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Background Lower respiratory tract infections are among the main causes of death. Although there are many respiratory viruses, diagnostic efforts are focused mainly on influenza. The Respiratory Viruses Network (RespVir) collects infection data, primarily from German university hospitals, for a high diversity of infections by respiratory pathogens. In this study, we computationally analysed a subset of the RespVir database, covering 217,150 samples tested for 17 different viral pathogens in the time span from 2010 to 2019. Methods We calculated the prevalence of 17 respiratory viruses, analysed their seasonality patterns using information-theoretic measures and agglomerative clustering, and analysed their propensity for dual infection using a new metric dubbed average coinfection exclusion score (ACES). Results After initial data pre-processing, we retained 206,814 samples, corresponding to 1,408,657 performed tests. We found that Influenza viruses were reported for almost the half of all infections and that they exhibited the highest degree of seasonality. Coinfections of viruses are frequent; the most prevalent coinfection was rhinovirus/bocavirus and most of the virus pairs had a positive ACES indicating a tendency to exclude each other regarding infection. Conclusions The analysis of respiratory viruses dynamics in monoinfection and coinfection contributes to the prevention, diagnostic, treatment, and development of new therapeutics. Data obtained from multiplex testing is fundamental for this analysis and should be prioritized over single pathogen testing.

Purpose Detection of SARS-CoV-2-infected pregnant women admitted to maternity units during a pandemic is crucial. In addition to the fact that pregnancy is a risk factor for severe COVID-19 and that medical surveillance has to be adjusted in infected women and their offspring, knowledge about infection status can provide the opportunity to protect other patients and healthcare workers against virus transmission. The aim of this prospective observational study was to determine the prevalence of SARS-CoV-2 infection among pregnant women in the hospital setting.Material and Methods All eligible pregnant women admitted to the nine participating hospitals in Franconia, Germany, from 2 June 2020 to 24 January 2021 were included. COVID-19-related symptoms, secondary diseases and pregnancy abnormalities were documented. SARS-CoV-2 RNA was detected by RT-PCR from nasopharyngeal swabs. The prevalence of acute SARS-CoV-2 infection was estimated by correcting the positive rate using the Rogan–Gladen method. The risk of infection for healthcare workers during delivery was estimated using a risk calculator.Results Of 2414 recruited pregnant women, six were newly diagnosed RT-PCR positive for SARS-CoV-2, which yielded a prevalence of SARS-CoV-2 infection of 0.26% (95% CI, 0.10 – 0.57%). Combining active room ventilation and wearing FFP2 masks showed an estimated reduction of risk of infection for healthcare workers in the delivery room to Conclusions The prevalence of newly diagnosed SARS-CoV-2 infection during pregnancy in this study is low. Nevertheless, a systematic screening in maternity units during pandemic situations is important to adjust hygienic and medical management. An adequate hygienic setting can minimise the calculated infection risk for medical healthcare workers during patientsʼ labour.

SESAME and a consortium of five Helmholtz Centers are designing and installing a state-of-the-art soft X-Ray undulator beamline at the SESAME light source in Amman, Jordan. Funding is provided by the Helmholtz Association over a four year project cycle that started in January 2019. This is an interim report covering the first 36 months of the project where the construction and installation has been almost completed and commissioning and characterization of the beamline is about to start. Additionally, seminars, workshops, and a training program are part of the project aimed at establishing a broad user community.

The HESEB (Helmholtz-SESAME soft X-ray beamline) project is an initiative by the Helmholtz Association of German Research Centers (HGF) to implement a new beamline at SESAME for scientific applications using soft X-ray spectroscopy techniques. The beamline design, procurement, and construction are being carried out by a consortium of the HGF centers DESY, FZJ, HZB, HZDR and KIT in collaboration with SESAME. The new beamline will be available for the SESAME user community within the four year project duration. It will allow for a large number of top-class scientific applications and provide new cooperation potentials with German and international research groups. The beamline is based on a variable-polarization undulator and a plane-grating monochromator covering an energy range 80 - 2000 eV. The optical design and the technical specifications have been completed and a call for tender procedure is ongoing. It is expected that the beamline will see first light in summer 2021. During the course of the project, the main goals to be achieved are (i) the construction and commissioning of the beamline at SESAME with a chamber for cultural heritage applications using spatially resolved soft X-ray fluorescence, (ii) the leveraging of additional contributions from the SESAME member countries to promote the build-up of international user consortia and to secure funding for experimental endstations and additional instrumentation, (iii) the training of SESAME staff at participating Helmholtz centers to enable reliable operation of the beamline by local staff, and (iv) the fostering of the establishment of a broad user community of HESEB from the SESAME member states through training, workshops, and schools. The first soft X-ray HESEB science workshop will take place in Istanbul beginning of 2020.

Mutations of the ribosomal protein S19 (RPS19) gene were recently identified in 10 patients with Diamond Blackfan anemia (DBA). To determine the prevalence of mutations in this gene in DBA and to begin to define the molecular basis for the observed variable clinical phenotype of this disorder, the genomic sequence of the 6 exons and the 5' untranslated region of the RPS19 gene was directly assessed in DBA index cases from 172 new families. Mutations affecting the coding sequence of RPS19 or splice sites were found in 34 cases (19.7%), whereas mutations in noncoding regions were found in 8 patients (4.6%). Mutations included nonsense, missense, splice sites, and frameshift mutations. A hot spot for missense mutations was identified between codons 52 and 62 of the RPS19 gene in a new sequence consensus motif W-[YFW]-[YF]-x-R-[AT]-A-[SA]-x-[AL]-R-[HRK]-[ILV]-Y. No correlation between the nature of mutations and the different patterns of clinical expression, including age at presentation, presence of malformations, and therapeutic outcome, could be documented. Moreover, RPS19 mutations were also found in some first-degree relatives presenting only with isolated high erythrocyte adenosine deaminase activity and/or macrocytosis. The lack of a consistent relationship between the nature of the mutations and the clinical phenotype implies that yet unidentified factors modulate the phenotypic expression of the primary genetic defect in families with RPS19 mutations.

Mathematical models are often used to formalize hypotheses on how a biochemical network operates. By selecting between competing models, different hypotheses can be compared. It is possible to estimate the evidence that data provides in support of one model over another. In a Bayesian framework, this is typically done by computing Bayes factors. When data is insufficiently informative to make a clear distinction, more data is required. Although the Bayesian model selection apparatus is suitable for selecting models, predicting distributions of Bayes factors is highly infeasible due to the computational complexity this involves. In this work, we propose searching for the experiment which optimally enables model selection by looking at predictive differences. We do so by simulating the posterior predictive distribution over new potential experiments. In many cases, distributions for single predictions typically show a large degree of overlap. The relations between the different prediction uncertainties depend on both the data and the model. Differences in these inter-prediction relations between competing models can be probed and used. In this work, we quantify differences in predictive distributions by means of the Jensen-Shannon divergence between predictive distributions belonging to competing models. The proposed method is evaluated by comparing its outcome to a predicted change in Bayes Factor upon simulating the experiment. Our simulations suggest that the Jensen-Shannon divergence between predictive densities is monotonically related to the increase in Bayes factor pointing toward the correct model. Therefore, it can be used to predict which experiments can effectively discriminate between models.

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