Teratogenic potential of nanoencapsulated vitamin A evaluated on an alternative model organism, the tunicate Ciona intestinalis

Nano-encapsulation is a technology used to pack substances in order to enhance their stability and bioavailability, but this packing may interact with living systems, causing unexpected toxicity. Vitamin A (vit A) is a substance that has received attention, because in developed countries, the increasing availability of supplements is leading to its excessive intake. This study aims to compare teratogenic effects caused by exposure to the traditional formulation of vit A versus nano-encapsulated vit A. We used ascidian embryos as an alternative model. Ascidians are marine organisms closely related to vertebrates that share with them a body plan and developmental programme, including the morphogenetic role of retinoic acid (RA). Our data showed that the adverse effects of exposure to the same concentration of the two formulations were different, suggesting that the nano-encapsulation increased the bioavailability of the molecule, which could be better absorbed and metabolised to RA, the effective teratogenic substance

Factors driving semi-aquatic predator occurrence in traditional cattle drinking pools : conservation issues

In several cases, human impact on water bodies and on their freshwater communities is detrimental, but in some cases the human activity may favour and enhance the biodiversity of small water bodies, as traditional cattle drinking pools. Despite their small size, small water bodies may constitute hot spot of biodiversity often representing the only lentic aquatic biotope in landscapes where superficial water lacks or flows in lotic environments like creeks and streams. Predators are good indicators of biodiversity in ponds and give information of food chain web complexity. In particular, semi-aquatic predators like amphibians and dragonflies may account for a substantial percentage of energy flow between aquatic and terrestrial ecosystems. In this study, we evaluated the conservation value of traditional cattle drinking pools building by assessing the factors determining the occurrence and distribution of the semiaquatic predators. From April to August 2015, we investigated 30 distinct pools recording several abiotic and biotic environmental variables. We detected 4 semi-aquatic predators: Salamandra salamandra larvae, Triturus carnifex, Aeshna sp. larvae and Libellula sp. larvae. Abiotic features played a major role in shaping the predator community that resulted linked to stable, with no dryness period, and large drinking pools. Invertebrate prey biomass was not particularly important, while vegetation cover and occurrence of unpalatable tadpoles were the most important biotic features of the pools. Our study provides novel evidence on the importance of cattle drinking pools management to preserve biodiversity especially in areas where traditional pastoral activity is disappearing

Exploring plenoptic properties of correlation imaging with chaotic light

In a setup illuminated by chaotic light, we consider different schemes that enable to perform imaging by measuring second-order intensity correlations. The most relevant feature of the proposed protocols is the ability to perform plenoptic imaging, namely to reconstruct the geometrical path of light propagating in the system, by imaging both the object and the focusing element. This property allows to encode, in a single data acquisition, both multi-perspective images of the scene and light distribution in different planes between the scene and the focusing element. We unveil the plenoptic property of three different setups, explore their refocusing potentialities and discuss their practical applications.Comment: 9 pages, 4 figure

The Mediterranean fishery management: A call for shifting the current paradigm from duplication to synergy

Independence of science and best available science are fundamental pillars of the UN-FAO code of conduct for responsible fisheries and are also applied to the European Union (EU) Common Fishery Policy (CFP), with the overarching objective being the sustainable exploitation of the fisheries resources. CFP is developed by DG MARE, the department of the European Commission responsible for EU policy on maritime affairs and fisheries, which has the Scientific, Technical and Economic Committee for Fisheries (STECF) as consultant body. In the Mediterranean and Black Sea, the General Fisheries Commission for the Mediterranean (FAO-GFCM), with its own Scientific Advisory Committee on Fisheries (GFCM-SAC), plays a critical role in fisheries governance, having the authority to adopt binding recommendations for fisheries conservation and management. During the last years, advice on the status of the main stocks in the Mediterranean and Black Sea has been provided both by GFCM-SAC and EU-STECF, often without a clear coordination and a lack of shared rules and practices. This has led in the past to: i) duplications of the advice on the status of the stocks thus adding confusion in the management process and, ii) a continuous managers’ interference in the scientific process by DG MARE officials hindering its transparency and independence. Thus, it is imperative that this stalemate is rapidly resolved and that the free role of science in Mediterranean fisheries assessment and management is urgently restored to assure the sustainable exploitation of Mediterranean marine resources in the future.En prens

Compositional Falsification of Cyber-Physical Systems with Machine Learning Components

Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small adversarial perturbations. This raises the question: can the output from learning components can lead to a failure of the entire CPS? In this work, we address this question by formulating it as a problem of falsifying signal temporal logic (STL) specifications for CPS with ML components. We propose a compositional falsification framework where a temporal logic falsifier and a machine learning analyzer cooperate with the aim of finding falsifying executions of the considered model. The efficacy of the proposed technique is shown on an automatic emergency braking system model with a perception component based on deep neural networks