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In this paper, we present an entity-centric infrastructure to manage legal documents, especially court judgments, based on the organization of a textual document repository and on the annotation of these documents to serve a variety of downstream tasks. Documents are pre-processed and then iteratively annotated using a set of NLP services that combine complementary approaches based on machine learning and syntactic rules. We present a framework that has been designed to be developed and maintained in a sustainable way, allowing for multiple services and uses of the annotated document repository and considering the scarcity of annotated data as an intrinsic challenge for its development. The design activity is the result of a cooperative project where a scientific team, institutional bodies, and companies appointed to implement the final system are involved in co-design activities. We describe experiments to demonstrate the feasibility of the solution and discuss the main challenges to scaling the system at a national level. In particular, we report the results we obtained in annotating data with different low-resource methods and with solutions designed to combine these approaches in a meaningful way. An essential aspect of the proposed solution is a human-in-the-loop approach to control the output of the annotation algorithms in agreement with the organizational processes in place in Italian courts. Based on these results we advocate for the feasibility of the proposed approach and discuss the challenges that must be addressed to ensure the scalability and robustness of the proposed solution.

Background: New precision medicine therapies are urgently required for glioblastoma (GBM). However, to date, efforts to subtype patients based on molecular profiles have failed to direct treatment strategies. We hypothesised that interrogation of the GBM tumour microenvironment (TME) and identification of novel TME-specific subtypes could inform new precision immunotherapy treatment strategies. Materials and methods: A refined and validated microenvironment cell population (MCP) counter method was applied to >800 GBM patient tumours (GBM-MCP-counter). Specifically, partition around medoids (PAM) clustering of GBM-MCP-counter scores in the GLIOTRAIN discovery cohort identified three novel patient clusters, uniquely characterised by TME composition, functional orientation markers and immune checkpoint proteins. Validation was carried out in three independent GBM-RNA-seq datasets. Neoantigen, mutational and gene ontology analysis identified mutations and uniquely altered pathways across subtypes. The longitudinal Glioma Longitudinal AnalySiS (GLASS) cohort and three immunotherapy clinical trial cohorts [treatment with neoadjuvant/adjuvant anti-programmed cell death protein 1 (PD-1) or PSVRIPO] were further interrogated to assess subtype alterations between primary and recurrent tumours and to assess the utility of TME classifiers as immunotherapy biomarkers. Results: TMEHigh tumours (30%) displayed elevated lymphocyte, myeloid cell immune checkpoint, programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 transcripts. TMEHigh/mesenchymal+ patients featured tertiary lymphoid structures. TMEMed (46%) tumours were enriched for endothelial cell gene expression profiles and displayed heterogeneous immune populations. TMELow (24%) tumours were manifest as an ‘immune-desert’ group. TME subtype transitions upon recurrence were identified in the longitudinal GLASS cohort. Assessment of GBM immunoth

Contains fulltext : 252001.pdf (Publisher’s version ) (Open Access), BACKGROUND: The Editorial Board of EJNMMI Radiopharmacy and Chemistry releases a biyearly highlight commentary to update the readership on trends in the field of radiopharmaceutical development. RESULTS: This commentary of highlights has resulted in 23 different topics selected by each member of the Editorial Board addressing a variety of aspects ranging from novel radiochemistry to first in man application of novel radiopharmaceuticals and also a contribution in relation to MRI-agents is included. CONCLUSION: Trends in (radio)chemistry and radiopharmacy are highlighted demonstrating the progress in the research field being the scope of EJNMMI Radiopharmacy and Chemistry.

Named entity extraction is a crucial task to support the population of Knowledge Bases (KBs) from documents written in natural language. However, in many application domains, these documents must be collected and processed incrementally to update the KB as more data are ingested. In some cases, quality concerns may even require human validation mechanisms along the process. While very recent work in the NLP community has discussed the importance of evaluating and benchmarking continuous entity extraction, it has proposed methods and datasets that avoid Named Entity Linking (NEL) as a component of the extraction process. In this paper, we advocate for batch-based incremental entity extraction methods that can exploit NEL with a background KB, detect mentions of entities that are not present in the KB yet (NIL mentions), and update the KB with the novel entities. Based on this assumption, we present a methodology to evaluate NEL-based incremental entity extraction, which can be applied to a “static” dataset for evaluating NEL into a dataset for evaluating incremental entity extraction. We apply this methodology to an existing benchmark for evaluating NEL algorithms, and evaluate an incremental extraction pipeline that orchestrates different strong state-of-the-art and baseline algorithms for the tasks involved in the extraction process, namely, NEL, NIL prediction, and NIL clustering. In presenting our experiments, we demonstrate the increased difficulty of the information extraction task in incremental settings and discuss the strengths of the available solutions as well as open challenges.

This paper describes a one-dimensional thermodynamic model developed using AVL BOOST with the objective of analysing the performance, combustion parameters and NOx emissions of a Euro III, six-cylinder turbocharged Cummins diesel engine. The model was validated against experimental data obtained from the same engine run at a constant speed of 2000 rpm at varying load conditions (full, three quarter (3Q) and half load) using low sulphur diesel fuel (D100), as well as fumigated ethanol at 10% (D90), 20% (D80) and 30% (D70) substitutions (by energy). The results for D100, D90, D80 and D70 were found to be in good agreement with the experimental data. The percentage variation for engine performance parameters such as: brake power (BP), indicated power (IP), indicated torque (IT) and mean effective pressure (MEP) for D100 have been found to be approximately in the range of -5% to 1.5% for all loads, whereas, the fuel energy variation was only 0.33% for all loads. With increasing ethanol fumigation, a rise in peak pressure of the cycle, more rapid initial heat release rate and a reduction in the NOx emissions were observed in this study.

© 2018 Combustion Institute. All Rights Reserved. This paper describes a one-dimensional thermodynamic model developed using AVL BOOST with the objective of analysing the performance, combustion parameters and NOx emissions of a Euro III, six-cylinder turbocharged Cummins diesel engine. The model was validated against experimental data obtained from the same engine run at a constant speed of 2000 rpm at varying load conditions (full, three quarter (3Q) and half load) using low sulphur diesel fuel (D100), as well as fumigated ethanol at 10% (D90), 20% (D80) and 30% (D70) substitutions (by energy). The results for D100, D90, D80 and D70 were found to be in good agreement with the experimental data. The percentage variation for engine performance parameters such as: brake power (BP), indicated power (IP), indicated torque (IT) and mean effective pressure (MEP) for D100 have been found to be approximately in the range of -5% to 1.5% for all loads, whereas, the fuel energy variation was only 0.33% for all loads. With increasing ethanol fumigation, a rise in peak pressure of the cycle, more rapid initial heat release rate and a reduction in the NOx emissions were observed in this study.

The so-called smart-drugs, plant-based substances used for centuries by diverse populations especially of Central America in strict ritualized shamanic context thanks to their psychotropic effects, from the nineties have had wide diffusion across Europe. In Italy smart-drugs business, by passing traditional clandestine labs and illicit channels, was possible by their availability in smart-shops appeared in Milan and Rome and moreover exploiting the ever-expanding internet system. Via the Internet avid surfers have found troublefree accessibility to smart-drugs at low cost with the advantage of difficulties in police control, anonymity and lack of regulations, too. In Italy, between 2001-2003, Postal Police has seized more than five hundred parcels containing suspicious botanical species and tablets sent after on-line purchases by a known Dutch Internet site to Italian buyers. The authors hereby present the results of toxicological analysis on these findings (1167), with evidence of active principles ascribable to different smart-drugs: salvinorine in Salvia divinorum; LSA (ergine) in seeds of Rivea corymbosa, Ipomea violacea, Argyreia nervosa, harmine and harmaline in Peganum harmala, mescaline in Lophophora Williamsii, psilocybin/psilocin in Copelandia Cyanescens, Psilocybe Tampanensis, Psilocybe Cubensis, Philosophers Stones, ephedrine and yohimbine in several pharmaceutical products. Considering smart-drugs an unknown hazard and a potential emerging drug of abuse we give a general overview of the possible psychoactive effects of the most widely used smart drugs in their various forms of intake and their possible harms on man. Furthermore we illustrate the most recent trade restrictions adopted in Italy to limit their diffusion via the Internet.