Effects of Dried Blood Spot Storage on Lipidomic Analysis

During the lipidomic analysis of red blood cell membranes, the distribution and percentage ratios of the fatty acids are measured. Since fatty acids are the key constituents of cell membranes, by evaluating their quantities it possible to understand the general health of the cells and to obtain health indicators of the whole organism. However, because the analysis is precise, it is necessary to ensure that the blood does not undergo significant variations between the point of collection and analysis. The composition of the blood may vary dramatically weeks after collection, hence, here an attempt is made to stabilize these complex matrixes using antioxidants deposited on the paper cards on which the blood itself is deposited

Starburst galaxies strike back: a multi-messenger analysis with Fermi-LAT and IceCube data

Starburst galaxies, which are known as "reservoirs" of high-energy cosmic-rays, can represent an important high-energy neutrino "factory" contributing to the diffuse neutrino flux observed by IceCube. In this paper, we revisit the constraints affecting the neutrino and gamma-ray hadronuclear emissions from this class of astrophysical objects. In particular, we go beyond the standard prototype-based approach leading to a simple power-law neutrino flux, and investigate a more realistic model based on a data-driven blending of spectral indexes, thereby capturing the observed changes in the properties of individual emitters. We then perform a multi-messenger analysis considering the extragalactic gamma-ray background (EGB) measured by Fermi-LAT and different IceCube data samples: the 7.5-year High-Energy Starting Events (HESE) and the 6-year high-energy cascade data. Along with starburst galaxies, we take into account the contributions from blazars and radio galaxies as well as the secondary gamma-rays from electromagnetic cascades. Remarkably, we find that, differently from the highly-constrained prototype scenario, the spectral index blending allows starburst galaxies to account for up to $40\%$ of the HESE events at $95.4\%$ CL, while satisfying the limit on the non-blazar EGB component. Moreover, values of $\mathcal{O}(100~\mathrm{PeV})$ for the maximal energy of accelerated cosmic-rays by supernovae remnants inside the starburst are disfavoured in our scenario. In broad terms, our analysis points out that a better modeling of astrophysical sources could alleviate the tension between neutrino and gamma-ray data interpretation.Comment: 20 pages, 15 figures. v2: updated to published versio

Gymnema sylvestre R. Br., an Indian medicinal herb: traditional uses, chemical composition, and biological activity

Gymnema sylvestre R. Br. is one of the most important medicinal plants that grows in tropical forests in India and South East Asia. Its active ingredients and extracts of leaves and roots are used in traditional medicine to treat various ailments and they are present in the market for pharmaceutical and parapharmaceutical products. Commercial products based on substances of plant origin that are generally connoted as natural have to be subjected to monitoring and evaluation by health authorities for their potential impacts on public health. The monitoring and evaluation of these products are critical because the boundary between a therapeutic action and a functional or healthy activity has not yet been defined in a clear and unambiguous way. Therefore, these products are considered borderline products, and they require careful and rigorous studies, in order to use them as complement and/or even replacement of synthetic drugs that are characterized by side effects and high economic costs. This review explores the traditional uses, chemical composition and biological activity of G. sylvestre extracts, providing a general framework on the most interesting extracts and what are the necessary studies for a complete definition of the range of activities

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

PaTre: a method for Paralogy Trees construction

Genes belonging to the same organism are called paralogs when they show a significant similarity in the sequences, even if they have a different biological function. It is an emergent biological paradigm that the families of paralogs in a genome derive from a mechanism of iterated gene duplication-with-modification. In order to investigate the evolution of organisms, it can be useful to infer the duplications that have taken place starting from an hypothetical original gene, and that have led to the current paralog genes family. This information can naturally be represented in a paralogy tree. Here we present a method, called PaTre, to generate a paralogy tree from a family of paralog genes. PaTre uses new techniques motivated by the specific application. The reliability of the inferential process is tested by means of a simulator that implements different hypotheses on the duplication-with-modification paradigm, and checked on real data

Expectations for the high-energy neutrino detection from starburst galaxies with KM3NeT/ARCA

Star-forming galaxies (SFGs) and starburst galaxies (SBGs) are extragalactic sources which could produce high-energy neutrinos. In principle, they could play a rather important role for explaining at least a sizeable part of IceCube's observations of astophysical neutrino. Using a recent theoretical model which implemented a blending of spectral indeces, we present the KM3NeT/ARCA sensitivities for such a diffuse flux from the startburst galaxies. In particular, we provide the 5-year differential sensitivity for the two building blocks of ARCA. We make use only of the track-like events in the range of 100 GeV - 10 PeV differentiate in 11 bins of energy. We show how the upcoming neutrino telescope could observe the diffuse SFG and SBG within 5 years of data taking. We found the minimum of the sensitivity at around 100 TeV, which is also the energy where the SBG contribution is expected to peak. This would not only constrain the multi-component fit of the observed astrophysical neutrino flux at that energy (100 TeV), but would also provide us a direct link between the star-forming activity in the reservoir environments and the hadronic emissions