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Mutations in human MPV17 cause a hepatocerebral form of mitochondrial DNA depletion syndrome (MDS) hallmarked by early-onset liver failure, leading to premature death. Liver transplantation and frequent feeding using slow-release carbohydrates are the only available therapies, although surviving patients eventually develop slowly progressive peripheral and central neuropathy. The physiological role of Mpv17, including its functional link to mitochondrial DNA (mtDNA) maintenance, is still unclear. We show here that Mpv17 is part of a high molecular weight complex of unknown composition, which is essential for mtDNA maintenance in critical tissues, i.e. liver, of a Mpv17 knockout mouse model. On a standard diet, Mpv17 -/- mouse shows hardly any symptom of liver dysfunction, but a ketogenic diet (KD) leads these animals to liver cirrhosis and failure. However, when expression of human MPV17 is carried out by adeno-associated virus (AAV)-mediated gene replacement, the Mpv17 knockout mice are able to reconstitute the Mpv17-containing supramolecular complex, restore liver mtDNA copy number and oxidative phosphorylation (OXPHOS) proficiency, and prevent liver failure induced by the KD. These results open new therapeutic perspectives for the treatment of MPV17-related liver-specific MDS. © The American Society of Gene & Cell Therapy.

Geographic relevance aims to assess the relevance of physical entities (e.g., shops and museums) in geographic space for a mobile user in a given context, thereby shifting the focus from the digital world (the realm of classical information retrieval) to the physical world. We study the elicitation of geographic relevance criteria by means of both a classical survey and an Amazon Mechanical Turk (a crowdsourcing platform) survey. This allows us to obtain three results: first, we gather a set of criteria and their relative importance; second, we gain a first insight on the differences between geographic relevance and classical relevance as commonly understoon in the IR field; and third we draw some considerations on the agreement, on the importance of specific criteria, among the participants to the classical and the crowdsourcing surveys.

The assumptions underlying the Probability Ranking Principle (PRP) have led to a number of alternative approaches that cater or compensate for the PRP’s limitations. All alternatives deviate from the PRP by incorporating dependencies. This results in a re-ranking that promotes or demotes documents depending upon their relationship with the documents that have been already ranked. In this paper, we compare and contrast the behaviour of state-of-the-art ranking strategies and principles. To do so, we tease out analytical relationships between the ranking approaches and we investigate the document kinematics to visualise the effects of the different approaches on document ranking.

Quantum-inspired models have recently attracted increasing attention in Information Retrieval. An intriguing characteristic of the mathematical framework of quantum theory is the presence of complex numbers. However, it is unclear what such numbers could or would actually represent or mean in Information Retrieval. The goal of this paper is to discuss the role of complex numbers within the context of Information Retrieval. First, we introduce how complex numbers are used in quantum probability theory. Then, we examine van Rijsbergen’s proposal of evoking complex valued representations of informations objects. We empirically show that such a representation is unlikely to be effective in practice (confuting its usefulness in Information Retrieval). We then explore alternative proposals which may be more successful at realising the power of complex numbers.

The aim of this paper is to investigate the role of emotion features in diversifying document rankings to improve the effectiveness of Information Retrieval (IR) systems. For this purpose, two approaches are proposed to consider emotion features for diversification, and they are empirically tested on the TREC 678 Interactive Track collection. The results show that emotion features are capable of enhancing retrieval effectiveness.

Two decades after its inception, Latent Semantic Analysis(LSA) has become part and parcel of every modern introduction to Information Retrieval. For any tool that matures so quickly, it is important to check its lore and limitations, or else stagnation will set in. We focus here on the three main aspects of LSA that are well accepted, and the gist of which can be summarized as follows: (1) that LSA recovers latent semantic factors underlying the document space, (2) that such can be accomplished through lossy compression of the document space by eliminating lexical noise, and (3) that the latter can best be achieved by Singular Value Decomposition. For each aspect we performed experiments analogous to those reported in the LSA literature and compared the evidence brought to bear in each case. On the negative side, we show that the above claims about LSA are much more limited than commonly believed. Even a simple example may show that LSA does not recover the optimal semantic factors as intended in the pedagogical example used in many LSA publications. Additionally, and remarkably deviating from LSA lore, LSA does not scale up well: the larger the document space, the more unlikely that LSA recovers an optimal set of semantic factors. On the positive side, we describe new algorithms to replace LSA (and more recent alternatives as pLSA, LDA, and kernel methods) by trading its l2 space for an l1 space, thereby guaranteeing an optimal set of semantic factors. These algorithms seem to salvage the spirit of LSA as we think it was initially conceived.