Properties of Persistent Mutual Information and Emergence

The persistent mutual information (PMI) is a complexity measure for stochastic processes. It is related to well-known complexity measures like excess entropy or statistical complexity. Essentially it is a variation of the excess entropy so that it can be interpreted as a specific measure of system internal memory. The PMI was first introduced in 2010 by Ball, Diakonova and MacKay as a measure for (strong) emergence. In this paper we define the PMI mathematically and investigate the relation to excess entropy and statistical complexity. In particular we prove that the excess entropy is an upper bound of the PMI. Furthermore we show some properties of the PMI and calculate it explicitly for some example processes. We also discuss to what extend it is a measure for emergence and compare it with alternative approaches used to formalize emergence.Comment: 45 pages excerpt of Diploma-Thesi

Causal discovery with Point of Sales data

[ES] GfK owns the world’s largest retail panel within the tech and durable good industries. The panel consists of weekly Point of Sales (PoS) data, such as price and sales units data at store level. From PoS data and other data, GfK derives insights and indicators to generate recommendations with regards to e.g. pricing, distribution or assortment optimization of tech and durable good products. By combining PoS data and business domain knowledge, we show how causal discovery can be done by applying the method of invariant causal prediction (ICP). Causal discovery, in essence, means to learn the actual cause and effect relations between the involved variables from data. After finding such a causal structure, one can try to further specify the function classes between those identified cause-effect pairs. Such a model could then be used to predict under intervention (predict when the underlying data generating mechanism changes) and to optimize and calculate counterfactual effects, given current and past data. In our development, we combine recent achievements in causal discovery research with PoS data structure and business domain knowledge (in the form of business rules). The key delivery of this presentation is to show fundamental differences between a causal model and a machine learning model. We further explain the advantages of combining a causal model with a machine learning model and why causal information is key to provide explainable prescriptive analytics. Furthermore, we demonstrate how to apply ICP (for sequential data) to context-specific PoS data to achieve improved models for sales unit predictions. As a result, we obtain a model for sales units that is on the one hand derived from observed data and on the other hand driven by business knowledge. Such a refined prediction model could then be used to stabilize and support other machine learning models that can be used for generating prescriptive analytics.Gmeiner, P. (2020). Causal discovery with Point of Sales data. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/149590OC

Dopamine D3 receptor ligands—Recent advances in the control of subtype selectivity and intrinsic activity

AbstractVarious pharmacological studies have implicated the dopamine D3 receptor as an interesting therapeutic target in the treatment of different neurological disorders. Because of these putative therapeutic applications, D3 receptor ligands with diverse intrinsic activities have been an active field of research in recent years. Separation of purely D3-mediated drug effects from effects produced by interactions with similar biogenic amine receptors allows to verify the therapeutic impact of D3 receptors and to reduce possible side-effects caused by “promiscuous” receptor interactions. The requirement to gain control of receptor selectivity and in particular subtype selectivity has been a challenging task in rational drug discovery for quite a few years. In this review, recently developed structural classes of D3 ligands are discussed, which cover a broad spectrum of intrinsic activities and show interesting selectivities

The Bitter Taste Receptor TAS2R14 as a Drug Target

G protein-coupled receptors (GPCRs) mediate most of our physiological responses to hormones, neurotransmitters and environmental stimulants. Besides human senses like vision and olfaction, taste perception is mostly mediated by GPCRs. Hence, the bitter taste receptor family TAS2R comprises 25 distinct receptors and plays a key role in food acceptance and drug compliance. The TAS2R14 subtype is the most broadly tuned bitter taste receptor, recognizing a range of chemically highly diverse agonists. Besides other tissues, it is expressed in human airway smooth muscle and may represent a novel drug target for airway diseases. Several natural products as well as marketed drugs including flufenamic acid have been identified to activate TAS2R14, but higher potency ligands are needed to investigate the ligand-controlled physiological function and to facilitate the targeted modulate for potential future clinical applications. A combination of structure-based molecular modeling with chemical synthesis and in vitro profiling recently resulted in new flufenamic acid agonists with improved TAS2R14 potency and provided a validated and refined structural model of ligand–TAS2R14 interactions, which can be applied for future drug design projects

Structure-based development of caged dopamine D2/D3 receptor antagonists

Dopamine is a neurotransmitter of great physiological relevance. Disorders in dopaminergic signal transduction are associated with psychiatric and neurological pathologies such as Parkinson's disease, schizophrenia and substance abuse. Therefore, a detailed understanding of dopaminergic neurotransmission may provide access to novel therapeutic strategies for the treatment of these diseases. Caged compounds with photoremovable groups represent molecular tools to investigate a biological target with high spatiotemporal resolution. Based on the crystal structure of the D-3 receptor in complex with eticlopride, we have developed caged D-2/D-3 receptor ligands by rational design. We initially found that eticlopride, a widely used D-2/D-3 receptor antagonist, was photolabile and therefore is not suitable for caging. Subtle structural modification of the pharmacophore led us to the photostable antagonist dechloroeticlopride, which was chemically transformed into caged ligands. Among those, the 2-nitrobenzyl derivative 4 (MG307) showed excellent photochemical stability, pharmacological behavior and decaging properties when interacting with dopamine receptor-expressing cells

Detection of DHCMT long-term metabolite glucuronides with LC-MSMS as an alternative approach to conventional GC-MSMS analysis

Dehydrochloromethyltestosterone (DHCMT) is one of the most detected illicit used anabolic–androgenic steroids in professional sports. Therefore, a fast and accurate analysis of this substance is of great importance for a constructive fight against doping abuse. The conventional method for the analysis of this drug, GC-MSMS, is very sensitive and selective but also very time- and resource-consuming. With the presented work, a new approach for simple detection with LC-HRMSMS without any sample preparation is introduced. The method is based on the direct analysis of two newly described phase-II metabolites of the DHCMT long-term metabolite 4-chloro-18-nor-17β-hydroxymethyl-17α-methyl-5β-androst-13-en-3α-ol (M3). LC-HRMSMS, GC-MSMS, fractionation and derivatization experiments are combined to identify and characterize for the first time two different glucuronide-acid conjugates of this metabolite in positive human urine samples. In addition, a third glucuronide metabolite was identified, however without isomeric structure determination. The detection of these metabolites is particularly interesting for confirmation analyses, as the method is rapid and requires little sample material

Detection of DHCMT long-term metabolite glucuronides with LC-MSMS as an alternative approach to conventional GC-MSMS analysis

Dehydrochloromethyltestosterone (DHCMT) is one of the most detected illicit used anabolic–androgenic steroids in professional sports. Therefore, a fast and accurate analysis of this substance is of great importance for a constructive fight against doping abuse. The conventional method for the analysis of this drug, GC-MSMS, is very sensitive and selective but also very time- and resource-consuming. With the presented work, a new approach for simple detection with LC-HRMSMS without any sample preparation is introduced. The method is based on the direct analysis of two newly described phase-II metabolites of the DHCMT long-term metabolite 4-chloro-18-nor-17β-hydroxymethyl-17α-methyl-5β-androst-13-en-3α-ol (M3). LC-HRMSMS, GC-MSMS, fractionation and derivatization experiments are combined to identify and characterize for the first time two different glucuronide-acid conjugates of this metabolite in positive human urine samples. In addition, a third glucuronide metabolite was identified, however without isomeric structure determination. The detection of these metabolites is particularly interesting for confirmation analyses, as the method is rapid and requires little sample material