Lipoedema is a subcutaneous adipose tissue disease characterized by the increase in the amount and structure of fat mass (FM) in specific areas, causing pain and discomfort. 95% of patients fail to lose weight in the lipoedema areas. The study was conducted to evaluate body composition and general health status modification in a group of lipoedema patients (LIPPY) and a control group (CTRL) after four weeks of a modified Mediterranean diet therapy (mMeD). A total of 29 subjects were included in the data analysis, divided in two groups: 14 LIPPY and 15 CTRL. After the mMeD, both groups significantly decreased their weight and body mass index; the CTRL also showed a reduction of all the circumferences and all FM’s compartments. LIPPY showed a decrease of FM in upper and lower limbs. No significant differences in Δ% between the groups were observed for the lean mass (LM). In LIPPY, an increase in the patients’ ability to perform various daily physical activities related to the loss of arms’ and legs’ fat was observed. According to the European Quality of Life scale, the possibility for LIPPY subjects to perform simple daily activities with less fatigue, pain and anxiety is highlighted. Further long-term studies are recommended to confirm the mMeD as a good strategy for Lipoedema treatment.

The treatment of deep-seated tumours with electrons of very high energies (VHEE, 70–150 MeV) has already been explored in the past, suggesting that a dosimetric coverage comparable with state-of-the-art proton (PT) or photon radiotherapy (RT) could be achieved with a large (> 10) number of fields and high electron energy. The technical and economical challenges posed by the deployment of such beams in treatment centres, together with the expected small therapeutic gain, prevented the development of such technique. This scenario could radically change in the light of recent developments that occurred in the compact, high-gradient, electron acceleration technology and, additionally, of the experimental evidence of the sparing of organs at risk achieved in ultra-high dose rate irradiation, also referred to as FLASH. Electrons with the energy required to treat intracranial lesions could be provided, at dose rates compatible with what is needed to trigger the FLASH effect, by accelerators that are a few metres long, and the organ sparing could be exploited to significantly simplify the irradiation geometry, decreasing the number of fields needed to treat a patient. In this paper, the case of two patients affected by a chordoma and a meningioma, respectively, treated with protons in Trento (IT) is presented. The proton plans have been compared with VHEE plans and X-ray intensity-modulated radiotherapy (IMRT) plans. The VHEE plans were first evaluated in terms of physical dose distribution and then assuming that the FLASH regimen can be achieved. VHEE beams demonstrated their potential in obtaining plans that have comparable tumour coverage and organs at risk sparing when benchmarked against current state-of-the-art IMRT and PT. These results were obtained with a number of explored fields that was in the range between 3 and 7, consistent with what is routinely performed in IMRT and PT conventional irradiations. The FLASH regimen, in all cases, showed its potential in reducing damage to the organs placed nearby the target volume, allowing, particularly in the chordoma case where the irradiation geometry is more challenging, a better tumour coverage with respect to the conventional treatments.

Mergers and Acquisitions represent important forms of business deals, both because of the volumes involved in the transactions and because of the role of the innovation activity of companies. Nevertheless, Economic Complexity methods have not been applied to the study of this field. By considering the patent activity of about one thousand companies, we develop a method to predict future acquisitions by assuming that companies deal more frequently with technologically related ones. We address both the problem of predicting a pair of companies for a future deal and that of finding a target company given an acquirer. We compare different forecasting methodologies, including machine learning and network-based algorithms, showing that a simple angular distance with the addition of the industry sector information outperforms the other approaches. Finally, we present the Continuous Company Space, a two-dimensional representation of firms to visualize their technological proximity and possible deals. Companies and policymakers can use this approach to identify companies most likely to pursue deals or explore possible innovation strategies.

Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results. However, both the generation of graphs and the handling of large graphs still remain open challenges. This work aims to introduce techniques for generating large graphs and test the approach on a complex problem such as the calculation of dose distribution in oncological radiotherapy applications. To this end, we introduced a pooling technique (ReNN-Pool) capable of sampling nodes that are spatially uniform without computational requirements in both model training and inference. By construction, the ReNN-Pool also allows the definition of a symmetric un-pooling operation to recover the original dimensionality of the graphs. We also present a Variational AutoEncoder (VAE) for generating graphs, based on the defined pooling and un-pooling operations, which employs convolutional graph layers in both encoding and decoding phases. The performance of the model was tested on both the realistic use case of a cylindrical graph dataset for a radiotherapy application and the standard benchmark dataset sprite. Compared to other graph pooling techniques, ReNN-Pool proved to improve both performance and computational requirements.