From medical language processing to BioNLP domain

This paper presents the results of a terminological work on a reference corpus in the domain of Biomedicine. In particular, the research tends to analyse the use of certain terms in Biomedicine in order to verify their change over the time with the aim of retrieving from the net the very essence of documentation. The terminological sample contains words used in BioNLP and biomedicine and identifies which terms are passing from scientific publications to the daily press and which are rather reserved to scientific production. The final scope of this work is to determine how scientific dissemination to an ever larger part of the society enables a public of common citizens to approach communication on biomedical research and development; and its main source is a reference corpus made up of three main repositories from which information related to BioNLP and Biomedicine is extracted. The paper is divided in three sections: 1) an introduction dedicated to data extracted from scientific documentation; 2) the second section devoted to methodology and data description; 3) the third part containing a statistical representation of terms extracted from the archive: indexes and concordances allow to reflect on the use of certain terms in this field and give possible keys for having access to the extraction of knowledge in the digital era

A Terminological Survey on the Titles of the Seventh Framework Programme (FP7)

This paper focuses on the automatic extraction of domain-specific knowledge from the European Commission projects of the 7th Framework Programme, hereinafter referred as FP7. The study is divided in three parts: the first part introduces the work starting from the building up of a corpus containing the titles of European Projects of the whole FP7 in order to obtain a relevant terminological sample for the different domains; the second describes software and methods while the third part focuses on the evaluation of results. Finally, we conclude by suggesting possible directions for further development of a comparison between terminological extraction from FP7 and FP5/FP6

Oncogenomic Approaches in Exploring Gain of Function of Mutant p53

Cancer is caused by the spatial and temporal accumulation of alterations in the genome of a given cell. This leads to the deregulation of key signalling pathways that play a pivotal role in the control of cell proliferation and cell fate. The p53 tumor suppressor gene is the most frequent target in genetic alterations in human cancers. The primary selective advantage of such mutations is the elimination of cellular wild type p53 activity. In addition, many evidences in vitro and in vivo have demonstrated that at least certain mutant forms of p53 may possess a gain of function, whereby they contribute positively to cancer progression. The fine mapping and deciphering of specific cancer phenotypes is taking advantage of molecular-profiling studies based on genome-wide approaches. Currently, high-throughput methods such as array-based comparative genomic hybridization (CGH array), single nucleotide polymorphism array (SNP array), expression arrays and ChIP-on-chip arrays are available to study mutant p53-associated alterations in human cancers. Here we will mainly focus on the integration of the results raised through oncogenomic platforms that aim to shed light on the molecular mechanisms underlying mutant p53 gain of function activities and to provide useful information on the molecular stratification of tumor patients

Assessing changes in global fire regimes

PAGES, Past Global Changes, is funded by the Swiss Academy of Sciences and the Chinese Academy of Sciences and supported in kind by the University of Bern, Switzerland. Financial support was provided by the U.S. National Science Foundation award numbers 1916565, EAR-2011439, and EAR-2012123. Additional support was provided by the Utah Department of Natural Resources Watershed Restoration Initiative. SSS was supported by Brigham Young University Graduate Studies. MS was supported by National Science Centre, Poland (grant no. 2018/31/B/ST10/02498 and 2021/41/B/ST10/00060). JCA was supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 101026211. PF contributed within the framework of the FCT-funded project no. UIDB/04033/2020. SGAF acknowledges support from Trond Mohn Stiftelse (TMS) and University of Bergen for the startup grant ‘TMS2022STG03’. JMP participation in this research was supported by the Forest Research Centre, a research unit funded by Fundação para a Ciência e a Tecnologia I.P. (FCT), Portugal (UIDB/00239/2020). A.-LD acknowledge PAGES, PICS CNRS 06484 project, CNRS-INSU, Région Nouvelle-Aquitaine, University of Bordeaux DRI and INQUA for workshop support.Background The global human footprint has fundamentally altered wildfire regimes, creating serious consequences for human health, biodiversity, and climate. However, it remains difficult to project how long-term interactions among land use, management, and climate change will affect fire behavior, representing a key knowledge gap for sustainable management. We used expert assessment to combine opinions about past and future fire regimes from 99 wildfire researchers. We asked for quantitative and qualitative assessments of the frequency, type, and implications of fire regime change from the beginning of the Holocene through the year 2300. Results Respondents indicated some direct human influence on wildfire since at least ~ 12,000 years BP, though natural climate variability remained the dominant driver of fire regime change until around 5,000 years BP, for most study regions. Responses suggested a ten-fold increase in the frequency of fire regime change during the last 250 years compared with the rest of the Holocene, corresponding first with the intensification and extensification of land use and later with anthropogenic climate change. Looking to the future, fire regimes were predicted to intensify, with increases in frequency, severity, and size in all biomes except grassland ecosystems. Fire regimes showed different climate sensitivities across biomes, but the likelihood of fire regime change increased with higher warming scenarios for all biomes. Biodiversity, carbon storage, and other ecosystem services were predicted to decrease for most biomes under higher emission scenarios. We present recommendations for adaptation and mitigation under emerging fire regimes, while recognizing that management options are constrained under higher emission scenarios. Conclusion The influence of humans on wildfire regimes has increased over the last two centuries. The perspective gained from past fires should be considered in land and fire management strategies, but novel fire behavior is likely given the unprecedented human disruption of plant communities, climate, and other factors. Future fire regimes are likely to degrade key ecosystem services, unless climate change is aggressively mitigated. Expert assessment complements empirical data and modeling, providing a broader perspective of fire science to inform decision making and future research priorities.Peer reviewe

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

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

Voci dall'aldilà: lo spiritismo nella letteratura fra Otto e Novecento

Fu verso la metà del XIX secolo, parallelamente alla diffusione e al consolidamento del credo positivista, che si diffuse lo spiritismo, a seguito delle esperienze soprannaturali attestate dalle giovani sorelle Fox nel 1848 nella loro casa di Hydesville (New York). Malgrado fosse nato in antagonismo con il pensiero positivista e con le scienze sperimentali, questo movimento esoterico si appropriò del paradigma scientifico per indagare il destino dell'anima dopo la morte. Lo spiritismo ebbe importanti ripercussioni su varie figure di letterati spagnoli fra la fine del XIX secolo e inizi del XX, come Amalia Domingo Soler (1835-1909) ed Emilio Carrere (1881-1947). Una volta illustrato il movimento e il suo sviluppo in Spagna, la tesi si concentra sulla ricostruzione della vita e dell'opera dei due scrittori suddetti