation of the Nubian Sandstone Aquifer System in Light of Islamic Norms and its Impact on the Emerging Law of Transboundary Fossil Aquifers

The Nubian Sandstone Aquifer System is one of the world’s largest transboundary fossil aquifers and stretches underneath the territories of the North African States of Egypt, Libya, Sudan and Chad. All four States have strong Islamic cultural backgrounds, and Egypt, Libya and Sudan have enshrined Shari'a as a fundamental source of law in their constitutions. This thesis assesses the extent to which the 2008 Draft Articles on the Law of Transboundary Aquifers, proposed to the UN General Assembly by the International Law Commission, are compatible with general principles of Islamic water law. Both the 2008 Draft Articles as the current culmination of international groundwater law and Islamic law suffer from certain shortcomings. Whilst the former lacks the same binding authority Islamic law enjoys and to date does not elaborate the potential issue of water commercialisation in water scarce regions, the latter lacks the transboundary perspective in relation to groundwater. This highlights the impact Islamic law could have on the on-going negotiations between the NSAS Aquifer States, whereby specific Islamic provisions could provide stepping-stones towards an innovative utilisation framework for the NSAS that adequately addresses the need for precaution and intergenerational equity, which, inter alia, could instil new impetus for a refined set of Draft Articles. An alternative future is likely to evolve along the lines of separate agreements and a more fragmented corpus of international law rather than a coherent body of codified international law on transboundary fossil aquifers, which would run counter to the International Law Commission’s objective

Particle identification using artificial neural networks with the ALICE transition radiation detector

Der ALICE Übergangsstrahlungsdetektor (TRD) wurde als Tracking-Detektor, als Trigger-Detektor für Elektronen und für die Identifikation von Elektronen konzipiert. Das Konstruktionsziel für den Übergangsstrahlungsdetektor war eine Pioneneffizienz von 1% bei einer Elektroneneffizienz von 90% zu erreichen. Das Signal das im TRD zur Teilchenidentifikation benutzt wird besteht aus zwei Komponenten. Geladene Teilchen, die den Übergangsstrahlungsdetektor durchqueren, deponieren Energie durch Stoßionisation. Zusätzliche dazu produtieren Elektronen Übergangsstrahlung die früh im Driftbereich des TRD absorbiert wird. In dieser Arbeit wurde die Anwendung von neuronalen Netzen für die Teilchenidentifikation mit dem TRD untersucht. Als Eingangsgröße für die Netze wurde das Signal in mehrere Abschnitte unterteilt. Die Ergebnisse sowohl von Teststrahl-Experimenten als auch von Simulationen zeigen, dass mit neuronalen Netzen eine bessere Teilchenidentifikation erreichbar ist, als mit anderen Methoden

Learning Layer-wise Equivariances Automatically using Gradients

Convolutions encode equivariance symmetries into neural networks leading to better generalisation performance. However, symmetries provide fixed hard constraints on the functions a network can represent, need to be specified in advance, and can not be adapted. Our goal is to allow flexible symmetry constraints that can automatically be learned from data using gradients. Learning symmetry and associated weight connectivity structures from scratch is difficult for two reasons. First, it requires efficient and flexible parameterisations of layer-wise equivariances. Secondly, symmetries act as constraints and are therefore not encouraged by training losses measuring data fit. To overcome these challenges, we improve parameterisations of soft equivariance and learn the amount of equivariance in layers by optimising the marginal likelihood, estimated using differentiable Laplace approximations. The objective balances data fit and model complexity enabling layer-wise symmetry discovery in deep networks. We demonstrate the ability to automatically learn layer-wise equivariances on image classification tasks, achieving equivalent or improved performance over baselines with hard-coded symmetry

VarSight: prioritizing clinically reported variants with binary classification algorithms.

BackgroundWhen applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient's phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance.MethodsWe tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network.ResultsWe treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20.ConclusionsWe demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees

Gaussian processes are frequently deployed as part of larger machine learning and decision-making systems, for instance in geospatial modeling, Bayesian optimization, or in latent Gaussian models. Within a system, the Gaussian process model needs to perform in a stable and reliable manner to ensure it interacts correctly with other parts of the system. In this work, we study the numerical stability of scalable sparse approximations based on inducing points. To do so, we first review numerical stability, and illustrate typical situations in which Gaussian process models can be unstable. Building on stability theory originally developed in the interpolation literature, we derive sufficient and in certain cases necessary conditions on the inducing points for the computations performed to be numerically stable. For low-dimensional tasks such as geospatial modeling, we propose an automated method for computing inducing points satisfying these conditions. This is done via a modification of the cover tree data structure, which is of independent interest. We additionally propose an alternative sparse approximation for regression with a Gaussian likelihood which trades off a small amount of performance to further improve stability. We provide illustrative examples showing the relationship between stability of calculations and predictive performance of inducing point methods on spatial tasks

The Bose-Einstein correlation function C2(Q)C_2(Q) from a Quantum Field Theory point of view

We show that a recently proposed derivation of Bose-Einstein correlations (BEC) by means of a specific version of thermal Quantum Field Theory (QFT), supplemented by operator-field evolution of the Langevin type, allows for a deeper understanding of the possible coherent behaviour of the emitting source and a clear identification of the origin of the observed shape of the BEC function $C_2(Q)$. Previous conjectures in this matter obtained by other approaches are confirmed and have received complementary explanation.Comment: Some misprints corrected. To be publishe in Phys. Rev.

Adiponectin protects against Toll-like receptor 4-mediated cardiac inflammation and injury

Aims Adiponectin (APN) is an immunomodulatory and cardioprotective adipocytokine. Toll-like receptor (TLR) 4 mediates autoimmune reactions that cause myocarditis resulting in inflammation-induced cardiac injury. Here, we investigated whether APN inhibits inflammation and injury in autoimmune myocarditis by interfering with TLR4 signalling. Methods and results APN overexpression in murine experimental autoimmune myocarditis (EAM) down-regulated cardiac expression of TLR4 and its downstream targets tumour necrosis factor (TNF)α, interleukin (IL)-6, IL-12, CC chemokine ligand (CCL)2, and intercellular adhesion molecule (ICAM)-1 resulting in reduced infiltration with cluster of differentiation (CD)3+, CD14+, and CD45+ immune cells as well as diminished myocardial apoptosis. Expression of TLR4 signalling pathway components was unchanged in hearts and spleens of APN-knockout (APN-KO) mice. In vitro APN had no effect on TLR4 expression in cardiac and immune cells but induced dissociation of APN receptors from the activated TLR4/CD14 signalling complex. APN inhibited the expression of a TLR4-mediated inflammatory phenotype induced by exogenous and endogenous TLR4 ligands as assessed by attenuated nuclear factor (NF)-κB activation and reduced expression of TNFα, IL-6, CCL2, and ICAM-1. Accordingly, following TLR4 ligation, splenocytes from APN-KO mice showed enhanced expression of TNFα, IL-6, IL-12, CCL2, and ICAM-1, whereas dendritic cells (DCs) from APN-KO mice demonstrated increased activation and T-cell priming capacity. Moreover, APN diminished TLR4-mediated splenocyte migration towards cardiac cells as well as cardiomyocyte apoptosis after co-cultivation with splenocytes. Mechanistically, APN inhibited TLR4 signalling through cyclooxygenase (COX)-2, protein kinase A (PKA), and meiosis-specific serine/threonine kinase (MEK)1. Conclusion Our observations indicate that APN protects against inflammation and injury in autoimmune myocarditis by diminishing TLR4 signalling thereby attenuating inflammatory activation and interaction of cardiac and immune cell

Analyzing quantum jumps of one and two atoms strongly coupled to an optical cavity

We induce quantum jumps between the hyperfine ground states of one and two Cesium atoms, strongly coupled to the mode of a high-finesse optical resonator, and analyze the resulting random telegraph signals. We identify experimental parameters to deduce the atomic spin state nondestructively from the stream of photons transmitted through the cavity, achieving a compromise between a good signal-to-noise ratio and minimal measurement-induced perturbations. In order to extract optimum information about the spin dynamics from the photon count signal, a Bayesian update formalism is employed, which yields time-dependent probabilities for the atoms to be in either hyperfine state. We discuss the effect of super-Poissonian photon number distributions caused by atomic motion.Comment: 12 pages, 13 figure

Adiponectin expression in patients with inflammatory cardiomyopathy indicates favourable outcome and inflammation control

Aims Circulating adiponectin (APN) is an immunomodulatory, pro-angiogenic, and anti-apoptotic adipocytokine protecting against acute viral heart disease and preventing pathological remodelling after cardiac injury. The purpose of this study was to describe the regulation and effects of APN in patients with inflammatory cardiomyopathy (DCMi). Methods and results Adiponectin expression and outcome were assessed in 173 patients with DCMi, 30 patients with non-inflammatory DCM, and 30 controls. Mechanistic background of these findings was addressed in murine experimental autoimmune myocarditis (EAM), a model of human DCMi, and further elucidated in vitro. Adiponectin plasma concentrations were significantly higher in DCMi compared with DCM or controls, i.e. 6.8 ± 3.9 µg/mL vs. 5.4 ± 3.6 vs. 4.76 ± 2.5 µg/mL (P< 0.05, respectively) and correlated significantly with cardiac mononuclear infiltrates (CD3+: r2= 0.025, P= 0.038; CD45R0+: r2= 0.058, P= 0.018). At follow-up, DCMi patients with high APN levels showed significantly increased left ventricular ejection fraction improvement, decreased left ventricular end-diastolic diameter, and reduced cardiac inflammatory infiltrates compared with patients with low APN levels. A multivariate linear regression analysis implicated APN as an independent prognostic factor for inhibition of cardiac inflammation. In accordance with these findings in human DCMi, EAM mice exhibited elevated plasma APN. Adiponectin gene transfer led to significant downregulation of key inflammatory mediators promoting disease. Mechanistically, APN acted as a negative regulator of T cells by reducing antigen specific expansion (P< 0.01) and suppressed TNFα-mediated NFκB activation (P< 0.01) as well as release of reactive oxygen species in cardiomyocytes. Conclusion Our results implicate that APN acts as endogenously upregulated anti-inflammatory cytokine confining cardiac inflammation and progression in DCM