ABSTRACT The increasing emergence of antimicrobial resistance and the development of new infective viral strains represent a constantly growing threat. Metal‐based nanomaterials have emerged as promising tools in the fight against bacterial and viral infections; however, the release of metal nanoparticles/ions in clinical applications may cause undesired side effects (allergies, systemic toxicity), reducing their practical use in antimicrobial treatment. Moreover, the metal‐based nanoparticles possess predominantly antibacterial effects, while their antiviral efficiency remains controversial. Thus, the development of metal‐free strategies enabling combined antibacterial/antiviral properties is a significant challenge. Here, we report a strategy based on light irradiation of nitrogen‐doped graphene acid (NGA) possessing dual photothermal and photodynamic modes of action. The antimicrobial activity is activated through a clinically approved near‐infrared (NIR) light source, and both viral and bacterial spreading can be hampered on the coating irradiation on a scale of minutes (5 to 10 min). The developed metal‐free strategy reduced 90.9% and 99.99% for S. aureus and P. aeruginosa, respectively, as well as 99.97% for murine hepatitis virus. Importantly, this research represents a significant advancement in the development of safe, metal‐free, and effective antimicrobial treatments. NGA coatings are safe for skin, showing no sensitization or irritation, and offer significant potential for advanced antimicrobial treatments.

While the global healthcare system is slowly recovering from the COVID-19 pandemic, new multi-drug-resistant pathogens are emerging as the next threat. To tackle these challenges there is a need for safe and sustainable antiviral and antibacterial functionalized materials. Here we develop an 'easy-to-apply' procedure for the surface functionalization of textiles, rendering them antiviral and antibacterial and assessing the performance of these textiles. A metal-free quaternary ammonium-based coating was applied homogeneously and non-covalently to hospital curtains. Abrasion, durability testing, and aging resulted in little change in the performance of the treated textile. Additionally, qualitative and quantitative antibacterial assays on Staphylococcus aureus , Pseudomonas aeruginosa, and Acinetobacter baumanii revealed excellent antibacterial activity with a CFU reduction of 98–100% within only 4 h of exposure. The treated curtain was aged 6 months before testing. Similarly, the antiviral activity tested according to ISO-18184 with murine hepatitis virus (MHV) showed > 99% viral reduction with the functionalized curtain. Also, the released active compounds of the coating 24 ± 5 µg mL −1 revealed no acute in vitro skin toxicity (IC 50 : 95 µg mL −1 ) and skin sensitization. This study emphasizes the potential of safe and sustainable metal-free textile coatings for the rapid antiviral and antibacterial functionalization of textiles.

The similarity of graph structures, such as Meaning Representations (MRs), is often assessed via structural matching algorithms, such as Smatch (Cai and Knight, 2013). However, Smatch involves a combinatorial problem that suffers from NP-completeness, making large-scale applications, e.g., graph clustering or search, infeasible. To alleviate this issue, we learn SMARAGD: Semantic Match for Accurate and Rapid Approximate Graph Distance. We show the potential of neural networks to approximate Smatch scores, i) in linear time using a machine translation framework to predict alignments, or ii) in constant time using a Siamese CNN to directly predict Smatch scores. We show that the approximation error can be substantially reduced through data augmentation and graph anonymization.

Reliability of machine learning evaluation -- the consistency of observed evaluation scores across replicated model training runs -- is affected by several sources of nondeterminism which can be regarded as measurement noise. Current tendencies to remove noise in order to enforce reproducibility of research results neglect inherent nondeterminism at the implementation level and disregard crucial interaction effects between algorithmic noise factors and data properties. This limits the scope of conclusions that can be drawn from such experiments. Instead of removing noise, we propose to incorporate several sources of variance, including their interaction with data properties, into an analysis of significance and reliability of machine learning evaluation, with the aim to draw inferences beyond particular instances of trained models. We show how to use linear mixed effects models (LMEMs) to analyze performance evaluation scores, and to conduct statistical inference with a generalized likelihood ratio test (GLRT). This allows us to incorporate arbitrary sources of noise like meta-parameter variations into statistical significance testing, and to assess performance differences conditional on data properties. Furthermore, a variance component analysis (VCA) enables the analysis of the contribution of noise sources to overall variance and the computation of a reliability coefficient by the ratio of substantial to total variance.

Since the start of the current COVID-19 pandemic, for the first time a significant fraction of the world's population cover their respiratory system for an extended period with mostly medical facemasks and textile masks. This new situation raises questions about the extent of mask related debris (fibers and particles) being released and inhaled and possible adverse effects on human health. This study aimed to quantify the debris release from a textile-based facemask in comparison to a surgical mask and a reference cotton textile using both liquid and air extraction. Under liquid extractions, cotton-based textiles released up to 29'452 ± 1'996 fibers g− 1 textile while synthetic textiles released up to 1'030 ± 115 fibers g− 1 textile. However, when the masks were subjected to air-based extraction scenarios, only a fraction (0.1–1.1%) of this fiber amount was released. Several metals including copper (up to 40.8 ± 0.9 µg g− 1) and iron (up to 7.0 ± 0.3 µg g− 1) were detected in acid dissolved textiles. Additionally the acute in vitro toxicity of size-fractionated liquid extracts (below and above 0.4 µm) were assessed on human alveolar basal epithelial cells. The current study shows no acute cytotoxicity response for all the analyzed facemasks.

Developing simulation and optimisation models for resource networks like water or energy systems increasingly involves integrating multiple data sources and software. Connecting multiple models and managing data accessed by different groups of analysts is a software challenge. Many resource systems are represented in computer models as networks of nodes and links, driven by a range of objectives and rules. We present a data storage platform, written in Python, which exploits the commonality of network representations to store data for multiple model types within a single deployment. This open-source platform provides a common source of data to multiple models using consistent data formats, reducing likelihood of error compared to file based data management. When deployed as a web service, it allows data to be shared securely among authorised users over the internet, facilitating collaboration. A case study describes the hosting of a water utility planning model, with an accompanying worked example.

BACKGROUND:Transfusion practice might significantly influence patient morbidity and mortality. Between European countries, transfusion practice of red blood cells (RBC) greatly differs. Only sparse data are available on transfusion practice of general internal medicine physicians in Switzerland.METHODS:In this cross-sectional survey, physicians working in general medicine teaching hospitals in Switzerland were investigated regarding their self-reported transfusion practice in anemic patients without acute bleeding. The definition of anemia, transfusion triggers, knowledge on RBC transfusion, and implementation of guidelines were assessed.RESULTS:560 physicians of 71 hospitals (64%) responded to the survey. Anemia was defined at very diverging hemoglobin values (by 38% at a hemoglobin <130 g/L for men and by 57% at <120 g/L in non-pregnant women). 62% and 43% respectively, did not define anemia in men and in women according to the World Health Organization. Fifty percent reported not to transfuse RBC according to international guidelines. Following factors were indicated to influence the decision to transfuse: educational background of the physicians, geographical region of employment, severity of anemia, and presence of known coronary artery disease. 60% indicated that their knowledge on Transfusion-related Acute Lung Injury (TRALI) did not influence transfusion practice. 50% of physicians stated that no local transfusion guidelines exist and 84% supported the development of national recommendations on transfusion in non-acutely bleeding, anemic patients.CONCLUSION:This study highlights the lack of adherence to current transfusion guidelines in Switzerland. Identifying and subsequently correcting this deficit in knowledge translation may have a significant impact on patient care.

Alkalinity is an important parameter in oceans, lakes, groundwaters and sediment porewaters as a link to the global carbon cycle. It is determined by classic titration with acid where sufficient sample volume is available. However, application to the limited amounts of sediment porewater requires a different approach. A portable low cost coulometric micro-titrator based on a RuO2 pH-sensitive electrode and a Ag/AgCl reference electrode requiring 50 pi of total sample volume is presented. By using a distinct sandwich cell design, a well-defined titration volume could be achieved. The micro-titrator performed well within the targeted range of 1-10 mmol (L-1) and a reproducibility within 3.5%. It was successfully applied to lake water and sediment porewater alkalinity measurements of Lake Lucerne and bears the potential for automation and in-situ applications. (C) 2017 Elsevier B.V. All rights reserved.

Construction of small hydropower plants (<10 megawatts) is booming worldwide, exacerbating ongoing habitat fragmentation and degradation, and further fueling biodiversity loss. A systematic approach for selecting hydropower sites within river networks may help to minimize the detrimental effects of small hydropower on biodiversity. In addition, a better understanding of reach- and basin-scale impacts is key for designing planning tools. We synthesize the available information about (1) reach-scale and (2) basin-scale impacts of small hydropower plants on biodiversity and ecosystem function, and (3) interactions with other anthropogenic stressors. We then discuss state-of-the-art, spatially explicit planning tools and suggest how improved knowledge of the ecological and evolutionary impacts of hydropower can be incorporated into project development. Such tools can be used to balance the benefits of hydropower production with the maintenance of ecosystem services and biodiversity conservation. Adequate planning tools that consider basin-scale effects and interactions with other stressors, such as climate change, can maximize long-term conservation.

Background: In clinical psychiatric practice, health care professionals (HCP) must decide in exceptional circumstances after the weighing of interests, which, if any, containment measures including coercion are to be used. Here, the risk for patients, staff, and third parties, in addition to therapeutic considerations, factor into the decision. Patients' preference and the inclusion of relatives in these decisions are important; therefore, an understanding of how patients and next of kin (NOK) experience different coercive measures is crucial for clinical decision making. The aim of this study is to compare how patients, HCP, and NOK assess commonly used coercive measures. Methods: A sample of 435 patients, 372 HCP, and 230 NOK completed the Attitudes to Containment Measures Questionnaire (ACMQ). This standardized self-rating questionnaire assessed the degree of acceptance or rejection of 11 coercive measures. Results: In general, HCPs rated the coercive measures as more acceptable than did NOK and patients. The largest discrepancy in the ratings was found in regard to the application of coercive intramuscular injection of medication (effect size: 1.0 HCP vs. patients). However, the ratings by NOK were significantly closer to the patients' ratings compared to patients and HCP. The only exception was the acceptance of treatment in a closed acute psychiatric ward, which was deemed significantly more acceptable by NOK than by patients. Also, patients who had experienced coercive measures themselves more strongly refused other measures. Conclusion: Patients most firmly rejected intramuscular injections, and the authors agree that these should only be used with reservation considering a high threshold. This knowledge about the discrepancy of the ratings should therefore be incorporated into professional training of HCP.

Modelling managed resource systems can involve the integration of multiple software modules into a single codebase. These modules are often written by non-software specialists, using heterogeneous terminologies and modelling approaches. One approach to model integration is to use a central structure to which each external module connects. This common interface acts as an agreed mode of communication for all contributors. We propose the Python Network Simulation (Pynsim) Framework, an open-source library for building simulation models of networked systems. Pynsim's central structure is a network, but it also supports non-physical entities like organisational hierarchies. We present two case studies using Pynsim which demonstrate how its use can lead to flexible and maintainable simulation models. First is a multi-agent model simulating the hydrologic and human components of Jordan's water system. The second uses a multi-objective evolutionary algorithm to identify the best locations for new run-of-river power plants in Switzerland.

The goal of the study was to compare the effectiveness of different suicide prevention measures implemented on bridges and other high structures in Switzerland. A national survey identified all jumping hotspots that have been secured in Switzerland; of the 15 that could be included in this study, 11 were secured by vertical barriers and 4 were secured by low-hanging horizontal safety nets. The study made an overall and individual pre-post analysis by using Mantel-Haenszel Tests, regression methods and calculating rate ratios. Barriers and safety nets were both effective, with mean suicide reduction of 68.7% (barriers) and 77.1% (safety nets), respectively. Measures that do not secure the whole hotspot and still allow jumps of 15 meters or more were less effective. Further, the analyses revealed that barriers of at least 2.3 m in height and safety-nets fixed significantly below pedestrian level deterred suicidal jumps. Secured bridgeheads and inbound angle barriers seemed to enhance the effectiveness of the measure. Findings can help to plan and improve the effectiveness of future suicide prevention measures on high structures.

A number of performance modeling approaches for predicting the performance of modern software systems and IT infrastructures exist in the literature. Different approaches differ in their modeling expressiveness and accuracy, on the one hand, and their modeling overhead and costs, on the other hand. Considering a representative set of established approaches, we analyze the semantic gaps between them as well as the trade-offs in using them; we further provide guidelines for selecting the right approach suitable for a given scenario.

Simulation of engineered resource systems is increasingly expected to represent the actions of multiple actors, from individual agents who manage one site to institutions that manage complex interactions within the resource system. These models often incorporate multiple components, which need to be integrated into a single computing context. In order to achieve such integration, a common semantic connection must be defined. We focus on systems such as water resources where a network of nodes and links can be used to represent the system. We present Pynsim, an open-source python simulation framework. In a Pynsim simulation, multiple model developers can contribute their code as a sub-model, or ‘engine’. Multiple engines operate on the same network, whose node types, topology and data are defined as Pynsim objects. Pynsim can also represent governmental, physical or social hierarchies. By providing this central structure, Pynsim can accommodate complex interdependencies between engines. We describe the application of Pynsim to two international case-studies. One is a multi-agent simulation of Jordan’s national water system, incorporating the physical, social, economic and political aspects of this system. This project demonstrates how developers with different backgrounds can work towards a complex integrated model using a single flexible software framework. Pynsim’s design ensures the resulting model consists of reusable and manageable components. The second project focuses on how the addition of new run-of-river power plants affects a mountainous river network. This project connects a Pynsim simulation to a multi-objective evolutionary algorithm to help determine the optimal placement of new power plants within a network. This example shows that topology changes can be performed during the optimisation process.

Flow reduction for hydropower production is expected to have significant effects on aquatic ecosystems in the Maggia River (Canton Ticino). Within this floodplain ecosystem, the ecological effects of flow regulation are likely to be mediated by aquatic habitat fragmentation and change in local environmental conditions (temperature, chemistry, oxygen levels, habitat size...). By studying macroinvertebrate community assembly and food web structure at sites linked by varying degrees of hydrological connectivity, we will quantify the effects of habitat fragmentation on aquatic ecosystems. More generally, this study will contribute to the Energy Strategy 2050 by providing robust knowledge on processes linking flow regulation and downstream ecological effects. Ecohydrology of Macroinvertebrate Metacommunity Assembly in a Regulated Floodplain Pierre Chanut1, Christopher T. Robinson1, Peter Molnar2 1EAWAG, Dübendorf, Switzerland. Email: pierre.chanut@eawag.ch 2ETH, IfU 1. Introduction The Maggia River is maintained at low flow during prolonged periods for hydropower production. This flow reduction creates a mosaic of habitat patches with varying degrees of hydrological connectivity, ranging from fully connected flowing channels to isolated ponds. Local environmental conditions are expected to be substantially different between these habitat patches due to differing hydrological regimes. In order to quantify the effects of flow reduction on the ecosystem in this fragmented floodplain habitat, we will study macroinvertebrate metacommunity assembly as inter-patch connectivity decreases after a flow event. 2. Methods Two sampling designs: a tri-monthly sampling campaign will reveal seasonal variation in macroinvertebrate metacommunity structure, and an intensive sampling campaign following a flood will identify processes driving metacommunity assembly. q Habitat characterization for each site: • 2D hydrodynamic model to derive hydrological regime for each site • Deployment of temperature data loggers • Drone imagery to derive habitat size fluctuations • Field-based habitat characterization: substrate-size distribution, water physicochemistry, habitat size, primary productivity (periphyton cover) q Characterization of spatial distances and connectivity among sites: • Drone imagery in combination with flow gauging to identify fluctuations of hydrological connectivity between habitats • Drone imagery to derive Euclidian distances between sites and friction maps q Analysis of macroinvertebrate community composition and food web structure • Characterization of macroinvertebrate community composition and biological traits from field samples • Analysis of stable isotopic ratios from macroinvertebrates, fish, and periphyton to derive food web structure • Combination of quantitative sampling and stable isotopic analysis to calculate energy flow through the food web 3. Conclusions This study of the effects of flow regulation on macroinvertebrate community assembly will provide key knowledge on ecological effects of flow regulation on downstream floodplain ecosystems. The combination of structural and functional ecological metrics will enable to not only identify patterns but also understand ecological processes linking flow regulation, habitat fragmentation and ecosystem health (in terms of resistance and resilience). 0.7 m3/s 15 m3/s 1.2 m3/s 300 m3/s Source: Wolfgang Ruf et al. “Modelling the interac7on between groundwater and river flow in an ac7ve alpine floodplain ecosystem”. Interna7onal Symposium: Floodplains. Goerlitz 2005 Habitat heterogeneity in the Maggia floodplain: Flow depth and hydrological connec7vity increase at higher river flow: Trade-offs Between Small Hydropower Plants and Ecosystem Services in an Alpine River Network Philipp Meier1, Katharina Lange2, Robin Schwemmle1, and Daniel Viviroli3 Eawag, Department of Surface Waters – Research and Management, Kastanienbaum; Eawag, Department of Fish Ecology and Evolution, Kastanienbaum; Hydrology and Climate Unit, Department of Geography, University of Zurich Swiss Competence Center for Energy Research – Supply of Electricity Annual Conference 2015