Multiperson Utility

We approach the problem of preference aggregation by endowing both individuals and coalitions with partially-ordered or incomplete cardinal preferences. Consistency across preferences for coalitions comes in the form of the Extended Pareto Rule: if two disjoint coalitions A and B prefer x to y, then so does the coalition A*B. The Extended Pareto Rule has important consequences for the social aggregation of individual preferences. Restricting attention to the case of complete individual preferences, and assuming complete preferences for some pairs of agents (interpersonal comparisons of utility units), we discover that the Extended Pareto Rule imposes a "no arbitrage" condition in the terms of utility comparison between agents. Furthermore, if all the individuals and pairs have complete preferences and certain non-degeneracy conditions are met, then we witness the emergence of a complete preference ordering for coalitions of all sizes. The corresponding utilities are a weighted sum of individual utilities, with the n-1 independent weights obtained from the preferences of n-1 pairs forming a spanning tree in the group. Keywords: Preference aggregation, Incomplete preferences, Extended Pareto Rule.

Differentiating sexually aggressive and non-sexually aggressive child sexual abuse survivors: The role of adjustment, emotions, and cognitions

The present study examined the adjustment, emotional, and cognitive differences between sexually aggressive child sexual abuse (CSA) survivors and non-sexually aggressive CSA survivors. Research suggests that children who have experienced sexual abuse may have high levels of shame and/or guilt (Deblinger & Runyon, 2005). In addition, a relation has been reported between adjustment difficulties following CSA and a pessimistic attribution style (Feiring, Taska, & Chen, 2002). It was hypothesized that compared to non-sexually aggressive CSA survivors, sexually aggressive CSA survivors would have more adjustment difficulties as evidenced by higher levels of internalizing, externalizing, and trauma-related symptomology; would be more prone to shame and maladaptive guilt; and would have a pessimistic attribution style. Participants were 83 children (44 females and 39 males) ranging in age from 4-12 years. Participants were divided into 3 groups. The Sexually Aggressive group (SA) consisted of 32 children referred to a sexual assault crisis centre because of a history of CSA and were evidencing interpersonal sexual behaviour problems (SBP). The Non-Sexually Aggressive group (NSA) consisted of 26 children referred to a sexual assault crisis centre because of a history of CSA and were not reported to have been displaying interpersonal SBP. The Comparison group (COM) consisted of 25 children from the community, with no known history of CSA or SBP. Scenario-based measures were used to assess participant\u27s attribution style and shame- and guilt-proneness. Caregiver-report measures were used to assess participant\u27s response to trauma, internalizing and externalizing symptomology, and the presence and intensity of sexual behaviours. Results indicated that children in the SA group evidenced more adjustment difficulties including both global and trauma-related symptomology. Although group differences were not found with respect to attribution style and shame- and guilt-proneness, a significant correlation was found between maladaptive guilt and SSP scores. The results highlight a pattern of risk factors associated with sexual aggression following CSA

Connectivity loss in human dominated landscape: operational tools for the identification of suitable habitat patches and corridors on amphibian's population

International audienceLandscape connectivity is a key issue for biodiversity conservation. Many species have to refrain to move between scattered resources patches. This is particularly the case for the common frog, a widespread amphibian migrating between forest and aquatic habitats for breeding. Face to the growing need for maintaining connectivity between amphibians' habitat patches, the aim of this study is to provide a method based on habitat suitability modelling and graph theory to explore and analyze ecological networks. We first used the maximum entropy modelling with environmental variables based on forest patches distribution to predict habitat patches distribution. Then, with considerations about landscape permeability, we applied graph theory in order to highlight the main habitat patches influencing habitat availability and connectivity by the use of the software's Conefor Sensinode 2.2 and Guidos. The use of the JRC Forest/Non Forest European map for the characterisation of common frog terrestrial habitat distribution combined with the maximum entropy modelling gives promising results for the identification of habitat discontinuities within a regional perspective. This approach should provide an operational tool for the identification of the effects of landscape barriers and corridors on populations structure. Then, the method appears as a promising tool for landscape planning

Unmet need in the hyperlipidaemia population with high risk of cardiovascular disease: a targeted literature review of observational studies

BACKGROUND: The aim of this study was to examine recommended target levels of low-density lipoprotein cholesterol (LDL-C) for hyperlipidaemia patients at high risk (i.e., with two or more risk factors or coronary heart disease or its risk equivalents) for cardiovascular disease (CVD); to determine LDL-C targets recommended by guidelines, and to examine the proportions of patients who do not achieve targeted LDL-C levels in real-world studies. METHODS: Electronic databases were searched: Medline, Medline In-Process, Embase, BIOSIS, and the Cochrane Library (1 January 2005 to 31 December 2013). Guideline searches were limited to publications in the last 5 years. There were no geographical or language restrictions. RESULTS: Seventeen guidelines and 42 observational studies that reported on high-risk hyperlipidaemia patients were identified. The National Cholesterol Education Program-Adult Treatment Panel III\u27s LDL-C target levels were the most common guidelines used for patients with very high hyperlipidaemia. However, between 68 and 96 % of patients in the studies did not achieve an LDL-C goal/dL, except in one study conducted in China (16.9 %). In high-risk patients, 61.8 to 93.8 % did not achieve a target of/dL. Regarding common comorbidities, patients with concomitant CVD or diabetes were least likely to reach their target LDL-C goals. CONCLUSION: In patients with high risk for CVD, the majority of patients do not attain recommended LDL-C goals, highlighting worldwide suboptimal hyperlipidaemia management and missed opportunities for reduction of the patients CVD risk. Lipid-modifying management strategies need to be intensified

Epigenetic alterations involved in cancer stem cell reprogramming

Current hypotheses suggest that tumors originate from cells that carry out a process of 'malignant reprogramming' driven by genetic and epigenetic alterations. Multiples studies reported the existence of stem-cell-like cells that acquire the ability to self-renew and are able to generate the bulk of more differentiated cells that form the tumor. This population of cancer cells, called cancer stem cells (CSC), is responsible for sustaining the tumor growth and, under determined conditions, can disseminate and migrate to give rise to secondary tumors or metastases to distant organs. Furthermore, CSCs have shown to be more resistant to anti-tumor treatments than the non-stem cancer cells, suggesting that surviving CSCs could be responsible for tumor relapse after therapy. These important properties have raised the interest in understanding the mechanisms that govern the generation and maintenance of this special population of cells, considered to lie behind the on/off switches of gene expression patterns. In this review, we summarize the most relevant epigenetic alterations, from DNA methylation and histone modifications to the recently discovered miRNAs that contribute to the regulation of cancer stem cell features in tumor progression, metastasis and response to chemotherapy

Precision medicine based in epigenomics: the paradigm of carcinoma of unknown primary

Epigenetic alterations are a common hallmark of human cancer. Single epigenetic markers are starting to be incorporated into clinical practice; however, the translational use of these biomarkers has not been validated at the 'omics' level. The identification of the tissue of origin in patients with cancer of unknown primary (CUP) is an example of how epigenomics can be incorporated in clinical settings, addressing an unmet need in the diagnostic and clinical management of these patients. Despite the great diagnostic advances made in the past decade, the use of traditional diagnostic procedures only enables the tissue of origin to be determined in ∼30% of patients with CUP. Thus, development of molecularly guided diagnostic strategies has emerged to complement traditional procedures, thereby improving the clinical management of patients with CUP. In this Review, we present the latest data on strategies using epigenetics and other molecular biomarkers to guide therapeutic decisions involving patients with CUP, and we highlight areas warranting further research to engage the medical community in this unmet need

A Search-Based Testing Approach for Deep Reinforcement Learning Agents

Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during the last decade to solve various decision-making problems such as autonomous driving and robotics. However, these algorithms have faced great challenges when deployed in safety-critical environments since they often exhibit erroneous behaviors that can lead to potentially critical errors. One way to assess the safety of DRL agents is to test them to detect possible faults leading to critical failures during their execution. This raises the question of how we can efficiently test DRL policies to ensure their correctness and adherence to safety requirements. Most existing works on testing DRL agents use adversarial attacks that perturb states or actions of the agent. However, such attacks often lead to unrealistic states of the environment. Their main goal is to test the robustness of DRL agents rather than testing the compliance of agents' policies with respect to requirements. Due to the huge state space of DRL environments, the high cost of test execution, and the black-box nature of DRL algorithms, the exhaustive testing of DRL agents is impossible. In this paper, we propose a Search-based Testing Approach of Reinforcement Learning Agents (STARLA) to test the policy of a DRL agent by effectively searching for failing executions of the agent within a limited testing budget. We use machine learning models and a dedicated genetic algorithm to narrow the search towards faulty episodes. We apply STARLA on Deep-Q-Learning agents which are widely used as benchmarks and show that it significantly outperforms Random Testing by detecting more faults related to the agent's policy. We also investigate how to extract rules that characterize faulty episodes of the DRL agent using our search results. Such rules can be used to understand the conditions under which the agent fails and thus assess its deployment risks

Assessment of soil fungal diversity in different alpine tundra habitats by means of pyrosequencing

Abstract Studying fungal diversity is vital if we want to shed light on terrestrial ecosystem functioning. However, there is still poor understanding of fungal diversity and variation given that Fungi are highly diversified and that most of fungal species remain uncultured. In this study we explored diversity with 454 FLX sequencing technology by using the Internal Transcribed Spacer 1 (ITS1) as the fungal barcode marker in order to evaluate the effect of 11 environmental conditions on alpine soil fungal diversity, as well as the consistency of those results by taking into account rare or unidentified Molecular Operational Taxonomic Units (MOTUs). In total we obtained 205131 ITS1 reads corresponding to an estimated fungal gamma diversity of between 5100 and 12 000 MOTUs at a 98% similarity threshold when considering respectively only identified fungal and all MOTUs. Fungal beta-diversity patterns were significantly explained by the environmental conditions, and were very consistent for abundant/rare and fungal/unidentified MOTUs confirming the ecological significance of rare/unidentified MOTUs, and therefore the existence of a fungal rare biosphere. This study shows that a beta-diversity estimation based on pyrosequencing is robust enough to support ecological studies. Additionally, our results suggest that rare MOTUs harbour ecological Guillaume Lentendu and Lucie Zinger equally contributed to this paper. Electronic supplementary material The online version of this articl