How cities prepare for climate change: Comparing Hamburg and Rotterdam

This paper compares the different ways in which the cities of Hamburg and Rotterdam are taking preemptive action to adapt to climate change. Literature, interviews, secondary data, official statistics, project reports and policy briefs were used to identify institutional arrangements used by the city governments to encourage innovations in climate adaptation strategies and involve the private sector in climate change policy implementation. We focus on cases that create positive opportunities; exploring how innovations are facilitated within the theoretical frameworks of the Porter hypothesis and eco-innovation. We examine two possible pathways of climate change governance, firstly strict regulation and formal enforcement, and secondly institutional eco-innovation and voluntary measures. We found that different emphasis is placed on the preferred pathway in each of the case studies. Hamburg focuses on formal enforcements while the Rotterdam city government encourages institutional eco-innovation by acting as a platform and also providing incentives. Our findings suggest that a well-designed institutional framework can enhance innovation and increase environmental and business performance. The framework could vary in instruments and patterns, using both formal constraints and incentives to increase voluntary actions to shape policy. The formal rules could be stringent or incentivising to shape the climate change measures. The research aims to contribute to both practice and science by providing examples that might motivate and inspire other cities to design appropriate institutions for climate change policy implementation

ISC-UNDRR-RISK KAN Briefing note on systemic risk

Systemic risk is associated with cascading impacts that spread within and across systems and sectors (e.g. ecosystems, health, infrastructure and the food sector) via the movements of people, goods, capital and information within and across boundaries (e.g. regions, countries and continents). The spread of these impacts can lead to potentially existential consequences and system collapse across a range of time horizons. Globalization contributes to systemic risk affecting people worldwide. The impacts of climate change or COVID-19 show how the challenges of addressing systemic risk go beyond conventional risk management and governance. Critical system interdependencies, amplified by underlying vulnerabilities, highlight that there is a growing need to better understand cascading impacts, systemic risks and the possible political (governance) and societal responses. This includes improving our understanding of the root causes of systemic risk, both biophysical and socio-economic, and related information needs. Addressing contemporary challenges in terms of systemic risk requires integrating different systems perspectives and fostering system thinking, while implementing key intergovernmental agendas, such as the Paris Agreement, the Sendai Framework for Disaster Risk Reduction and the Sustainable Development Goals. This Briefing Note represents an integrated perspective of climate, environmental and disaster risk science and practice regarding systemic risk. It provides an overview of the concepts of systemic risk that have evolved over time and identifies commonalities across terminologies and perspectives associated with systemic risk used in different contexts. Key attributes of systemic risk are outlined without prescribing a single definition, and information and data requirements that are essential for a better and more actionable understanding of the systemic nature of risk are discussed. Finally, the opportunities to connect research and policy for addressing systemic risk are highlighted, followed by recommendations for future work in science, policy and practice on systemic risk. The Briefing Note is based on insights and knowledge gained from an expert workshop, literature review and expert elicitation

Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone.National Institutes of Health (U.S.) (NIH grant P50-GM68762)National Institutes of Health (U.S.) (Grant U54-CA112967)United States. Dept. of Defense (Institute for Collaborative Biotechnologies

Iminosugar-Based Inhibitors of Glucosylceramide Synthase Increase Brain Glycosphingolipids and Survival in a Mouse Model of Sandhoff Disease

The neuropathic glycosphingolipidoses are a subgroup of lysosomal storage disorders for which there are no effective therapies. A potential approach is substrate reduction therapy using inhibitors of glucosylceramide synthase (GCS) to decrease the synthesis of glucosylceramide and related glycosphingolipids that accumulate in the lysosomes. Genz-529468, a blood-brain barrier-permeant iminosugar-based GCS inhibitor, was used to evaluate this concept in a mouse model of Sandhoff disease, which accumulates the glycosphingolipid GM2 in the visceral organs and CNS. As expected, oral administration of the drug inhibited hepatic GM2 accumulation. Paradoxically, in the brain, treatment resulted in a slight increase in GM2 levels and a 20-fold increase in glucosylceramide levels. The increase in brain glucosylceramide levels might be due to concurrent inhibition of the non-lysosomal glucosylceramidase, Gba2. Similar results were observed with NB-DNJ, another iminosugar-based GCS inhibitor. Despite these unanticipated increases in glycosphingolipids in the CNS, treatment nevertheless delayed the loss of motor function and coordination and extended the lifespan of the Sandhoff mice. These results suggest that the CNS benefits observed in the Sandhoff mice might not necessarily be due to substrate reduction therapy but rather to off-target effects

Beneficial and Detrimental Effects of Plasmin(ogen) during Infection and Sepsis in Mice

Plasmin has been proposed to be an important mediator during inflammation/infection. In this study, by using mice lacking genes for plasminogen, tissue-type plasminogen activator (tPA), and urokinase-type PA (uPA), we have investigated the functional roles of active plasmin in infection and sepsis. Two models were used: an infection model by intravenous injection of 1×107 CFU of S. aureus, and a sepsis model by intravenous injection of 1.6×108 CFU of S. aureus. We found that in the infection model, wild-type (WT) mice showed significantly higher survival rates than plasminogen-deficient (plg-/-) mice. However, in the sepsis model, plg-/- or tPA-/-/uPA-/- mice showed the highest survival rate whereas WT and tPA+/-/uPA+/- mice showed the lowest survival rate, and plg+/-, tPA-/-, and uPA-/- mice had an intermediate survival rate. These results indicate that the levels of active plasmin are critical in determining the survival rate in the sepsis, partly through high levels of inflammatory cytokines and enhanced STAT3 activation. We conclude that plasmin is beneficial in infection but promotes the production of inflammatory cytokines in sepsis that may cause tissue destruction, diminished neutrophil function, and an impaired capacity to kill bacteria which eventually causes death of these mice

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

This article contains supporting information online at http://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2113561119/-/DCSupplemental.Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.Integrative Biolog

Do Chinese and British university students use smartphones differently? A cross-cultural mixed methods study

Although an increasing number of studies have focused on problematic smartphone use and smartphone addiction, few of these studies have employed both quantitative and qualitative methods or employed a cross-cultural design. A limited number of studies have compared eastern and western groups. The present study investigates the prevalence and causes of problematic smartphone use among Chinese and British undergraduates. A sample of n = 778 undergraduates participated in this study (475 Chinese students and 303 British students). Students’ scores on a self-report measure of problematic smartphone use were compared across country and gender. Qualitative data were analyzed using the framework approach. Chinese undergraduates reported significantly higher levels of PSU than British undergraduates, with a medium to large effect size. Females scored significantly higher than males in both groups. Chinese students reported that the sharp transition from a strictly managed high school life to a freer university life affected their level of smartphone use. This study indicates the importance of considering cultural and educational backgrounds when conducting studies on problematic smartphone use

Global Retinoblastoma Presentation and Analysis by National Income Level

Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- A nd middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs