Mesothelioma and thymic tumors: Treatment challenges in (outside) a network setting

The management of patients with mesothelioma and thymic malignancy requires continuous multidisciplinary expertise at any step of the disease. A dramatic improvement in our knowledge has occurred in the last few years, through the development of databases, translational research programs, and clinical trials. Access to innovative strategies represents a major challenge, as there is a lack of funding for clinical research in rare cancers and their rarity precludes the design of robust clinical trials that could lead to specific approval of drugs. In this context, patient-centered initiatives, such as the establishment of dedicated networks, are warranted. International societies, such as IMIG (International Mesothelioma Interest Group) and ITMIG (International Thymic Malignancy Interest Group) provide infrastructure for global collaboration, and there are many advantages to having strong regional groups working on the same issues. There may be regional differences in risk factors, susceptibility, management and outcomes. The ability to address questions both regionally as well as globally is ideal to develop a full understanding of mesothelioma and thymic malignancies. In Europe, through the integration of national networks with EURACAN, the collaboration with academic societies and international groups, the development of networks in thoracic oncology provides multiplex integration of clinical care and research, ultimately ensuring equal access to high quality care to all patients, with the opportunity of conducting high level clinical and translational research projects

The Estimation of the Effective Centre of Mass Energy in q-qbar-gamma Events from DELPHI

The photon radiation in the initial state lowers the energy available for the e$^+$e$^-$ collisions; this effect is particularly important at LEP2 energies (above the mass of the Z boson). Being aligned to the beam direction, such initial state radiation is mostly undetected. This article describes the procedure used by the DELPHI experiment at LEP to estimate the effective centre-of-mass energy in hadronic events collected at energies above the Z peak. Typical resolutions ranging from 2 to 3 GeV on the effective center-of-mass energy are achieved, depending on the event topology.Comment: 12 pages, 6 figure

Correlation Clustering with Adaptive Similarity Queries

In correlation clustering, we are givennobjects together with a binary similarityscore between each pair of them. The goal is to partition the objects into clustersso to minimise the disagreements with the scores. In this work we investigatecorrelation clustering as an active learning problem: each similarity score can belearned by making a query, and the goal is to minimise both the disagreementsand the total number of queries. On the one hand, we describe simple activelearning algorithms, which provably achieve an almost optimal trade-off whilegiving cluster recovery guarantees, and we test them on different datasets. On theother hand, we prove information-theoretical bounds on the number of queriesnecessary to guarantee a prescribed disagreement bound. These results give a richcharacterization of the trade-off between queries and clustering error

Resistance traits and AFLP characterization of diploid primitive tuber-bearing potatoes.

ISSN: 0925-986

Integrated use of multi-temporal multi-sensor and multiscale Remote Sensing data for the understanding of archaeological contexts: the case study of Metaponto, Basilicata

This paper is focused on the archaeological area of Metaponto (Μεταπόντιον) and its territory, located in southern Italy. The area played an important role for the agricultural economy and the traffic of goods and people, from the south of Italy towards the central regions, starting from the Neolithic period, and reaching the zenith with the Greek polis of Metaponto and its hinterland. The site is herein analyzed through an integrated use of several Earth observation and remote sensing technologies and ancillary data produced over the years by archaeologists and scholars. The aim was to identify new buried elements of archaeological interest, for the reconstruction of the historical-archaeological landscape. Through the combined use of optical and radar satellite images, high-resolution images obtained by Unmanned Aerial System (visible, multispectral, and thermal infrared), geophysical data, and archival data, it was possible to deepen the knowledge of the area, in particular the “Castrum” area, identifying new buried evidence (structures, roads, and elements of the ancient landscape)

How to manage massive spatiotemporal dataset from stationary and non-stationary sensors in commercial DBMS?

The growing diffusion of the latest information and communication technologies in different contexts allowed the constitution of enormous sensing networks that form the underlying texture of smart environments. The amount and the speed at which these environments produce and consume data are starting to challenge current spatial data management technologies. In this work, we report on our experience handling real-world spatiotemporal datasets: a stationary dataset referring to the parking monitoring system and a non-stationary dataset referring to a train-mounted railway monitoring system. In particular, we present the results of an empirical comparison of the retrieval performances achieved by three different off-the-shelf settings to manage spatiotemporal data, namely the well-established combination of PostgreSQL + PostGIS with standard indexing, a clustered version of the same setup, and then a combination of the basic setup with Timescale, a storage extension specialized in handling temporal data. Since the non-stationary dataset has put much pressure on the configurations above, we furtherly investigated the advantages achievable by combining the TSMS setup with state-of-the-art indexing techniques. Results showed that the standard indexing is by far outperformed by the other solutions, which have different trade-offs. This experience may help researchers and practitioners facing similar problems managing these types of data

Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept

Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. In this scenario, objective pain assessment is an open research challenge. This work introduces a framework for automatic pain assessment. The proposed method is based on a wearable biosignal platform to extract quantitative indicators of the patient pain experience, evaluated through a self-assessment report. Two preliminary case studies focused on the simultaneous acquisition of electrocardiography (ECG), electrodermal activity (EDA), and accelerometer signals are illustrated and discussed. The results demonstrate the feasibility of the approach, highlighting the potential of EDA in capturing skin conductance responses (SCR) related to pain events in chronic cancer pain. A weak correlation (R = 0.2) is found between SCR parameters and the standard deviation of the interbeat interval series (SDRR), selected as the Heart Rate Variability index. A statistically significant (p < 0.001) increase in both EDA signal and SDRR is detected in movement with respect to rest conditions (assessed by means of the accelerometer signals) in the case of motion-associated cancer pain, thus reflecting the relationship between motor dynamics, which trigger painful responses, and the subsequent activation of the autonomous nervous system. With the objective of integrating parameters obtained from biosignals to establish pain signatures within different clinical scenarios, the proposed framework proves to be a promising research approach to define pain signatures in different clinical contexts