Mounting evidence points to the soluble oligomers of amyloid β (Aβ) peptide as important neurotoxic species in Alzheimer's disease, causing synaptic dysfunction and neuronal injury, and finally leading to neuronal death. The mechanism of the Aβ peptide self-assembly is still under debate. Here, Aβ1-40 peptide oligomers were studied using mass spectrometry combined with ion mobility spectrometry, which allowed separation of the signals of numerous oligomers and measurement of their collisional cross-section values (Ω). For several oligomers, at least two different species of different Ω values were detected, indicating the presence of at least two families of conformers: compact and extended. The obtained results are rationalized by a set of molecular models of Aβ1-40 oligomer structure that provided a very good correlation between the experimental and theoretical Ω values, both for the compact and the extended forms. Our results indicate that mass spectrometry detects oligomeric species that are on-pathway in the process of fibril formation or decay, but also alternative structures which may represent off-pathway evolution of oligomers.

Eukaryotic translation initiation factor 4E (eIF4E) is essential for efficient protein synthesis in cap-dependent translation. The protein specifically binds the cap structure at the mRNA 5' terminus and facilitates the assembly of the mRNA with other initiation factors and the 40S ribosomal subunit. Phosphorylation of eIF4E is implicated in the regulation of the initiation step of translation. However, the molecular mechanism of this regulation still remains unclear. To address this problem, we have determined the binding affinities of eIF4E specifically mutated at position 209 or 159 for a series of novel mono- and dinucleotide cap analogues by a fluorometric time-synchronized titration method. A 1.5-3-fold reduction in the affinity of cap for the S209E mutant and a 1-2-fold increase in the affinity of cap for the S209K mutant, depending on the negative charge of phosphate chains, indicate that phosphorylation at Ser209 creates electrostatic repulsion between the protein and the negatively charged cap structure. The inhibition of the ability to bind cap analogues by the K159A mutant and its phosphorylated counterpart shows significant participation of Lys159 in the binding of the capped mRNA. Both structural modifications, phosphorylation and the replacement of lysine with alanine, result in an increase in the negative Gibbs free energy of association that is proportional to the length of the cap phosphate chain and additive, i.e., equal to the sum of the individual destabilizing changes of DeltaG degrees. The possible implication of these results for the mechanism of control of eIF4E by phosphorylation, especially for the "clamping model", is discussed.

Ribosome recruitment to the majority of eukaryotic mRNAs is facilitated by the interaction of the cap binding protein, eIF4E, with the mRNA 59 cap structure. eIF4E stimulates translation through its interaction with a scaffolding protein, eIF4G, which helps to recruit the ribosome. Metazoans also contain a homolog of eIF4E, termed 4EHP, which binds the cap structure, but not eIF4G, and thus cannot stimulate translation, but it instead inhibits the translation of only one known, and possibly subset mRNAs. To understand why 4EHP does not inhibit general translation, we studied the binding affinity of 4EHP for cap analogs using two methods: fluorescence titration and stopped-flow measurements. We show that 4EHP binds cap analogs m 7 GpppG and m 7 GTP with 30 and 100 lower affinity than eIF4E. Thus, 4EHP cannot compete with eIF4E for binding to the cap structure of most mRNAs.

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Fluorimetric Cu(ii) titrations of Alzheimer's disease (AD) Abeta40 peptide at various ammonium acetate concentrations demonstrated that ternary Cu(Abeta40)L complexes are formed with buffer components L, thereby providing a novel perspective on Cu(ii)-Abeta interactions.

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Potential regulation of use of artificial intelligence by business should minimize the risks for consumers and the society without impeding the possible benefits. To do so, we argue, the legal reaction should be grounded in an empirical analysis and proceed case-by-case, bottom-up, as a series of responses to concrete research questions. The ambition of this report has been to commence and facilitate that process. We extensively document and evaluate the market practice of the corporate use of AI, map the scholarly debates about (consumer) law and artificial intelligence, and present a list of twenty five research questions which, in our opinion, require attention of regulators and academia. The report is divided into four sections. The first explains our understanding of the concepts of "artificial intelligence" (a set of socio-technological practices enabled by machine learning and big data) and "consumer law" (various legal instruments concretizing the principles of the weaker party protection, non-discrimination, regulated autonomy and consumer privacy). The second section documents the ways in which the business uses artificial intelligence in seven sectors of the economy: finance and insurance, information services, energy and "smart solutions", retail, autonomous vehicles, healthcare and legal services. For each analyzed sector we study the gains for the businesses stemming from the deployment of AI, the potential gains, but also challenges for consumers, as well as third party effects. In the third section, we repeat the analysis through the lens of four general "uses" of AI by businesses in various sectors: knowledge generation, automated decision making, advertising and other commercial practices and personal digital assistants. Finally, in the fourth section, we present the questions which we believe should be addressed in the next stage of the research. We cluster them into: normative questions about regulatory goals, technological and governance questions about regulatory means, and theoretical questions about concepts and preconceptions.

Recent years have been tainted by market practices that continuously expose us, as consumers, to new risks and threats. We have become accustomed, and sometimes even resigned, to businesses monitoring our activities, examining our data, and even meddling with our choices. Artificial Intelligence (AI) is often depicted as a weapon in the hands of businesses and blamed for allowing this to happen. In this paper, we envision a paradigm shift, where AI technologies are brought to the side of consumers and their organizations, with the aim of building an efficient and effective counter-power. AI-powered tools can support a massive-scale automated analysis of textual and audiovisual data, as well as code, for the benefit of consumers and their organizations. This in turn can lead to a better oversight of business activities, help consumers exercise their rights, and enable the civil society to mitigate information overload. We discuss the societal, political, and technological challenges that stand before that vision.