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The German healthcare system is facing unprecedented challenges due to the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic. Palliative care for critically ill patients and their families was also severely compromised, especially during the first wave of the pandemic, in both inpatient and outpatient settings.

The paper is based on our experience in routine inpatient palliative care and partial results of astudy conducted as part of the collaborative project "National Strategy for Palliative Care in Pandemic Times (PallPan)". Based on our experience from the inpatient care of patients suffering from severe or life-limiting disease, best-practice examples for improving or maintaining care in the on-going pandemic are described.

Restrictive visitor regulations, communication barriers and insufficient possibilities to accompany dying patients or their grieving relatives continue to pose major challenges in general and specialized inpatient palliative care. In order to maintain high-quality palliative care, it is necessary to create structures that enable targeted therapy discussions and end-of-life care in the presence of relatives. Therefore, innovative communication methods like video calls or individualized exceptions from visitor restrictions are needed.

Adequate care for seriously ill and dying patients and their relatives must be guaranteed during the pandemic. Individual arrangements should be arranged and implemented. If available, earlier involvement of specialized palliative care teams can be beneficial.

Adequate care for seriously ill and dying patients and their relatives must be guaranteed during the pandemic. Individual arrangements should be arranged and implemented. If available, earlier involvement of specialized palliative care teams can be beneficial.The SARS-CoV-encoded papain-like cysteine protease (PLpro) plays crucial roles in viral replication and maturation processes. It is required to cleave the precursor polyproteins into functional proteins. Thus, it is considered to be a promising target for developing specific drugs. For rational optimization of hit compounds, information about the structure-activity relationship (SAR) is fundamental. Herein, we characterize isoindolines as a new class of PLpro inhibitors.The Corona pandemic has painfully taught us the threat of new pathogens in a globalized world and how vital modern vaccines are. Platform technologies play an important role in the discovery of new vaccines as reducing the time for the development dramatically - time that saves lives. Here, we present the protein Dodecin and how it may be utilized as a versatile platform technology to produce cheap and robust new vaccines for everyone in all parts of the world.The special nature, volume and broadness of biomedical literature pose barriers for automated classification methods. On the other hand, manually indexing is time-consuming, costly and error prone. We argue that current word embedding algorithms can be efficiently used to support the task of biomedical text classification even in a multilabel setting, with many distinct labels. The ontology representation of Medical Subject Headings provides machine-readable labels and specifies the dimensionality of the problem space. Both deep- and shallow network approaches are implemented. Predictions are determined by the similarity between extracted features from contextualized representations of abstracts and headings. The addition of a separate classifier for transfer learning is also proposed and evaluated. Large datasets of biomedical citations are harvested for their metadata and used for training and testing. These automated approaches are still far from entirely substituting human experts, yet they can be useful as a mechanism for validation and recommendation. Dataset balancing, distributed processing and training parallelization in GPUs, all play an important part regarding the effectiveness and performance of proposed methods.A critical understanding of digital technologies is an empowering competence for citizens of all ages. In this paper we introduce an open educational approach of artificial intelligence (AI) for everyone. Through a hybrid and participative MOOC we aim to develop a critical and creative perspective about the way AI is integrated in the different domains of our lives. We have built and now operate a MOOC in AI for all the citizens from 15 years old. The MOOC aims to help understanding AI foundations and applications, intended for a large public beyond the school domain, with more than 20,000 participants engaged in the MOOC after nine months. This study addresses the pedagogical methods for designing and evaluating the MOOC in AI. Danicopan Through this study we raise four questions regarding citizen education in AI Why (i.e., to which aim) sharing such citizen formation? What is the disciplinary knowledge to be shared? What are the competencies to develop? How can it be shared and evaluated? We finally share learning analytics, quantitative and qualitative evaluations and explain to which extent educational science research helps enlighten such large scale initiatives. The analysis of the MOOC in AI helps to identify that the main feedback related to AI is "fear", because AI is unknown and mysterious to the participants. After developing playful AI simulations, the AI mechanisms become familiar for the MOOC participants and they can overcome their misconception on AI to develop a more critical point of view. This contribution describes a K-12 AI educational project or initiatives of a considerable impact, via the formation of teachers and other educators.

The online version contains supplementary material available at 10.1007/s13218-021-00725-7.

The online version contains supplementary material available at 10.1007/s13218-021-00725-7.Corona Virus Disease 19 (COVID-19) firstly spread in China since December 2019. Then, it spread at a high rate around the world. Therefore, rapid diagnosis of COVID-19 has become a very hot research topic. One of the possible diagnostic tools is to use a deep convolution neural network (DCNN) to classify patient images. Chest X-ray is one of the most widely-used imaging techniques for classifying COVID-19 cases. This paper presents a proposed wireless communication and classification system for X-ray images to detect COVID-19 cases. Different modulation techniques are compared to select the most reliable one with less required bandwidth. The proposed DCNN architecture consists of deep feature extraction and classification layers. Firstly, the proposed DCNN hyper-parameters are adjusted in the training phase. Then, the tuned hyper-parameters are utilized in the testing phase. These hyper-parameters are the optimization algorithm, the learning rate, the mini-batch size and the number of epochs. From simulation results, the proposed scheme outperforms other related pre-trained networks.