Techno-species in the Becoming Towards a Relational Ontology of Multi-species Assemblages (ROMA).

Robots equipped with artificial intelligence pose a huge challenge to traditional ontological differentiations between the spheres of the human and the non-human. Drawing mainly from neo-animistic and perspectivist approaches in anthropology and science and technology studies, the paper explores the potential of new forms of interconnectedness and rhizomatic entanglements between humans and a world transcending the boundaries between species and material spheres. We argue that intelligent robots meet virtually all criteria Western biology came up with to define 'life' and that it ultimately makes sense to recognize them as a new species that is part of our social universe. Contrasting dualistic concepts of man and nature with a monistic approach, we show that traditional properties of life (agency, self-replication, etc.) may apply to artefacts and that, once we accept the idea that social relationships are ultimately open connections to matter of any kind, the seemingly strict boundaries between species and material spheres can no longer be sustained. Instead, we propose to include 'matter' and ideas into the sphere of the social as agents in their own right to form a relational ontology of multi-species assemblages (ROMA).

Psychological burden in patients with COVID-19 and their relatives 90 days after hospitalization: A prospective observational cohort study.

OBJECTIVE: COVID-19 causes psychological distress for patients and their relatives at short term. However, little research addressed the longer-term psychological outcomes in this population. Therefore, we aimed to prospectively assess clinically relevant psychological distress in hospitalized patients with COVID-19 and their relatives 90 days after hospital discharge. METHODS: This exploratory, prospective, observational cohort study included consecutive adult patients hospitalized in two Swiss tertiary-care hospitals between March and June 2020 for confirmed COVID-19 and their relatives. The primary outcome was psychological distress defined as clinically relevant symptoms of anxiety and/or depression measured with the Hospital Anxiety and Depression Scale (HADS) 90 days after discharge. RESULTS: Clinically relevant psychological distress 90 days after hospital discharge was present in 23/108 patients (21.3%) and 22/120 relatives (18.3%). For patients, risk and protective factors associated with clinically relevant psychological distress included sociodemographic, illness-related, psychosocial, and hospital-related factors. A model including these factors showed good discrimination, with an area under the receiver-operating characteristic curve (AUC) of 0.84. For relatives, relevant risk factors were illness-related, psychosocial, and hospital-related factors. Resilience was negatively associated with anxiety and depression in both patients and relatives and regarding PTSD in relatives only. CONCLUSION: COVID-19 is linked to clinically relevant psychological distress in a subgroup of patients and their relatives 90 days after hospitalization. If confirmed in an independent and larger patient cohort, knowledge about these potential risk and protective factors might help to develop preventive strategies.

Prevalence and factors associated with psychological burden in COVID-19 patients and their relatives: A prospective observational cohort study.

BACKGROUND: Due to the dramatic measures accompanying isolation and the general uncertainty and fear associated with COVID-19, patients and relatives may be at high risk for adverse psychological outcomes. Until now there has been limited research focusing on the prevalence of psychological distress and associated factors in COVID-19 patients and their relatives. The objective of our study was to assess psychological distress in COVID-19 patients and their relatives 30 days after hospital discharge. METHODS: In this prospective observational cohort study at two Swiss tertiary-care hospitals we included consecutive adult patients hospitalized between March and June 2020 for a proven COVID-19 and their relatives. Psychological distress was defined as symptoms of anxiety and/or depression measured with the Hospital Anxiety and Depression Scale (HADS), i.e., a score of ≥8 on the depression and/or anxiety subscale. We further evaluated symptoms of post-traumatic stress disorder (PTSD), defined as a score of ≥1.5 on the Impact of Event Scale-Revised (IES-R). RESULTS: Among 126 included patients, 24 (19.1%) had psychological distress and 10 (8.7%) had symptoms of PTSD 30 days after hospital discharge. In multivariate logistic regression analyses three factors were independently associated with psychological distress in patients: resilience (OR 0.82; 95%CI 0.71 to 0.94; p = 0.005), high levels of perceived stress (OR 1.21; 95%CI 1.06 to 1.38; p = 0.006) and low frequency of contact with relatives (OR 7.67; 95%CI 1.42 to 41.58; p = 0.018). The model showed good discrimination, with an area under the receiver-operating characteristic curve (AUC) of 0.92. Among 153 relatives, 35 (22.9%) showed symptoms of psychological distress, and 3 (2%) of PTSD. For relatives, resilience was negatively associated (OR 0.85; 95%CI 0.75 to 0.96; p = 0.007), whereas perceived overall burden caused by COVID-19 was positively associated with psychological distress (OR 1.72; 95%CI 1.31 to 2.25; p<0.001). The overall model also had good discrimination, with an AUC of 0.87. CONCLUSION: A relevant number of COVID-19 patients as well as their relatives exhibited psychological distress 30 days after hospital discharge. These results might aid in development of strategies to prevent psychological distress in COVID-19 patients and their relatives.

Visualizing fuzzy overlapping communities in networks.

An important feature of networks for many application domains is their community structure. This is because objects within the same community usually have at least one property in common. The investigation of community structure can therefore support the understanding of object attributes from the network topology alone. In real-world systems, objects may belong to several communities at the same time, i.e., communities can overlap. Analyzing fuzzy community memberships is essential to understand to what extent objects contribute to different communities and whether some communities are highly interconnected. We developed a visualization approach that is based on node-link diagrams and supports the investigation of fuzzy communities in weighted undirected graphs at different levels of detail. Starting with the network of communities, the user can continuously drill down to the network of individual nodes and finally analyze the membership distribution of nodes of interest. Our approach uses layout strategies and further visual mappings to graphically encode the fuzzy community memberships. The usefulness of our approach is illustrated by two case studies analyzing networks of different domains: social networking and biological interactions. The case studies showed that our layout and visualization approach helps investigate fuzzy overlapping communities. Fuzzy vertices as well as the different communities to which they belong can be easily identified based on node color and position.