[Long Term Evaluation of the Implementation of the Safewards Model - Results of a Follow-Up-Study Among Patients and Staff in Acute Psychiatric Wards]. / Langzeitevaluation der Implementierung des Safewards-Modells ­ Ergebnisse einer Follow-Up-Erhebung unter Patient*innen und Mitarbeitenden der Akutpsychiatrie.

OBJECTIVE: Evaluation of long-term effects of the implementation of the Safewards Model (SM) among staff and patients in acute psychiatry in Germany. METHOD: Assessment of ward atmosphere, job satisfaction, fidelity, and coercive interventions in 2 locked wards directly before and 15 months after implementation of the SM. RESULTS: Ward atmosphere was assessed significantly better after implementation, job satisfaction was still above-average at both times, coercive interventions declined significantly in one ward, fidelity and degree of implementation were still high. CONCLUSIONS: The implementing of the SM in locked wards in acute psychiatry can also have positive effects in long run.

Comparing the impact of vaccination strategies on the spread of COVID-19, including a novel household-targeted vaccination strategy.

With limited availability of vaccines, an efficient use of the limited supply of vaccines in order to achieve herd immunity will be an important tool to combat the wide-spread prevalence of COVID-19. Here, we compare a selection of strategies for vaccine distribution, including a novel targeted vaccination approach (EHR) that provides a noticeable increase in vaccine impact on disease spread compared to age-prioritized and random selection vaccination schemes. Using high-fidelity individual-based computer simulations with Oslo, Norway as an example, we find that for a community reproductive number in a setting where the base pre-vaccination reproduction number R = 2.1 without population immunity, the EHR method reaches herd immunity at 48% of the population vaccinated with 90% efficiency, whereas the common age-prioritized approach needs 89%, and a population-wide random selection approach requires 61%. We find that age-based strategies have a substantially weaker impact on epidemic spread and struggle to achieve herd immunity under the majority of conditions. Furthermore, the vaccination of minors is essential to achieving herd immunity, even for ideal vaccines providing 100% protection.

Complex Network Analysis in Microbial Systems: Theory and Examples.

A central driver for the field of systems biology is to develop an understanding of how interactions between components affect the functioning of a system as a whole. Network analysis is an approach that is uniquely suited to uncover patterns and organizing principles in a wide variety of complex systems. In this chapter, we will give a detailed description of basic concepts for characterizing empirical networks, frequently used random network models, and how to compute properties of networks using Python packages. We will demonstrate the application of network analysis by investigating several biological networks.

Transcriptomic Response Analysis of Escherichia coli to Palladium Stress.

Palladium (Pd), due to its unique catalytic properties, is an industrially important heavy metal especially in the form of nanoparticles. It has a wide range of applications from automobile catalytic converters to the pharmaceutical production of morphine. Bacteria have been used to biologically produce Pd nanoparticles as a new environmentally friendly alternative to the currently used energy-intensive and toxic physicochemical methods. Heavy metals, including Pd, are toxic to bacterial cells and cause general and oxidative stress that hinders the use of bacteria to produce Pd nanoparticles efficiently. In this study, we show in detail the Pd stress-related effects on E. coli. Pd stress effects were measured as changes in the transcriptome through RNA-Seq after 10 min of exposure to 100 µM sodium tetrachloropalladate (II). We found that 709 out of 3,898 genes were differentially expressed, with 58% of them being up-regulated and 42% of them being down-regulated. Pd was found to induce several common heavy metal stress-related effects but interestingly, Pd causes unique effects too. Our data suggests that Pd disrupts the homeostasis of Fe, Zn, and Cu cellular pools. In addition, the expression of inorganic ion transporters in E. coli was found to be massively modulated due to Pd intoxication, with 17 out of 31 systems being affected. Moreover, the expression of several carbohydrate, amino acid, and nucleotide transport and metabolism genes was vastly changed. These results bring us one step closer to the generation of genetically engineered E. coli strains with enhanced capabilities for Pd nanoparticles synthesis.

Containing pandemics through targeted testing of households.

BACKGROUND: While invasive social distancing measures have proven efficient to control the spread of pandemics failing wide-scale deployment of vaccines, they carry vast societal costs. The development of a diagnostic methodology for identifying COVID-19 infection through simple testing was a reality only a few weeks after the novel virus was officially announced. Thus, we were interested in exploring the ability of regular testing of non-symptomatic people to reduce cases and thereby offer a non-pharmaceutical tool for controlling the spread of a pandemic. METHODS: We developed a data-driven individual-based epidemiological network model in order to investigate epidemic countermeasures. This models is based on high-resolution demographic data for each municipality in Norway, and each person in the model is subject to Susceptible-Exposed-Infectious-Recovered (SEIR) dynamics. The model was calibrated against hospitalization data in Oslo, Norway, a city with a population of 700k which we have used as the simulations focus. RESULTS: Finding that large households function as hubs for the propagation of COVID-19, we assess the intervention efficiency of targeted pooled household testing (TPHT) repeatedly. For an outbreak with reproductive number R=1.4, we find that weekly TPHT of the 25% largest households brings R below unity. For the case of R=1.2, our results suggest that TPHT with the largest 25% of households every three days in an urban area is as effective as a lockdown in curbing the outbreak. Our investigations of different disease parameters suggest that these results are markedly improved for disease variants that more easily infect young people, and when compliance with self-isolation rules is less than perfect among suspected symptomatic cases. These results are quite robust to changes in the testing frequency, city size, and the household-size distribution. Our results are robust even with only 50% of households willing to participate in TPHT, provided the total number of tests stay unchanged. CONCLUSIONS: Pooled and targeted household testing appears to be a powerful non-pharmaceutical alternative to more invasive social-distancing and lock-down measures as a localized early response to contain epidemic outbreaks.

High-quality genome-scale metabolic model of Aurantiochytrium sp. T66.

The long-chain, ω-3 polyunsaturated fatty acids (PUFAs) (e.g., eicosapentaenoic acid [EPA] and docosahexaenoic acid [DHA]), are essential for humans and animals, including marine fish species. Presently, the primary source of these PUFAs is fish oils. As the global production of fish oils appears to be reaching its limits, alternative sources of high-quality ω-3 PUFAs is paramount to support the growing aquaculture industry. Thraustochytrids are a group of heterotrophic protists with the capability to synthesize and accrue large amounts of DHA. Thus, the thraustochytrids are prime candidates to solve the increasing demand for ω-3 PUFAs using microbial cell factories. However, a systems-level understanding of their metabolic shift from cellular growth into lipid accumulation is, to a large extent, unclear. Here, we reconstructed a high-quality genome-scale metabolic model of the thraustochytrid Aurantiochytrium sp. T66 termed iVS1191. Through iterative rounds of model refinement and extensive manual curation, we significantly enhanced the metabolic scope and coverage of the reconstruction from that of previously published models, making considerable improvements with stoichiometric consistency, metabolic connectivity, and model annotations. We show that iVS1191 is highly consistent with experimental growth data, reproducing in vivo growth phenotypes as well as specific growth rates on minimal carbon media. The availability of iVS1191 provides a solid framework for further developing our understanding of T66's metabolic properties, as well as exploring metabolic engineering and process-optimization strategies in silico for increased ω-3 PUFA production.

High Throughput Semiquantitative UHPSFC-MS/MS Lipid Profiling and Lipid Class Determination.

High throughput and high-resolution lipid analyses are important for many biological model systems and research questions. This comprises both monitoring at the individual lipid species level and broad lipid classes. Here, we present a nontarget semiquantitative lipidomics workflow based on ultrahigh performance supercritical fluid chromatography (UHPSFC)-mass spectrometry (MS). The optimized chromatographic conditions enable the base-line separation of both nonpolar and polar classes in a single 7-minute run. Ionization efficiencies of lipid classes vary 10folds in magnitude and great care must be taken in a direct interpretation of raw data. Therefore, the inclusion of internal standards or experimentally determined Response factors (RF) are highly recommended for the conversion of raw abundances into (semi) quantitative data. We have deliberately developed an algorithm for automatic semiquantification of lipid classes by RF. The workflow was tested and validated using a bovine liver extract with satisfactory results. The RF corrected data provide a more representative relative lipid class determination, but also the interpretation of individual lipid species should be performed on RF corrected data. In addition, semiquantification can be improved by using internal or also external standards when more accurate quantitative data are of interest but this requires validation for all new sample types. The workflow established greatly extends the potential of nontarget UHPSFC-MS/MS based analysis.

Corrigendum: Preventing and Reducing Coercive Measures-An Evaluation of the Implementation of the Safewards Model in Two Locked Wards in Germany.

[This corrects the article DOI: 10.3389/fpsyt.2019.00340.].

[Changes on Ward Atmosphere and Job Satisfaction after Implementation of the Safewards Model in Two Locked Acute Psychiatric Wards - A Multi-Perspective Evaluation]. / Veränderungen des Stationsklimas und der Arbeitszufriedenheit nach Einführung des Safewards-Modells auf zwei geschützten akutpsychiatrischen Stationen ­ Eine multiperspektivische Evaluation.

OBJECTIVE: Evaluation of the changes of ward atmosphere and job satisfaction after the implementation of the Safewards model in acute psychiatry in Germany. METHOD: A multi-perspective pre-post study design was conducted in two locked wards among patients (n = 80) and staff (n = 88) before and after the implementation of the Safewards model over a period of 12 months. RESULTS: After the implementation of the Safewards model, ward atmosphere and job satisfaction improved. Both correlated positively amongst staff. Furthermore, job satisfaction correlated positively with a high degree of implementation of two interventions. Fidelity to the Safewards model was high. CONCLUSIONS: Implementing the Safewards model in acute psychiatry with high fidelity can have positive effects on positive ward atmosphere and job satisfaction. Thus, patients as well as staff benefit from this model. With regards to high fluctuation in acute psychiatry, the implementation of the Safewards model can additionally facilitate retention management.

Preventing and Reducing Coercive Measures-An Evaluation of the Implementation of the Safewards Model in Two Locked Wards in Germany.

Introduction: Aggression and violence are highly complex problems in acute psychiatry that often lead to the coercive interventions. The Safewards Model is an evidence-informed conflict-reduction strategy to prevent and reduce such incidents. The aim of this study was to evaluate the implementation of this model with regard to coercive interventions in inpatient care. Materials and Methods: We evaluated outcomes of the implementation of the Safewards Model in two locked psychiatric wards in Germany. Frequency and duration of coercive interventions applied during a period of 10 weeks before and 10 weeks after the implementation period were assessed through routine data. Fidelity to the Safewards Model was assessed by the Organization Fidelity Checklist. Results: Fidelity to the Safewards Model was high in both wards. The overall use of coercive measures differed significantly between wards [case-wise: χ2 (1, n = 250) = 35.34, p ≤ 0.001; patient-wise: χ2 (1, n = 103) = 21.45, p ≤ 0.001] and decreased post-implementation. In one ward, the number of patients exposed to coercive interventions in relation to the overall number of admissions decreased significantly [χ2 (1, 182) = 9.30, p = 0.003]. Furthermore, the mean duration of coercive interventions overall declined significantly [U(55,21) = -2.142, p = 0.032] with an effect size of Cohen's d = -0.282 (95% CI: -0.787, 0.222) in that ward. Both aspects declined as well in the other ward, but not significantly. Discussion: Results indicate that the implementation of the Safewards interventions according to the model in acute psychiatric care can reduce coercive measures. They also show the role of enabling factors as well as of obstacles for the implementation process.

Assessment of weighted topological overlap (wTO) to improve fidelity of gene co-expression networks.

BACKGROUND: For more than a decade, gene expression data sets have been used as basis for the construction of co-expression networks used in systems biology investigations, leading to many important discoveries in a wide range of subjects spanning human disease to evolution and the development of organisms. A commonly encountered challenge in such investigations is first that of detecting, then subsequently removing, spurious correlations (i.e. links) in these networks. While access to a large number of measurements per gene would reduce this problem, often only a small number of measurements are available. The weighted Topological Overlap (wTO) measure, which incorporates information from the shared network-neighborhood of a given gene-pair into a single score, is a metric that is frequently used with the implicit expectation of producing higher-quality networks. However, the actual extent to which wTO improves on the accuracy of a co-expression analysis has not been quantified. RESULTS: Here, we used a large-sample biological data set containing 338 gene-expression measurements per gene as a reference system. From these data, we generated ensembles consisting of 10, 20 and 50 randomly selected measurements to emulate low-quality data sets, finding that the wTO measure consistently generates more robust scores than what results from simple correlation calculations. Furthermore, for the data sets consisting of only 10 and 20 samples per gene, we find that wTO serves as a better predictor of the correlation scores generated from the full data set. However, we find that using wTO as a score for network building substantially alters several topographical aspects of the resulting networks, with no conclusive evidence that the resulting structure is more accurate. Importantly, we find that the much used approach of applying a soft-threshold modifier to link weights prior to computing the wTO substantially decreases the robustness of the resulting wTO network, but increases the predictive power of wTO networks with regards to the reference correlation (soft threshold) network, particularly as the size of the data sets increases. CONCLUSION: Our analysis demonstrates that, in agreement with previous assumptions, the wTO approach is capable of significantly improving the fidelity of co-expression networks, and that this effect is especially evident for cases of low-sample number gene-expression data sets.

wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool.

BACKGROUND: Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network. RESULTS: Here, we present an R package for calculating the weighted topological overlap (wTO), that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks. The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (CN) from two or more networks into our R package. To graphically inspect the resulting networks, the R package contains a visualization tool, which allows for the direct network manipulation and access of node and link information. When testing the package on a standard laptop computer, we can conduct all calculations for systems of more than 20,000 genes in under two hours. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set. CONCLUSION: In this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL -2 Open Source License ( https://cran.r-project.org/web/packages/wTO/ ).

A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma.

Differential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disorders, or to infer molecular interactions. However, existing methods for differential co-expression network analysis are unable to distinguish between various forms of differential co-expression. To close this gap, here we define the three different kinds (conserved, specific, and differentiated) of differential co-expression and present a systematic framework, CSD, for differential co-expression network analysis that incorporates these interactions on an equal footing. In addition, our method includes a subsampling strategy to estimate the variance of co-expressions. Our framework is applicable to a wide variety of cases, such as the study of differential co-expression networks between healthy and disease states, before and after treatments, or between species. Applying the CSD approach to a published gene-expression data set of cerebral cortex and basal ganglia samples from healthy individuals, we find that the resulting CSD network is enriched in genes associated with cognitive function, signaling pathways involving compounds with well-known roles in the central nervous system, as well as certain neurological diseases. From the CSD analysis, we identify a set of prominent hubs of differential co-expression, whose neighborhood contains a substantial number of genes associated with glioblastoma. The resulting gene-sets identified by our CSD analysis also contain many genes that so far have not been recognized as having a role in glioblastoma, but are good candidates for further studies. CSD may thus aid in hypothesis-generation for functional disease-associations.