Patatin-like phospholipase domain-containing protein 3 and Liver Disease: Opportunities to Unravel Mechanisms Underlying Statistical Associations

International audience

Caracterização estrutural e avaliação das atividades farmacológicas da fucana B extraída da alga Dictyota menstrualis

Seaweeds are a major source of biologically active compounds . In the extracellular matrix of these organisms are sulfated polysaccharides that functions as structural components preventing it against dehydration. The fraction 0.9 (FucB) rich in sulfated fucans obtained from brown seaweed Dictyota menstrualis was chemical characterized and evaluated for pharmacological activity by testing anticoagulant activity, stimulatory action on the synthesis of an antithrombotic heparan sulfate, antioxidant activity and its effects in cell proliferation. The main components were FucB carbohydrates (49.80 ± 0.10 %) and sulfate (42.30 ± 0.015 %), with phenolic compounds ( 3.86 ± 0.016 %) and low protein contamination ( 0.58 ± 0.001 % ) . FucB showed polydisperse profile and analysis of signals in the infrared at 1262, 1074 and 930 cm -1 and 840 assigned to S = O bonds sulfate esters , CO bond presence of 3,6- anhydrogalactose , β -D- galactose non- sulfated sulfate and the axial position of fucose C4 , respectively. FucB exhibited moderate anticoagulant activity , the polysaccharides prolonged time (aPTT ) 200 ug ( > 90s ) partial thromboplastin FucB no effect on prothrombin time (PT), which corresponds to the extrinsic pathway of coagulation was observed. This stimulation promoted fraction of about 3.6 times the synthesis of heparan sulfate (HS) by endothelial cells of the rabbit aorta ( RAEC ) in culture compared with cells not treated with FucB . This has also been shown to compete for the binding site with heparin. The rich fraction sulfated fucans exhibited strong antioxidant activity assays on total antioxidant (109.7 and 89.5 % compared with BHT and ascorbic acid standards ) , reducing power ( 71 % compared to ascorbic acid ) and ferric chelation ( 71 , comparing with 5 % ascorbic acid). The fraction of algae showed cytostatic activity on the RAEC cells revealed that the increase of the synthesis of heparan sulfate is not related to proliferation. FucB showed antiproliferative action on cell lines modified as Hela and Hep G2 by MTT assay . These results suggest that FucB Dictyota menstrualis have anticoagulant , antithrombotic , antioxidant potential as well as a possible antitumor action, promoting the stimulation of the synthesis of antithrombotic HS by endothelial cells and is useful in the prevention of thrombosis, also due to its inhibitory action on species reactive oxygen ( ROS ) in some in vitro systems , being involved in promoting a hypercoagulable state

Machine learning as a service for high energy physics (MLaaS4HEP): a service for ML-based data analyses

With the CERN LHC program underway, there has been an acceleration of data growth in the High Energy Physics (HEP) field and the usage of Machine Learning (ML) in HEP will be critical during the HL-LHC program when the data that will be produced will reach the exascale. ML techniques have been successfully used in many areas of HEP nevertheless, the development of a ML project and its implementation for production use is a highly time-consuming task and requires specific skills. Complicating this scenario is the fact that HEP data is stored in ROOT data format, which is mostly unknown outside of the HEP community. The work presented in this thesis is focused on the development of a ML as a Service (MLaaS) solution for HEP, aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTP calls. These pipelines are executed by using the MLaaS4HEP framework, which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. Such a solution provides HEP users non-expert in ML with a tool that allows them to apply ML techniques in their analyses in a streamlined manner. Over the years the MLaaS4HEP framework has been developed, validated, and tested and new features have been added. A first MLaaS solution has been developed by automatizing the deployment of a platform equipped with the MLaaS4HEP framework. Then, a service with APIs has been developed, so that a user after being authenticated and authorized can submit MLaaS4HEP workflows producing trained ML models ready for the inference phase. A working prototype of this service is currently running on a virtual machine of INFN-Cloud and is compliant to be added to the INFN Cloud portfolio of services.

Green approach to Palladium Cross-Coupling reactions and development of new methodologies based on highly abundant metals

Transition metal catalyzed cross-coupling reactions represent among the most versatile and useful tools in organic synthesis for the carbon-carbon (C-C) bond formation and have a prominent role in both the academic and pharmaceutical segments. Among them, palladium catalyzed cross-coupling reactions are currently the most versatile. In this thesis, the applications, impact and development of green palladium cross-coupling reactions are discussed. Specifically, we discuss the translation of the Twelve Principles of Green Chemistry and their applications in pharmaceutical organometallic chemistry to stimulate the development of cost-effective and sustainable catalytic processes for the synthesis of active pharmaceutical ingredients (API). The Heck-Cassar-Sonogashira (HCS) and the Suzuki-Miyaura (SM) protocols, using HEP/H2O as green mixture and sulfonated phosphine ligands, allowed to recycle and recover the catalyst, always guaranteeing high yields and fast conversion under mild conditions, with aryl iodides, bromides, triflates and chlorides. No catalyst leakage or metal contamination of the final product were observed during the HCS and SM reactions, respecting the very low limits for metal impurities in medicines established by the International Conference of Harmonization Guidelines Q3D (ICH Q3D). In addition, a deep understanding of the reaction mechanism is very important if the final target is to develop efficient protocols that can be applied at industrial level. Experimental and theoretical studies pointed out the presence of two catalytic cycles depending on the counterion, shedding light on the role of base in catalyst reduction and acetylene coordination in the HCS coupling. Finally, the development of a cross-coupling reaction to form aryldifluoronitriles in the presence of copper is discussed, highlighting the importance of inserting fluorine atoms within biological structures and the use of readily available metals such as copper as an alternative to palladium.

Machine learning "as a service" for high energy physics (MLaaS4HEP): evolution of a framework for ML-based physics analyses

The scientific success of the LHC experiments at CERN highly depends on the availability of computing resources which efficiently store, process, and analyse the amount of data collected every year. This is ensured by the Worldwide LHC Computing Grid infrastructure that connect computing centres distributed all over the world with high performance network. LHC has an ambitious experimental program for the coming years, which includes large investments and improvements both for the hardware of the detectors and for the software and computing systems, in order to deal with the huge increase in the event rate expected from the High Luminosity LHC (HL-LHC) phase and consequently with the huge amount of data that will be produced. Since few years the role of Artificial Intelligence has become relevant in the High Energy Physics (HEP) world. Machine Learning (ML) and Deep Learning algorithms have been successfully used in many areas of HEP, like online and offline reconstruction programs, detector simulation, object reconstruction, identification, Monte Carlo generation, and surely they will be crucial in the HL-LHC phase. This thesis aims at contributing to a CMS R&D project, regarding a ML "as a Service" solution for HEP needs (MLaaS4HEP). It consists in a data-service able to perform an entire ML pipeline (in terms of reading data, processing data, training ML models, serving predictions) in a completely model-agnostic fashion, directly using ROOT files of arbitrary size from local or distributed data sources. This framework has been updated adding new features in the data preprocessing phase, allowing more flexibility to the user. Since the MLaaS4HEP framework is experiment agnostic, the ATLAS Higgs Boson ML challenge has been chosen as physics use case, with the aim to test MLaaS4HEP and the contribution done with this work.