Infection and transmission risks of COVID-19 in schools and their contribution to population infections in Germany: A retrospective observational study using nationwide and regional health and education agency notification data.

BACKGROUND: School-level infection control measures in Germany during the early Coronavirus Disease 2019 (COVID-19) pandemic differed across the 16 federal states and lacked a dependable evidence base, with available evidence limited to regional data restricted to short phases of the pandemic. This study aimed to assess the (a) infection risks in students and staff; (b) transmission risks and routes in schools; (c) effects of school-level infection control measures on school and population infection dynamics; and (d) contribution of contacts in schools to population cases. METHODS AND FINDINGS: For this retrospective observational study, we used German federal state (NUTS-2) and county (NUTS-3) data from public health and education agencies from March 2020 to April 2022. We assessed (a) infection risk as cumulative risk and crude risk ratios and (b) secondary attack rates (SARs) with 95% confidence interval (CI). We used (c) multiple regression analysis for the effects of infection control measures such as reduced attendance, mask mandates, and vaccination coverage as absolute reduction in case incidence per 100,000 inhabitants per 14 days and in percentage relative to the population, and (d) infection dynamic modelling to determine the percentage contribution of school contacts to population cases. We included (a) nationwide NUTS-2 data from calendar weeks (W) 46-50/2020 and W08/2021-W15/2022 with 3,521,964 cases in students and 329,283 in teachers; (b) NUTS-3 data from W09-25/2021 with 85,788 student and 9,427 teacher cases; and (c) detailed data from 5 NUTS-3 regions from W09/2020 to W27/2021 with 12,814 cases (39% male, 37% female; median age 14, range 5 to 63), 43,238 contacts and 4,165 secondary cases for students (for teachers, 14,801 [22% male, 50% female; median age 39, range 16 to 75], 5,893 and 472). Infection risk (a) for students and teachers was higher than the population risk in all phases of normal presence class and highest in the early 2022 omicron wave with 30.6% (95% CI 30.5% to 32.6%) of students and 32.7% (95% CI 32.6% to 32.8%) of teachers infected in Germany. SARs (b) for students and staff were below 5% in schools throughout the study period, while SARs in households more than doubled from 13.8% (95% CI 10.6% to 17.6%) W21-39/2020 to 28.7% (95% CI 27% to 30.4%) in W08-23/2021 for students and 10.9% (95% CI 7% to 16.5%) to 32.7% (95% CI 28.2% to 37.6%) for staff. Most contacts were reported for schools, yet most secondary cases originated in households. In schools, staff predominantly infected staff. Mandatory surgical mask wearing during class in all schools was associated with a reduction in the case incidence of students and teachers (c), by 56/100,000 persons per 14 days (students: 95% CI 47.7 to 63.4; teachers: 95% CI 39.6 to 71.6; p < 0.001) and by 29.8% (95% CI 25% to 35%, p < 0.001) and 24.3% (95% CI 13% to 36%, p < 0.001) relative to the population, respectively, as were reduced attendance and higher vaccination coverage. The contribution of contacts in schools to population cases (d) was 2% to 20%, lowest during school closures/vacation and peaked during normal presence class intervals, with the overall peak early during the omicron wave. Limitations include underdetection, misclassification of contacts, interviewer/interviewee dependence of contact-tracing, and lack of individual-level confounding factors in aggregate data regression analysis. CONCLUSION: In this study, we observed that open schools under hygiene measures and testing strategies contributed up to 20% of population infections during the omicron wave early 2022, and as little as 2% during vacations/school closures; about a third of students and teachers were infected during the omicron wave in early 2022 in Germany. Mandatory mask wearing during class in all school types and reduced attendance models were associated with a reduced infection risk in schools.

Physiological Dependency Explains Temperature Differences in Sensitivity Towards Chemical Exposure.

In chemical risk assessment, extrapolations from laboratory tests to more realistic conditions are essential to address the toxic effects of pesticides on individuals and populations under field conditions. To transfer toxicological laboratory tests to differing temperature conditions, or outdoor field scenarios, the consideration of temperature dependence is essential and increases realism. Special consideration is given to the impact of temperature on direct sensitivity of organisms to pesticides, for which there are only few modelling approaches available so far. We present a concept for applying physiological temperature dependencies to toxicokinetic-toxicodynamic (TKTD) parameters in the General Uniformed Threshold model of Survival (GUTS). To test this approach in an exemplary study, temperature dependencies from studies on the developmental rate of the mayfly Cloeon dipterum were applied to the parameters of a previously parameterised TKTD model of this species after exposure to imidacloprid. Using a physiologically derived temperature correction for the TKTD rate constants, model predictions for independently conducted toxicology experiments with temperature ranges between 7.8 and 26.4 °C were performed for validation. Our approach demonstrates the successful transfer of a physiological observed temperature dependency on toxicity parameters and survival patterns for Cloeon dipterum and imidacloprid as a case study.

Ultracold atom interferometry in space.

Bose-Einstein condensates (BECs) in free fall constitute a promising source for space-borne interferometry. Indeed, BECs enjoy a slowly expanding wave function, display a large spatial coherence and can be engineered and probed by optical techniques. Here we explore matter-wave fringes of multiple spinor components of a BEC released in free fall employing light-pulses to drive Bragg processes and induce phase imprinting on a sounding rocket. The prevailing microgravity played a crucial role in the observation of these interferences which not only reveal the spatial coherence of the condensates but also allow us to measure differential forces. Our work marks the beginning of matter-wave interferometry in space with future applications in fundamental physics, navigation and earth observation.

Evaluation of a temporal causal model for predicting the mood of clients in an online therapy.

Background Self-reported client assessments during online treatments enable the development of statistical models for the prediction of client improvement and symptom development. Evaluation of these models is mandatory to ensure their validity. Methods For this purpose, we suggest besides a model evaluation based on study data the use of a simulation analysis. The simulation analysis provides insight into the model performance and enables to analyse reasons for a low predictive accuracy. In this study, we evaluate a temporal causal model (TCM) and show that it does not provide reliable predictions of clients' future mood levels. Results Based on the simulation analysis we investigate the potential reasons for the low predictive performance, for example, noisy measurements and sampling frequency. We conclude that the analysed TCM in its current form is not sufficient to describe the underlying psychological processes. Conclusions The results demonstrate the importance of model evaluation and the benefit of a simulation analysis. The current manuscript provides practical guidance for conducting model evaluation including simulation analysis.

Mechanochemical Synthesis of Mn3O4 Nanocrystals and Their Lithium Intercalation Capability.

Syntheses of Mn3O4 involve either high sintering temperatures to produce well crystallized products or the use of water-soluble precursors, surfactants, and organic solvents to generate nanocrystalline products. Mechanochemical approaches are known to be effective in the preparation of fine-grained or nanoscaled materials, while also being environmentally friendly because no solvents and no sintering at high temperatures are required. We report the solvent free mechanochemical synthesis of Mn3O4 nanocrystals at room temperature, from a mixture of MnO and Mn2O3. The single-phase product was characterized by Rietveld refinement and SEM images. The obtained crystallite size was 14.2(2) nm, which is among the smallest ever produced crystallite sizes of Mn3O4. The obtained product reveals an enormous increase in lithium intercalation capability, which was proven via chemical lithium intercalation at room temperature. More than 50% lithiation of nanocrystalline Mn3O4 is observed after a reaction time of 1 h, while coarse-grained material from a solid-state reaction shows no intercalation under the same reaction conditions. Therefore, the produced manganese oxide has a high potential in lithium battery applications.

Space-borne Bose-Einstein condensation for precision interferometry.

Owing to the low-gravity conditions in space, space-borne laboratories enable experiments with extended free-fall times. Because Bose-Einstein condensates have an extremely low expansion energy, space-borne atom interferometers based on Bose-Einstein condensation have the potential to have much greater sensitivity to inertial forces than do similar ground-based interferometers. On 23 January 2017, as part of the sounding-rocket mission MAIUS-1, we created Bose-Einstein condensates in space and conducted 110 experiments central to matter-wave interferometry, including laser cooling and trapping of atoms in the presence of the large accelerations experienced during launch. Here we report on experiments conducted during the six minutes of in-space flight in which we studied the phase transition from a thermal ensemble to a Bose-Einstein condensate and the collective dynamics of the resulting condensate. Our results provide insights into conducting cold-atom experiments in space, such as precision interferometry, and pave the way to miniaturizing cold-atom and photon-based quantum information concepts for satellite-based implementation. In addition, space-borne Bose-Einstein condensation opens up the possibility of quantum gas experiments in low-gravity conditions1,2.

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis.

BACKGROUND: Different treatment alternatives exist for psychological disorders. Both clinical and cost effectiveness of treatment are crucial aspects for policy makers, therapists, and patients and thus play major roles for healthcare decision-making. At the start of an intervention, it is often not clear which specific individuals benefit most from a particular intervention alternative or how costs will be distributed on an individual patient level. OBJECTIVE: This study aimed at predicting the individual outcome and costs for patients before the start of an internet-based intervention. Based on these predictions, individualized treatment recommendations can be provided. Thus, we expand the discussion of personalized treatment recommendation. METHODS: Outcomes and costs were predicted based on baseline data of 350 patients from a two-arm randomized controlled trial that compared treatment as usual and blended therapy for depressive disorders. For this purpose, we evaluated various machine learning techniques, compared the predictive accuracy of these techniques, and revealed features that contributed most to the prediction performance. We then combined these predictions and utilized an incremental cost-effectiveness ratio in order to derive individual treatment recommendations before the start of treatment. RESULTS: Predicting clinical outcomes and costs is a challenging task that comes with high uncertainty when only utilizing baseline information. However, we were able to generate predictions that were more accurate than a predefined reference measure in the shape of mean outcome and cost values. Questionnaires that include anxiety or depression items and questions regarding the mobility of individuals and their energy levels contributed to the prediction performance. We then described how patients can be individually allocated to the most appropriate treatment type. For an incremental cost-effectiveness threshold of 25,000 €/quality-adjusted life year, we demonstrated that our recommendations would have led to slightly worse outcomes (1.98%), but with decreased cost (5.42%). CONCLUSIONS: Our results indicate that it was feasible to provide personalized treatment recommendations at baseline and thus allocate patients to the most beneficial treatment type. This could potentially lead to improved decision-making, better outcomes for individuals, and reduced health care costs.

Predictive modeling in e-mental health: A common language framework.

Recent developments in mobile technology, sensor devices, and artificial intelligence have created new opportunities for mental health care research. Enabled by large datasets collected in e-mental health research and practice, clinical researchers and members of the data mining community increasingly join forces to build predictive models for health monitoring, treatment selection, and treatment personalization. This paper aims to bridge the historical and conceptual gaps between the distant research domains involved in this new collaborative research by providing a conceptual model of common research goals. We first provide a brief overview of the data mining field and methods used for predictive modeling. Next, we propose to characterize predictive modeling research in mental health care on three dimensions: 1) time, relative to treatment (i.e., from screening to post-treatment relapse monitoring), 2) types of available data (e.g., questionnaire data, ecological momentary assessments, smartphone sensor data), and 3) type of clinical decision (i.e., whether data are used for screening purposes, treatment selection or treatment personalization). Building on these three dimensions, we introduce a framework that identifies four model types that can be used to classify existing and future research and applications. To illustrate this, we use the framework to classify and discuss published predictive modeling mental health research. Finally, in the discussion, we reflect on the next steps that are required to drive forward this promising new interdisciplinary field.

Predicting therapy success for treatment as usual and blended treatment in the domain of depression.

In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.

The 2015 Annual Meeting of SETAC German Language Branch in Zurich (7-10 September, 2015): Ecotoxicology and environmental chemistry-from research to application.

This report provides a brief review of the 20th annual meeting of the German Language Branch of the Society of Environmental Toxicology and Chemistry (SETAC GLB) held from September 7th to 10th 2015 at ETH (Swiss Technical University) in Zurich, Switzerland. The event was chaired by Inge Werner, Director of the Swiss Centre for Applied Ecotoxicology (Ecotox Centre) Eawag-EPFL, and organized by a team from Ecotox Centre, Eawag, Federal Office of the Environment, Federal Office of Agriculture, and Mesocosm GmbH (Germany). Over 200 delegates from academia, public agencies and private industry of Germany, Switzerland and Austria attended and discussed the current state of science and its application presented in 75 talks and 83 posters. In addition, three invited keynote speakers provided new insights into scientific knowledge 'brokering', and-as it was the International Year of Soil-the important role of healthy soil ecosystems. Awards were presented to young scientists for best oral and poster presentations, and for best 2014 master and doctoral theses. Program and abstracts of the meeting (mostly in German) are provided as Additional file 1.

Removal of antibiotics in wastewater by enzymatic treatment with fungal laccase - Degradation of compounds does not always eliminate toxicity.

In this study, the performance of immobilised laccase (Trametes versicolor) was investigated in combination with the mediator syringaldehyde (SYR) in removing a mixture of 38 antibiotics in an enzymatic membrane reactor (EMR). Antibiotics were spiked in osmosed water at concentrations of 10µg·L(-1) each. Laccase without mediator did not reduce the load of antibiotics significantly. The addition of SYR enhanced the removal: out of the 38 antibiotics, 32 were degraded by >50% after 24h. In addition to chemical analysis, the samples' toxicity was evaluated in two bioassays (a growth inhibition assay and the Microtox assay). Here, the addition of SYR resulted in a time-dependent increase of toxicity in both bioassays. In cooperation with SYR, laccase effectively removes a broad range of antibiotics. However, this enhanced degradation induces unspecific toxicity. If this issue is resolved, enzymatic treatment may be a valuable addition to existing water treatment technologies.

Effects of carbamazepine and two of its metabolites on the non-biting midge Chironomus riparius in a sediment full life cycle toxicity test.

The antiepileptic drug carbamazepine (CBZ) and its main metabolites carbamazepine-10,11-epoxide (EP-CBZ) and 10,11-dihydro-10,11-dihydroxy-carbamazepine (DiOH-CBZ) were chosen as test substances to assess chronic toxicity on the non-biting midge Chironomus riparius. All the three substances were tested in a 40-day sediment full life cycle test (according to OECD 233) in which mortality, emergence, fertility, and clutch size were evaluated. In addition, these parameters were considered to calculate the population growth rate which represents an integrated measure to assess population relevant effects. With an LC50 of 0.20 mg/kg (time-weighted mean), the metabolite EP-CBZ was significantly more toxic than the parent substance CBZ (LC50: 1.1 mg/kg). Especially mortality, emergence, and fertility showed to be sensitive parameters under the exposure to CBZ and EP-CBZ. By using classical molecular dynamics (MD) simulations, the binding of CBZ to the ecdysone receptor was investigated as one possible mode of action (MoA) but appeared to be unlikely. The second metabolite DiOH-CBZ did not cause any effects within the tested concentration rage (0.17-1.2 mg/kg). Even though CBZ was less toxic compared to EP-CBZ, CBZ is found in the environment at much higher concentrations and therefore causes a higher potential risk for sediment dwelling organisms compared to its metabolites. Nevertheless, the current study illustrates the importance of including commonly found metabolites into the risk assessment of parent substances.

Personal networks and private forestry in Minnesota.

Personal networks affect the flow information and behavior through social groups. We investigated the role of personal relationships in the flow of information and adoption of sustainable forest management behavior by private forest landowners. Among the 1767 owners of 20 or more acres of Minnesota forest land surveyed, 90% have received forestry information from at least one source including 65% from a peer and 53% from a professional forester. Forestry information personal network size ranged from 0 to 14 with a mean of 2.92. Network diversity, expressed as the number of different types of information sources within the network, was relatively high relative to network size, suggesting that most landowners value diverse perspectives, despite reporting fairly small networks. Larger acreage owners, management plan holders, and frequent visitors to their forest land had significantly larger and more diverse networks. Network size and diversity were statistically unrelated to ownership tenure, landowner age, and resident/absentee status. Significantly more respondents named a peer or a professional as their most helpful source than other source categories. Satisfaction with forestry information networks was positively associated with network size and diversity, further suggesting that landowners prefer information from a variety of different sources. The results suggest that landowner education designed to foster peer learning and relationship building between landowners, foresters, loggers, and other groups may promote adoption of sustainable forest management practices.

Results of community deliberation about social impacts of ecological restoration: comparing public input of self-selected versus actively engaged community members.

Participatory processes for obtaining residents' input about community impacts of proposed environmental management actions have long raised concerns about who participates in public involvement efforts and whose interests they represent. This study explored methods of broad-based involvement and the role of deliberation in social impact assessment. Interactive community forums were conducted in 27 communities to solicit public input on proposed alternatives for recovering wild salmon in the Pacific Northwest US. Individuals identified by fellow residents as most active and involved in community affairs ("AE residents") were invited to participate in deliberations about likely social impacts of proposed engineering and ecological actions such as dam removal. Judgments of these AE participants about community impacts were compared with the judgments of residents motivated to attend a forum out of personal interest, who were designated as self-selected ("SS") participants. While the magnitude of impacts rated by SS participants across all communities differed significantly from AE participants' ratings, in-depth analysis of results from two community case studies found that both AE and SS participants identified a large and diverse set of unique impacts, as well as many of the same kinds of impacts. Thus, inclusion of both kinds of residents resulted in a greater range of impacts for consideration in the environmental impact study. The case study results also found that the extent to which similar kinds of impacts are specified by AE and SS group members can differ by type of community. Study results caution against simplistic conclusions drawn from this approach to community-wide public participation. Nonetheless, the results affirm that deliberative methods for community-based impact assessment involving both AE and SS residents can provide a more complete picture of perceived impacts of proposed restoration activities.