Multi-Scale Monitoring of Rupestrian Heritage: Methodological Approach and Application to a Case Study

Most of the artistic heritage in the Mediterranean basin is hosted in rupestrian hypogeum whose peculiarity is given by the presence of at least one open side, which makes them particularly sensitive to meteorological conditions. This makes mandatory the monitoring of both indoor and outdoor environmental parameters to analyze the cause–effect relationship between microclimatic inside and outside the hypogeum. The paper proposes a spatial and temporal multi-scale methodological approach applied to a rupestrian church in Matera, which hosts precious wall paintings, particularly vulnerable to the effects of environmental parameters. The approach is based on the analysis of data acquired by three platforms: indoor, close-range outdoor, and outdoor data from a meteorological station and weather forecast from the COSMO 5 model. The method allowed to characterize the relationships between the indoor and outdoor parameters at different spatial and temporal scales. The results showed a significant correlation between the parameters, thus opening new opportunities for the monitoring of the rupestrian heritage based on the use of data systematically available, such as those from meteorological stations and meteorological forecast

CMS Monte Carlo production in the WLCG computing Grid

Monte Carlo production in CMS has received a major boost in performance and scale since the past CHEP06 conference. The production system has been re-engineered in order to incorporate the experience gained in running the previous system and to integrate production with the new CMS event data model, data management system and data processing framework. The system is interfaced to the two major computing Grids used by CMS, the LHC Computing Grid (LCG) and the Open Science Grid (OSG). Operational experience and integration aspects of the new CMS Monte Carlo production system is presented together with an analysis of production statistics. The new system automatically handles job submission, resource monitoring, job queuing, job distribution according to the available resources, data merging, registration of data into the data bookkeeping, data location, data transfer and placement systems. Compared to the previous production system automation, reliability and performance have been considerably improved. A more efficient use of computing resources and a better handling of the inherent Grid unreliability have resulted in an increase of production scale by about an order of magnitude, capable of running in parallel at the order of ten thousand jobs and yielding more than two million events per day

Distributed Computing Grid Experiences in CMS

The CMS experiment is currently developing a computing system capable of serving, processing and archiving the large number of events that will be generated when the CMS detector starts taking data. During 2004 CMS undertook a large scale data challenge to demonstrate the ability of the CMS computing system to cope with a sustained data-taking rate equivalent to 25% of startup rate. Its goals were: to run CMS event reconstruction at CERN for a sustained period at 25 Hz input rate; to distribute the data to several regional centers; and enable data access at those centers for analysis. Grid middleware was utilized to help complete all aspects of the challenge. To continue to provide scalable access from anywhere in the world to the data, CMS is developing a layer of software that uses Grid tools to gain access to data and resources, and that aims to provide physicists with a user friendly interface for submitting their analysis jobs. This paper describes the data challenge experience with Grid infrastructure and the current development of the CMS analysis system

MRI analysis for Hippocampus segmentation on a distributed infrastructure

Medical image computing raises new challenges due to the scale and the complexity of the required analyses. Medical image databases are currently available to supply clinical diagnosis. For instance, it is possible to provide diagnostic information based on an imaging biomarker comparing a single case to the reference group (controls or patients with disease). At the same time many sophisticated and computationally intensive algorithms have been implemented to extract useful information from medical images. Many applications would take great advantage by using scientific workflow technology due to its design, rapid implementation and reuse. However this technology requires a distributed computing infrastructure (such as Grid or Cloud) to be executed efficiently. One of the most used workflow manager for medical image processing is the LONI pipeline (LP), a graphical workbench developed by the Laboratory of Neuro Imaging (http://pipeline.loni.usc.edu). In this article we present a general approach to submit and monitor workflows on distributed infrastructures using LONI Pipeline, including European Grid Infrastructure (EGI) and Torque-based batch farm. In this paper we implemented a complete segmentation pipeline in brain magnetic resonance imaging (MRI). It requires time-consuming and data-intensive processing and for which reducing the computing time is crucial to meet clinical practice constraints. The developed approach is based on web services and can be used for any medical imaging application

Optimization of Italian CMS Computing Centers via MIUR funded Research Projects

In 2012, 14 Italian Institutions participating LHC Experiments (10 in CMS) have won a grant from the Italian Ministry of Research (MIUR), to optimize Analysis activities and in general the Tier2/Tier3 infrastructure. A large range of activities is actively carried on: they cover data distribution over WAN, dynamic provisioning for both scheduled and interactive processing, design and development of tools for distributed data analysis, and tests on the porting of CMS software stack to new highly performing / low power architectures

N-Oleoyl-glycine reduces nicotine reward and withdrawal in mice.

Cigarette smokers with brain damage involving the insular cortex display cessation of tobacco smoking, suggesting that this region may contribute to nicotine addiction. In the present study, we speculated that molecules in the insular cortex that are sensitive to experimental traumatic brain injury (TBI) in mice might provide leads to ameliorate nicotine addiction. Using targeted lipidomics, we found that TBI elicited substantial increases of a largely uncharacterized lipid, N-acyl-glycine, N-oleoyl-glycine (OlGly), in the insular cortex of mice. We then evaluated whether intraperitoneal administration of OlGly would alter withdrawal responses in nicotine-dependent mice as well as the rewarding effects of nicotine, as assessed in the conditioned place preference paradigm (CPP). Systemic administration of OlGly reduced mecamylamine-precipitated withdrawal responses in nicotine-dependent mice and prevented nicotine CPP. However, OlGly did not affect morphine CPP, demonstrating a degree of selectivity. Our respective in vitro and in vivo observations that OlGly activated peroxisome proliferator-activated receptor alpha (PPAR-α) and the PPAR-α antagonist GW6471 prevented the OlGly-induced reduction of nicotine CPP in mice suggests that this lipid acts as a functional PPAR-α agonist to attenuate nicotine reward. These findings raise the possibility that the long chain fatty acid amide OlGly may possess efficacy in treating nicotine addiction

A Cloud-Based Framework for Machine Learning Workloads and Applications

[EN] In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models.This work was supported by the project DEEP-Hybrid-DataCloud ``Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud'' that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant 777435Lopez Garcia, A.; Marco De Lucas, J.; Antonacci, M.; Zu Castell, W.; David, M.; Hardt, M.; Lloret Iglesias, L.... (2020). A Cloud-Based Framework for Machine Learning Workloads and Applications. IEEE Access. 8:18681-18692. https://doi.org/10.1109/ACCESS.2020.2964386S1868118692

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

The interaction between alpha 7 nicotinic acetylcholine receptor and nuclear peroxisome proliferator-activated receptor-alpha represents a new antinociceptive signaling pathway in mice

Recently, alpha 7 nicotinic acetylcholine receptors (nAChRs), primarily activated by binding of orthosteric agonists, represent a target for anti-inflammatory and analgesic drug development. These receptors may also be modulated by positive allosteric modulators (PAMs), ago-allosteric ligands (ago-PAMs), and alpha 7-silent agonists. Activation of 00 nAChRs has been reported to increase the brain levels of endogenous ligands for nuclear peroxisome proliferator-activated receptors type-alpha (PPAR-alpha), palmitoylethanolamide (PEA) and oleoylethanolamide (OEA), in a Ca2+-dependent manner. Here, we investigated potential crosstalk between alpha 7 nAChR and PPAR-alpha, using the formalin test, a mouse model of tonic pain. Using pharmacological and genetic approaches, we found that PNU282987, a full alpha 7 agonist, attenuated formalin-induced nociceptive behavior in alpha 7 -dependent manner. Interestingly, the selective PPAR-alpha antagonist GW6471 blocked the antinociceptive effects of PNU282987, but did not alter the antinociceptive responses evoked by the alpha 7 nAChR PAM PNU120596, ago-PAM GAT107, and silent agonist NS6740. Moreover, GW6471 administered systemically or spinally, but not via the intraplantar surface of the formalin-injected paw blocked PNU282987-induced antinociception. Conversely, exogenous administration of the naturally occurring PPAR-alpha agonist PEA potentiated the antinociceptive effects of PNU282987. In contrast, the cannabinoid 031 antagonist rimonabant and the CB2 antagonist SR144528 failed to reverse the antinociceptive effects of PNU282987. These findings suggest that PPAR-alpha plays a key role in a putative antinociceptive alpha 7 nicotinic signaling pathway.United States Department of Health & Human Services National Institutes of Health (NIH) - USA - GM57481 - R01 CA206028United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Cancer Institute (NCI) - R01CA206028United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of General Medical Sciences (NIGMS) - R01GM057481United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute on Drug Abuse (NIDA) European Commission - T32DA00702