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Extreme weather events often trigger massive population displacement. A compounding factor is that the frequency and intensity of such events is affected by anthropogenic climate change. However, the effect of historical climate change on displacement risk has so far not been quantified. Here, we show how displacement can be partially attributed to climate change, using the example of the 2019 tropical cyclone Idai in Mozambique. We estimate population exposure to flooding following Idai’s landfall, using a combination of storm surge modeling and flood depth estimation from remote sensing images, for factual (climate change) and counterfactual (no climate change) mean sea level and maximum wind speed conditions. We find that climate change has increased displacement risk from this event by approximately 3.1 to 3.5%, corresponding to 16,000 - 17,000 additional displaced persons. Besides highlighting the significant effects on humanitarian conditions already imparted by climate change, our study provides a blueprint for event-based displacement attribution.

Global warming is likely to increase the proportion of intense hurricanes in the North Atlantic. Here, we analyze how this may affect economic growth. To this end, we introduce an event-based macroeconomic growth model that temporally resolves how growth depends on the heterogeneity of hurricane shocks. For the United States, we find that economic growth losses scale superlinearly with shock heterogeneity. We explain this by a disproportional increase of indirect losses with the magnitude of direct damage, which can lead to an incomplete recovery of the economy between consecutive intense landfall events. On the basis of two different methods to estimate the future frequency increase of intense hurricanes, we project annual growth losses to increase between 10 and 146% in a 2°C world compared to the period 1980–2014. Our modeling suggests that higher insurance coverage can compensate for this climate change–induced increase in growth losses.

The intensification of extreme weather events under global warming [1] requires the development of tailored evidence-based adaptation and mitigation strategies [2] to reduce the loss and damage caused by these events\cite [3]. Recently, first empirical evidence emerged that extreme weather events can have adverse impacts on economic development for more than a decade [4-7]. The neglect of these long-term effects in state-of-the-art integrated assessment exercises weighing the costs of climate change impacts against the costs of adaptation and mitigation measures may result in a critical lack of ambition in climate action [8]. We here estimate total and country-specific climate change-induced discounted annual damage (DAD) from tropical cyclones (TCs) for a large set of 41 TC-prone countries under different climate and socioeconomic futures. Accounting for the persisting impacts of TCs on the economy, these estimates reveal future adaptation needs of the countries. We then derive temperature-dependent TC damage function for a large set of 41 TC-affected countries that account for persistent growth effects. These functions allow us, for the first time, to quantify the country-level social cost of carbon (SCC) induced by TCs as a key metric to inform mitigation decisions. We find that for a middle-of-the-road scenario regarding greenhouse gas emissions, population development and economic growth, the additional average per-capita DAD corresponds to about one day of annual income lost for the average household in strongly affected high-income countries such as Hong Kong, Taiwan, Japan, the United States but also in TC-prone developing countries as for instance the Philippines or Vietnam. Further, accounting for TC impacts significantly increases the SCC of strongly affected major greenhouse gas emitters such as Japan (+39.8%), China (+8.1%), or the US (+6.3%) as well as globally by +2.1% (+5.4% in TC-affected countries). Our results underline the importance - and pave the way - to adequately account for the impacts of extreme weather events in integrated assessment of remaining climate damages along mitigation and adaptation pathways.

The Russian invasion of Ukraine is a major threat to the global food system. We explore multiple scenarios for global annual wheat prices and country supplies, finding that prices could spike by up to 65%, exceeding the price spikes of the two recent world food price crises in 2007/08 and 2010/11 if the war escalates any further and national policy makers react uncoordinatedly and unilaterally. International cooperation and leadership are urgently needed to help coordinate an effective international response.

The existing research about hindcasting flood surges and flood plains caused by tropical cyclones (TCs) is largely specific to single storms, regions or countries. Often enough, the tools and data used are not publicly available, making it challenging to extend those analyses to other world regions. For instance, there is not yet a global data set of flood surges and flood extent maps from tropical cyclones. We present a modeling framework for hindcasting TC surges, based on the open-source software GeoClaw. We use open-source software and publicly available data sources only, making our approach fully transparent, reproducible and reusable for a global community of impact and risk modelers. The selected input data products include all world regions. Thus, our setup can be applied to all ocean basins that experience TCs. We evaluate our framework by comparing i) the flood surges of selected events to tide gauge records and modeled (GTSM) sea level, and ii) the flood extent maps to a global data set of satellite-based (MODIS) flood maps for the period 2000-2019.Our analysis of tide gauge records shows that the setup captures the storm surge component of ocean dynamics at gauge locations very well, even though the model's capability to incorporate astronomical tides is limited. From the satellite-based flood maps, it becomes clear that beneath storm surge also rainfall and fluvial floods are important drivers of TC-related flooding, making it difficult to evaluate our model's performance based on its agreement with observed flood extents. Further, a comparison with high water marks in the field indicates that satellite-based products are often not sufficient to capture the full extent of short-term coastal flooding due to limited satellite overpass times. In this sense, storm surge model output can complement satellite observations.The modeling framework allows us to generate globally consistent TC storm surge hazard data for all world regions, including the global south and least developed countries. It is suitable for historical analyses as well as for attribution studies or future climate projections, based on synthetic events or ensembles.

Background Calling germline SNP variants from bisulfite-converted sequencing data poses a challenge for conventional software, which have no inherent capability to dissociate true polymorphisms from artificial mutations induced by the chemical treatment. Nevertheless, SNP data is desirable both for genotyping and to understand the DNA methylome in the context of the genetic background. The confounding effect of bisulfite conversion however can be conceptually resolved by observing differences in allele counts on a per-strand basis, whereby artificial mutations are reflected by non-complementary base pairs. Results Herein, we present a computational pre-processing approach for adapting sequence alignment data, thus indirectly enabling downstream analysis on a per-strand basis using conventional variant calling software such as GATK or Freebayes. In comparison to specialised tools, the method represents a marked improvement in precision-sensitivity based on high-quality, published benchmark datasets for both human and model plant variants. Conclusion The presented “double-masking” procedure represents an open source, easy-to-use method to facilitate accurate variant calling using conventional software, thus negating any dependency on specialised tools and mitigating the need to generate additional, conventional sequencing libraries alongside bisulfite sequencing experiments. The method is available at https://github.com/bio15anu/revelioand an implementation with Freebayes is available at https://github.com/EpiDiverse/SNP

Magic and Witchery in the Modern West: Celebrating the Twentieth Anniversary of “The Triumph of the Moon,” edited by Shai Feraro and Ethan Doyle White. Palgrave Macmillan, 2019. 259pp. ePDF $79.99, ISBN-13: 9783030155490; Pb. $99.99, ISBN-13: 9783030155513; Hb. $139.99, ISBN-13: 9783030155483.

Background Astronauts on long-duration spaceflight missions may develop changes in ocular structure and function, which can persist for years after the return to normal gravity. Chronic exposure to elevated intracranial pressure during spaceflight is hypothesized to be a contributing factor, however, the etiologic causes remain unknown. Purpose To investigate the intracranial effects of microgravity by measuring combined changes in intracranial volumetric parameters, pituitary morphologic structure, and aqueductal cerebrospinal fluid (CSF) hydrodynamics relative to spaceflight and to establish a comprehensive model of recovery after return to Earth. Materials and Methods This prospective longitudinal MRI study enrolled astronauts with planned long-duration spaceflight. Measures were conducted before spaceflight followed by 1, 30, 90, 180, and 360 days after landing. Intracranial volumetry and aqueductal CSF hydrodynamics (CSF peak-to-peak velocity amplitude and aqueductal stroke volume) were quantified for each phase. Qualitative and quantitative changes in pre- to postflight (day 1) pituitary morphologic structure were determined. Statistical analysis included separate mixed-effects models per dependent variable with repeated observations over time. Results Eleven astronauts (mean age, 45 years ± 5 [standard deviation]; 10 men) showed increased mean volumes in the brain (28 mL; P < .001), white matter (26 mL; P < .001), mean lateral ventricles (2.2 mL; P < .001), and mean summated brain and CSF (33 mL; P < .001) at postflight day 1 with corresponding increases in mean aqueductal stroke volume (14.6 μL; P = .045) and mean CSF peak-to-peak velocity magnitude (2.2 cm/sec; P = .01). Summated mean brain and CSF volumes remained increased at 360 days after spaceflight (28 mL; P < .001). Qualitatively, six of 11 (55%) astronauts developed or showed exacerbated pituitary dome depression compared with baseline. Average midline pituitary height decreased from 5.9 to 5.3 mm (P < .001). Conclusion Long-duration spaceflight was associated with increased pituitary deformation, augmented aqueductal cerebrospinal fluid (CSF) hydrodynamics, and expansion of summated brain and CSF volumes. Summated brain and CSF volumetric expansion persisted up to 1 year into recovery, suggesting permanent alteration. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lev in this issue.

Climate change entails an intensification of extreme weather events that can potentially trigger socioeconomic and energy system disruptions. As we approach 1 °C of global warming we should start learning from historical extremes and explicitly incorporate such events in integrated climate–economy and energy systems models.

Thlaspi arvense (field pennycress) is being domesticated as a winter annual oilseed crop capable of improving ecosystems and intensifying agricultural productivity without increasing land use. It is a selfing diploid with a short life cycle and is amenable to genetic manipulations, making it an accessible field-based model species for genetics and epigenetics. The availability of a high quality reference genome is vital for understanding pennycress physiology and for clarifying its evolutionary history within the Brassicaceae. Here, we present a chromosome-level genome assembly of var. MN106-Ref with improved gene annotation, and use it to investigate gene structure differences between two accessions (MN108 and Spring32-10) that are highly amenable to genetic transformation. We describe small RNAs, pseudogenes, and transposable elements, and highlight tissue specific expression and methylation patterns. Resequencing of forty wild accessions provides insights into genome-wide genetic variation as well as QTL regions for flowering time and a seedling color phenotype. Altogether, these data will serve as a tool for pennycress improvement in general and for translational research across the Brassicaceae.

Ongoing global warming is likely to increase the return frequency of very intense hurricanes in the North Atlantic. Here, we analyse how this frequency increase may impact on economic growth. To this end, we introduce an event-based macroeconomic growth model that temporally resolves how growth depends on the heterogeneity in timing and intensity of hurricane impacts. We calibrate the model to the United States and find that economic growth losses scale super-linearly with their heterogeneity. We explain this by a disproportional increase of indirect losses with event severity which can lead to an incomplete recovery of the economy between consecutive intense landfall events. Based on two different methods to estimate the future frequency increase of intense hurricanes compared to the period 1980-2014, we estimate annual growth losses to increase between 10% and 146% in a Paris-compatible 2°C world and even up to 522% in a 2.7°C world in compliance with the median end-of-century warming under currently implemented or enacted policies. We finally study the efficacy of disaster insurance as an adaptation strategy and find that higher insurance coverage may higher insurance coverage may be a viable means to mitigate these climate change-induced increases in growth losses.

Tropical cyclones range among the most severe disasters on Earth. Their economic repercussions along the supply and trade network also affect remote economies that are not directly affected. To find these repercussions in data is challenging due to the strong volatility of economic interactions. Numerical simulations can help to identify these ripples by approaching the problem from an exploratory angle isolating the effect of interest. We here simulate possible global repercussions for the example case of Hurricane Sandy (2012) using the shock-propagation model Acclimate. It models the behaviour of over 7,000 regional economic sectors, interlinked through about 1.8 million trade relations. The modeled shock yields a global three-phase ripple: an initial demand reduction and price decrease, followed by a supply shortage with increasing prices, and finally a recovery phase. Regions with strong trade relations to the US experience stronger magnitudes of the ripple. A dominating demand reduction or supply shortage leads to overall consumption gains or losses of a region, respectively. Throughout the three phases the model suggests overall a global consumption loss (~ 0.03%) with the strongest impact onto the US itself (~ 0.14%).

Academic theory and practitioner-performance metrics both focus on consumer recall and recognition of advertising content. A collaborative effort between academics and advertising practitioners, the current research considers advertising aligned with rational, emotional, and physiological appeals. The study employed a dataset representing 59,000 consumer interviews collected nationally on 590 television commercials that aired for the top 16 quick-service restaurant brands in the United States between 2008 and 2009. A moment-by-moment visual-recognition memory test developed, validated, and implemented by industry professionals demonstrates that compared with rational visual appeals, emotional, and physiological appeals more likely are related to consumer purchase intention. Implications stress the importance of understanding how advertising content influences brand memory and offer further insights into both theory and practice.

Prehospital stroke code activation results in reduced pre- and in-hospital delays and triage and transport of stroke patients to the right centers. In Paraguay, data about acute reper fusion treatment are not available. Recently, a pilot prehospital stroke code program was implemented in the country in November 2016. In an observational, single-center cohort study with a before–after design, from April 2015 to July 2018, we found that 193/832 (23.1%) of stroke patients were stroke code activated, and from these, 54 (6.5%) were brought to hospital under the prehospital stroke code protocol. Fifty-eight patients (58 alteplase and 2 additional endovascular treatment) received reperfusion therapy. Prehospital stroke code patients had a lower mean door-to-CT time (24 vs. 33 min, p = 0.021) and lower mean door-to-needle time (35.3 vs.76.3 min, p

The expanding scope and scale of next generation sequencing experiments in ecological plant epigenetics brings new challenges for computational analysis. Existing tools built for model data may not address the needs of users looking to apply these techniques to non-model species, particularly on a population or community level. Here we present a toolkit suitable for plant ecologists working with whole genome bisulfite sequencing; it includes pipelines for mapping, the calling of methylation values and differential methylation between groups, epigenome-wide association studies, and a novel implementation for both variant calling and discriminating between genetic and epigenetic variation.

Whole genome bisulfite sequencing is currently at the forefront of epigenetic analysis, facilitating the nucleotide-level resolution of 5-methylcytosine (5mC) on a genome-wide scale. Specialized software have been developed to accommodate the unique difficulties in aligning such sequencing reads to a given reference, building on the knowledge acquired from model organisms such as human, or Arabidopsis thaliana. As the field of epigenetics expands its purview to non-model plant species, new challenges arise which bring into question the suitability of previously established tools. Herein, nine short-read aligners are evaluated: Bismark, BS-Seeker2, BSMAP, BWA-meth, ERNE-BS5, GEM3, GSNAP, Last and segemehl. Precision-recall of simulated alignments, in comparison to real sequencing data obtained from three natural accessions, reveals on-balance that BWA-meth and BSMAP are able to make the best use of the data during mapping. The influence of difficult-to-map regions, characterized by deviations in sequencing depth over repeat annotations, is evaluated in terms of the mean absolute deviation of the resulting methylation calls in comparison to a realistic methylome. Downstream methylation analysis is responsive to the handling of multi-mapping reads relative to mapping quality (MAPQ), and potentially susceptible to bias arising from the increased sequence complexity of densely methylated reads.

Weather extremes such as heat waves, tropical cyclones, and river floods are likely to intensify with increasing global mean temperature. In a globally connected supply and trade network such extreme weather events cause economic shocks that may interfere with each other potentially amplifying their overall economic impact.Here we analyze the economic resonance of consecutive extreme events, that is the overlapping of economic response dynamics of more than one extreme event category both spatially and temporally. In our analysis we focus on the event categories heat stress, river floods, and tropical cyclones. We simulate, via an agent-based anomaly model with more than 7,000 economic agents and 1.8 million connections, the regional (direct) and inter-regional (indirect via supply chains) economic losses and gains for each extreme event category individually as well as for their concurrent occurrence for the next two decades (2020-2039). Thus we compare the outcome of the sum of the three single simulations to the outcome of the concurrent simulation. We show that the global welfare losses due to concurrent weather extremes are increased by more than 18% due to market effects compared to the summation of the losses of each single event category. Overall, this economic resonance yields a non-linearly enhanced price effect, which leads to a stronger economic impact. As well as a highly heterogeneous distribution of the amplification of regional welfare losses among countries.Our analysis is based on the climate models of the CMIP5 ensemble which have been bias-corrected within the ISIMIP2b project towards an observation-based data set using a trend-preserving method. From these we use RCP2.6 and 6.0 for future climate projections. Thus we compute for each of the three extreme weather event category regional, and sectoral production failure on a daily time scale. Our agent-based dynamic economic loss-propagation model Acclimate then uses these local production failures to compute the immediate response dynamics within the global supply chain as well as the subsequent trade adjustments. The Acclimate model thereby depicts a highly interconnected network of firms and consumers, which maximize their profits by choosing the optimal production level and corresponding upstream demand as well as the optimal distribution of this demand among its suppliers; transport and storage inventories act as buffers for supply shocks. The model accounts for local price changes; supply and demand mismatches are resolved explicitly over time.Our results suggest that economic impacts of weather extremes are larger than can be derived from conventional single event analysis. Consequently the societal cost of climate change are likely to be underestimated in studies focusing on single extreme categories.

Global food security is threatened by the effects of COVID-19 on international agricultural supply chains and locusts destroying crops and livelihoods in the Horn of Africa and South Asia. We quantify the possible impacts on global supplies and prices of wheat, rice and maize. We show that local production declines have moderate impacts on global prices and supply—but trade restrictions and precautionary purchases by a few key actors could create global food price spikes and severe local food shortages. COVID-19 and locust swarms have threatened international agricultural supply chains. Here, the possible impacts on wheat, rice and maize trade are modelled, showing that trade restrictions could create food price spikes and localized food shortages.

In informal learning scenarios the popularity of multimedia content, such as video tutorials or lectures, has significantly increased. Yet, the users’ interactions, navigation behavior, and consequently learning outcome, have not been researched extensively. Related work in this field, also called search as learning, has focused on behavioral or text resource features to predict learning outcome and knowledge gain. In this paper, we investigate whether we can exploit features representing multimedia resource consumption to predict the knowledge gain (KG) during Web search from in-session data, that is without prior knowledge about the learner. For this purpose, we suggest a set of multimedia features related to image and video consumption. Our feature extraction is evaluated in a lab study with 113 participants where we collected data for a given search as learning task on the formation of thunderstorms and lightning. We automatically analyze the monitored log data and utilize state-of-the-art computer vision methods to extract features about the seen multimedia resources. Experimental results demonstrate that multimedia features can improve KG prediction. Finally, we provide an analysis on feature importance (text and multimedia) for KG prediction.

Whole genome bisulfite sequencing is currently at the forefront of epigenetic analysis, facilitating the nucleotide-level resolution of 5-methylcytosine (5mC) on a genome-wide scale. Specialised software have been developed to accommodate the unique difficulties in aligning such sequencing reads to a given reference, building on the knowledge acquired from model organisms such as human, or Arabidopsis thaliana. As the field of epigenetics expands its purview to non-model plant species, new challenges arise which bring into question the suitability of previously established tools.Herein, nine short-read aligners are evaluated: Bismark, BS-Seeker2, BSMAP, BWA-meth, ERNE-BS5, GEM3, GSNAP, Last, and segemehl. Precision-recall of simulated alignments, in comparison to real sequencing data obtained from three natural accessions, reveals on-balance that BWA-meth and BSMAP are able to make the best use of the data during mapping. The influence of difficult-to-map regions, characterised by deviations in sequencing depth over repeat annotations, is evaluated in terms of the mean absolute deviation of the resulting methylation calls in comparison to a realistic methylome. Downstream methylation analysis is responsive to the handling of multi-mapping reads relative to mapping quality (MAPQ), and potentially susceptible to bias arising from the increased sequence complexity of densely-methylated reads.

Climate change affects precipitation patterns. Here, we investigate whether its signals are already detectable in reported river flood damages. We develop an empirical model to reconstruct observed damages and quantify the contributions of climate and socio-economic drivers to observed trends. We show that, on the level of nine world regions, trends in damages are dominated by increasing exposure and modulated by changes in vulnerability, while climate-induced trends are comparably small and mostly statistically insignificant, with the exception of South & Sub-Saharan Africa and Eastern Asia. However, when disaggregating the world regions into subregions based on river-basins with homogenous historical discharge trends, climate contributions to damages become statistically significant globally, in Asia and Latin America. In most regions, we find monotonous climate-induced damage trends but more years of observations would be needed to distinguish between the impacts of anthropogenic climate forcing and multidecadal oscillations., Nature Communications, 12 (1), ISSN:2041-1723

Recent studies of past changes in precipitation patterns suggest regionally varying but clearly detectable trends of global warming on physical flood indicators such as river discharge. Whether these trends are also visible in economic flood losses, has not yet been clearly answered, as changes in trends of damage records may be induced by either climatic or socio-economic drivers. In general, the socioeconomic impact of an extreme weather event is composed of three components: The hazard, the exposure of socioeconomic values to the event, and the vulnerabilities of the values, i.e., their propensity or predisposition to be adversely affected. In this work, we separate the historically observed trends in economic losses from river floods into the three contributions. We then quantify the effect of each driver on the overall change in economic losses from river floods between 1980 - 2010 for different world regions. In particular, this allows us to determine in which regions anthropogenic warming has already contributed to the observed trends in damages. We use flood depth as biophysical hazard indicator calculated by combining discharge simulations from 12 global hydrological models of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) model ensemble with the river-routing and flood inundation model CaMa-Flood. The hydrological models are driven by observed weather data and our simulations account for present-day protection standards from the FLOPROS database. Asset losses are estimated by combining gridded asset data with state-of-the-art flood damage functions translating flood depth to the fraction of affected assets employing the open-source socioeconomic impact modelling framework CLIMADA. Trends in modeled historical flood damages are then compared to observational damages by Munich Re’s NATCATService database in order to explain residual differences in trends by the three types of drivers. We first show that the method permits to reproduce the year-to-year variability observed damages on the regional level. We identify changes in exposure as the main driver of rising damage trends, but also observe significant - rising as well as declining - trends in flood hazards in several regions. Thus, effects of anthropogenic climate change that have already shown to unfold in discharge patterns, partly manifest already in economic damages, too. Residual trends in observed losses, that cannot be explained by changes in the hazard and the exposure alone, are caused by changes in vulnerability that can be well explained with trends in GDP per capita. Mostly, rising regional income results in declining vulnerability to river floods, in particular in less developed world regions. However, we also find indications of maladaptation, i.e., in some regions, vulnerability increases with GDP per capita.

Labour productivity declines in hot conditions. The frequency and intensities of extreme heat events is projected to increase substantially with climate change across the world, which causes not only severe impacts on health and well-being but could also lead to adverse impacts on the economy in particular in developing countries. Wet bulb globe temperature (WBGT) is a commonly used metric that combines temperature and humidity to estimate the occurrence of heat stress in occupational health. Although the links between heat stress and economic effects are well established, there are substantial differences between existing impact models of labour productivity. Here we present results of future changes in labour productivity based on a comprehensive intercomparison of labour productivity models across indoor and outdoor working environments, locations and countries. Under the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we applied projections from multiple bias corrected global climate models to multiple labour productivity impact models and consider different socioeconomic futures. In addition to models used in existing literature, we use a newly developed model based on empirical exposure-response functions estimated from three- hundred surveys (56 million observations) from 89 countries, that allows for projections at the sub-national level. Based on our model intercomparison results, we can provide robust and spatially explicit projections for changes in labour productivity across the globe. At the same time, our approach allows us to assess and compare existing models of labour productivity estimates, therefore covering multiple dimensions of uncertainty.

Weather extremes such as heat waves, tropical cyclones and river floods are likely to intensify with increasing global mean temperature. In a globally connected supply and trade network such extreme weather events cause economic shocks that may interfere with each other potentially amplifying their overall economic impact.Here we analyze the economic resonance of concurrent extreme events, that is the overlapping of economic response dynamics of more than one extreme event category both spatially and temporally. In our analysis we focus on the event categories heat stress, river floods and tropical cyclones. We simulate the regional (direct) and global (indirect via supply chains) economic losses and gains for each extreme event category individually as well as for their concurrent occurrence for the next two decades. Thus we compare the outcome of the sum of the three single simulations to the outcome of the concurrent simulation. Here we show that the global welfare loss due to concurrent weather extremes is increased by more than 17% due to market effects compared to the summation of the losses of each single event category. Overall, this economic resonance yields a non-linearly enhanced price effect, which leads to a stronger economic impact. As well as a highly heterogeneous distribution of the amplification of regional welfare losses among countries.Our analysis is based on the climate models of the CMIP5 ensemble which have been bias-corrected within the ISIMIP2b project towards an observation-based data set using a trend-preserving method. From these we use RCP2.6 and 6.0 for future climate projections. We transfer the three extreme weather event categories to a daily, regional and sectoral production failure. Our agent-based dynamic economic loss-propagation model Acclimate then uses these local production failures to compute the immediate response dynamics within the global supply chain as well as the subsequent trade adjustments. The Acclimate model thereby depicts a highly interconnected network of firms and consumers, which maximize their profits by choosing the optimal production level and corresponding upstream demand as well as the optimal distribution of this demand among its suppliers; transport and storage inventories act as buffers for supply shocks. The model accounts for local price changes, and supply and demand mismatches are resolved explicitly over time.Our results suggest that economic impacts of weather extremes are larger than can be derived from conventional single event analysis. Consequently the societal cost of climate change are likely to be underestimated in studies focusing on single extreme categories.

Background Reports on sex differences in stroke outcome and risk factors are scarce in Latin America. Our objective was to analyze clinical and prognostic differences according to sex among participants in the LASE (Latin American Stroke Registry). Methods and Results Nineteen centers across Central and South America compiled data on demographics, vascular risk factors, clinical stroke description, ancillary tests, and functional outcomes at short‐term follow‐up of patients included from January 2012 to January 2017. For the present study, all these variables were analyzed according to sex at hospital discharge. We included 4788 patients with a median in‐hospital stay of 8 days (interquartile range, 5–8); 2677 were male (median age, 66 years) and 2111 female (median age, 60 years). Ischemic stroke occurred in 4293: 3686 as cerebral infarction (77%) and 607 as transient ischemic attack cases (12.7%); 495 patients (10.3%) corresponded to intracerebral hemorrhage. Poor functional outcome (modified Rankin scale, 3–6) was present in 1662 (34.7%) patients and 38.2% of women ( P P =0.01). Death and poor functional outcome for all‐type stroke showed a higher risk in female patients (hazard ratio, 1.3, P =0.03; and hazard ratio, 1.1, P =0.001, respectively). Conclusions A worse functional outcome and higher mortality rates occurred in women compared with men in the LASE , confirming sex differences issues at short‐term follow‐up.

The beneficial, complementary nature of visual and textual information to convey information is widely known, for example, in entertainment, news, advertisements, science, or education. While the complex interplay of image and text to form semantic meaning has been thoroughly studied in linguistics and communication sciences for several decades, computer vision and multimedia research remained on the surface of the problem more or less. An exception is previous work that introduced the two metricsCross-Modal Mutual InformationandSemantic Correlationin order to model complex image-text relations. In this paper, we motivate the necessity of an additional metric calledStatusin order to cover complex image-text relations more completely. This set of metrics enables us to derive a novel categorization of eight semantic image-text classes based on three dimensions. In addition, we demonstrate how to automatically gather and augment a dataset for these classes from the Web. Further, we present a deep learning system to automatically predict either of the three metrics, as well as a system to directly predict the eight image-text classes. Experimental results show the feasibility of the approach, whereby the predict-all approach outperforms the cascaded approach of the metric classifiers.

Already today, at about 1°C of global warming, we observe a regional intensification of extreme weather events. While the short-term economic impacts of these events are well documented, little is known about their impacts on economic growth in the long-term. Using a three-way fixed-effects panel model, an outlier-robust regression, and ``people exposed'' as exogenous predictor, we study the short-, medium-, and long-term impacts of tropical cyclones and fluvial floods on per-capita GDP growth for a set of 158 countries for the period 1971--2010. To understand how the observed impacts depend upon the countries' development level, we divide countries into four groups based on the inequality-adjusted human development index. We further decompose national gross domestic product (GDP) into i) its national income components and ii) its sectoral contributions enabling us to single out income components as well as sectors that are most important for impact transmission. On the global level, weather extremes of both categories have significant negative short-, medium-, and long-term impacts on economic growth; over 15 years, long-term growth losses from severe tropical cyclones accumulate to -6.6% and are about five times larger than growth losses from severe fluvial floods (-1.2%). For both event categories, we find growth losses to depend non-monotonously upon development level challenging the common narrative that development can generally protect against disaster losses. Across all levels of development, investment, international trade are the most important transmission channels. Further, in poor countries growth losses mainly result from declines in agriculture and manufacturing growth. Our results provide guidance and decision support for the implementation of evidence-based coping and adaptation strategies, for instance, for the design of National Adaptation Plans for developing countries.