Recent guidelines suggest that duration of antibiotic therapy for hospitalized patients with community-acquired pneumonia (CAP) can be reduced by individualising treatment based on patient's clinical response. However, the degree of application of this principle in clinical practice is unknown.Duration of therapy was analysed in patients identified from the Community-Acquired Pneumonia Organization database and evaluated with respect to severity of the disease on admission and time to clinical stability (TCS).Among the 2,003 patients enrolled, mean duration of total antibiotic therapy was 11 days. Neither the pneumonia severity index (r²= 0.005) nor the CRB-65 (r²= 0.004) scores were related to total duration of therapy. Duration of intravenous antibiotic therapy was related to TCS (r²= 0.198). Conversely, TCS was not related to duration of either oral (r²= 0.014) or total (r²= 0.02) antibiotic therapy. Neither TCS nor other characteristics were found to be significantly associated with duration of total therapy by logistic regression analysis (r²<0.09).The individualised approach suggested by recent guidelines has not been adopted in current clinical practice. Duration of therapy is not influenced by either the severity of disease at the time of hospitalisation or the clinical response to therapy.

Link prediction in Online Social Networks—OSNs—has been the focus of numerous studies in the machine learning community. A successful machine learning-based solution for this task needs to (i) leverage global and local properties of the graph structure surrounding links; (ii) leverage the content produced by OSN users; and (iii) allow their representations to change over time, as thousands of new links between users and new content like textual posts, comments, images and videos are created/uploaded every month. Current works have successfully leveraged the structural information but only a few have also taken into account the textual content and/or the dynamicity of network structure and node attributes. In this paper, we propose a methodology based on temporal graph neural networks to handle the challenges described above. To understand the impact of textual content on this task, we provide a novel pipeline to include textual information alongside the structural one with the usage of BERT language models, dense preprocessing layers, and an effective post-processing decoder. We conducted the evaluation on a novel dataset gathered from an emerging blockchain-based online social network, using a live-update setting that takes into account the evolving nature of data and models. The dataset serves as a useful testing ground for link prediction evaluation because it provides high-resolution temporal information on link creation and textual content, characteristics hard to find in current benchmark datasets. Our results show that temporal graph learning is a promising solution for dynamic link prediction with text. Indeed, combining textual features and dynamic Graph Neural Networks—GNNs—leads to the best performances over time. On average, the textual content can enhance the performance of a dynamic GNN by 3.1% and, as the collection of documents increases in size over time, help even models that do not consider the structural information of the network.

The thermodynamic behavior of the carbon schwarzite fcc $({\mathrm{C}}_{36}{)}_2$ has been studied by tight-binding molecular-dynamics simulations at constant temperature and pressure. The system is totally ${\mathrm{sp}}^2$ bonded and it can be represented as a folded graphite sheet made by hexagonal and heptagonal rings. The structure has a density $\rho$ as low as $\rho =1.05\mathrm{}{\mathrm{g}\mathrm{r}/\mathrm{c}\mathrm{m}}^3$ at $T=300\mathrm{}$ K and a bulk modulus B, evaluated by measuring the volume variation upon application of an external hydrostatic load, as large as $B=0.70\mathrm{}$ Mbar. At $T\sim 4000$ K, the structure unfolds via a topological transformation, which brings the value of the topological connectivity of the structure $k_c$ from $k_c=19\mathrm{}$ (for a cell including four molecules) to $k_c=3\mathrm{}$ (which is the value of a graphite sheet). The behavior of the structural, dynamical, and electronic-structure properties has been evaluated in the whole temperature range, below and above the temperature of the topological transition.