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Woody (lignocellulosic) plant biomass is an abundant renewable feedstock, rich in polysaccharides that are bound into an insoluble fiber composite with lignin. Marine crustacean woodborers of the genus Limnoria are among the few animals that can survive on a diet of this recalcitrant material without relying on gut resident microbiota. Analysis of fecal pellets revealed that Limnoria targets hexose-containing polysaccharides (mainly cellulose, and also glucomannans), corresponding with the abundance of cellulases in their digestive system, but xylans and lignin are largely unconsumed. We show that the limnoriid respiratory protein, hemocyanin, is abundant in the hindgut where wood is digested, that incubation of wood with hemocyanin markedly enhances its digestibility by cellulases, and that it modifies lignin. We propose that this activity of hemocyanins is instrumental to the ability of Limnoria to feed on wood in the absence of gut symbionts. These findings may hold potential for innovations in lignocellulose biorefining., Marine woodborers can digest woody biomass without the help of gut microbiota but the mechanism has remained unclear. Here, the authors provide evidence that the woodborer’s respiratory protein hemocyanin plays a central role in wood digestion and may offer a route toward biorefining of woody plant biomass.

During the austral summer 2015/16, severe flooding displaced over 170 000 people on the Paraguay River system in Paraguay, Argentina, and southern Brazil. These floods were driven by repeated heavy rainfall events in the lower Paraguay River basin. Alternating sequences of enhanced moisture inflow from the South American low-level jet and local convergence associated with baroclinic systems were conducive to mesoscale convective activity and enhanced precipitation. These circulation patterns were favored by cross-time-scale interactions of a very strong El Niño event, an unusually persistent Madden–Julian oscillation in phases 4 and 5, and the presence of a dipole SST anomaly in the central southern Atlantic Ocean. The simultaneous use of seasonal and subseasonal heavy rainfall predictions could have provided decision-makers with useful information about the start of these flooding events from two to four weeks in advance. Probabilistic seasonal forecasts available at the beginning of November successfully indicated heightened probability of heavy rainfall (90th percentile) over southern Paraguay and Brazil for December–February. Raw subseasonal forecasts of heavy rainfall exhibited limited skill at lead times beyond the first two predicted weeks, but a model output statistics approach involving principal component regression substantially improved the spatial distribution of skill for week 3 relative to other methods tested, including extended logistic regressions. A continuous monitoring of climate drivers impacting rainfall in the region, and the use of statistically corrected heavy precipitation seasonal and subseasonal forecasts, may help improve flood preparedness in this and other regions.

Additional file 7: Figure S6. The Na+ Site on the TtAA10A Surface. The modelled sodium ion is shown as a grey sphere with the groups that coordinate it shown as sticks coloured by atom type. The 2Fobsâ Fcalc map is shown as a blue wire mesh contoured at 1Ď . The sodium ion is octahedrally coordinated with three main chain carbonyl groups and three water molecules. Sodium was assigned at this site based on the coordinating bond lengths and this was the ion that gave a B-factor following refinement closest to the surrounding protein atoms.

Le cessez-le-feu est un instrument souvent utilisé pour contrer la violence. Améliorer l’efficacité des cessez-le-feu pour promouvoir la paix requiert des efforts concertés de la part des médiateurs, des décideurs politiques et des chercheurs pour mieux comprendre l’impact des cessez-le-feu sur les décisions politiques prises dans le cadre des processus de paix., Politique de sécurité: analyses du CSS, 252, ISSN:2296-0228

Ceasefires are one instrument frequently used to try and stop violence. To improve the effectiveness of ceasefires in advancing peace, concerted efforts by mediators, policymakers and researchers are needed to better understand the way they interact with the political decisions made in peace processes., Waffenstillstände sind häufig Instrument, um Gewalt zu beenden. Damit ein Waffenstillstand den Frieden wirksam voranbringen kann, müssen sich Mediator*innen, Politiker*innen und Forschende gemeinsam für ein besseres Verständnis des Zusammenspiels von Waffenstillständen mit politischen Entscheidungen im Friedensprozess einsetzen., CSS Analyses in Security Policy, 252, ISSN:2296-0244

Waffenstillstände sind häufig Instrument, um Gewalt zu beenden. Damit ein Waffenstillstand den Frieden wirksam voranbringen kann, müssen sich Mediator*innen, Politiker*innen und Forschende gemeinsam für ein besseres Verständnis des Zusammenspiels von Waffenstillständen mit politischen Entscheidungen im Friedensprozess einsetzen., CSS Analysen zur Sicherheitspolitik, 252, ISSN:2296-0236

East Africa experiences chronic food insecurity, with levels varying from year-to-year across the region. Given that much can be done to prevent this level of suffering before it happens, humanitarian agencies monitor early indicators of food insecurity to trigger early action. Forecasts of total seasonal rainfall are one tool used to monitor and anticipate food security outcomes. Factors beyond rainfall, such as conflict, are key determinants of whether lack of rainfall can become a problem. In this paper, we present a quantitative analysis that isolates the value of rainfall information in anticipating food security outcomes across livelihood groups in East Africa. Comparing observed rainfall and temperature with food security classifications, we quantify how much the chance of food insecurity increases when rainfall is low. Results differed dramatically among livelihood groups; pastoralists in East Africa more frequently experience food insecurity than do non-pastoralists, and 12 months of low rainfall greatly increases the chances of "crisis" and "emergency" food security in pastoralist regions. In non-pastoralist regions, the relationship with total rainfall is not as strong. Similar results were obtained for livelihood groups in Kenya and Ethiopia, with slightly differing results in Somalia. Given this, we evaluated the relevance of monitoring and forecasting seasonal total rainfall. Our quantitative results demonstrate that six months of rainfall observations can already indicate a heightened risk of food insecurity, a full six months before conditions deteriorate. Combining rainfall observations with seasonal forecasts can further change the range of possible outcomes to indicate higher or lower risk of food insecurity, but the added value of seasonal forecasts is noticeable only when they show a strong probability of below-normal rainfall.

No Abstract

Extreme temperatures are one of the leading causes of death and disease in both developed and developing countries, and heat extremes are projected to rise in many regions. To reduce risk, heatwave plans and cold weather plans have been effectively implemented around the world. However, much of the world’s population is not yet protected by such systems, including many data-scarce but also highly vulnerable regions. In this study, we assess at a global level where such systems have the potential to be effective at reducing risk from temperature extremes, characterizing (1) long-term average occurrence of heatwaves and coldwaves, (2) seasonality of these extremes, and (3) short-term predictability of these extreme events three to ten days in advance. Using both the NOAA and ECMWF weather forecast models, we develop global maps indicating a first approximation of the locations that are likely to benefit from the development of seasonal preparedness plans and/or short-term early warning systems for extreme temperature. The extratropics generally show both short-term skill as well as strong seasonality; in the tropics, most locations do also demonstrate one or both. In fact, almost 5 billion people live in regions that have seasonality and predictability of heatwaves and/or coldwaves. Climate adaptation investments in these regions can take advantage of seasonality and predictability to reduce risks to vulnerable populations.

Thermobia domestica belongs to an ancient group of insects and has a remarkable ability to digest crystalline cellulose without microbial assistance. By investigating the digestive proteome of Thermobia, we have identified over 20 members of an uncharacterized family of lytic polysaccharide monooxygenases (LPMOs). We show that this LPMO family spans across several clades of the Tree of Life, is of ancient origin, and was recruited by early arthropods with possible roles in remodeling endogenous chitin scaffolds during development and metamorphosis. Based on our in-depth characterization of Thermobia’s LPMOs, we propose that diversification of these enzymes toward cellulose digestion might have endowed ancestral insects with an effective biochemical apparatus for biomass degradation, allowing the early colonization of land during the Paleozoic Era. The vital role of LPMOs in modern agricultural pests and disease vectors offers new opportunities to help tackle global challenges in food security and the control of infectious diseases., LPMOs catalyze the oxidative breakdown of polysaccharides, thereby facilitating biomass degradation. By analyzing the digestive proteome of firebrats, the authors here identify a yet uncharacterized LPMO family and provide phylogenetic, structural and biochemical insights into its origin and functions.

Up to 33% of hemp by mass can be lost in the form of dust during processing for fibre production. Heptane Soxhlet extractions and supercritical carbon dioxide extractions (scCO2) of hemp dust samples yielded significant quantities of high value lipophilic molecules including fatty acids, policosanols (fatty alcohols), fatty aldehydes, hydrocarbons, sterols, triterpenoids and cannabinoids (cannabidiol (CBD)). Dust collected from different stages of the mechanical processing of hemp fibres gave rise to lipophilic extractives with varying compositions, thus making the isolation and purification of these compounds easier. Of particular interest is CBD (5832.5 ± 118.9 μg/g of dust), which has attracted much attention for clinical-level studies due to its therapeutic efficacy in the treatment of a variety of central nervous system (CNS) disorders. Factorial experimental design was carried out to optimise the scCO2 extraction, with 350 bar and 50 °C yielding the selective extraction of higher value components.

In this chapter, we examine how weather forecasts are made and how they may be used by the health sector. We explore why, where and when we might obtain good forecasts and why sometimes the forecasts go wrong. We consider the theoretical and practical constraints on weather forecasts and why their accuracy declines rapidly after only a few days. Knowing the limitations of weather forecasts helps us to learn how to make best use of such information.

Throughout this book, we have sought to identify how, when and where climate information, based on historical data, monitoring products and predictions of future weather and climate can be used to inform health policy and practice. We have prioritized operational information over research opportunities to ensure a focus on practical outcomes. In this chapter, we provide a short review of climate information currently available, as described in detail in Chapters 4-9. We then explore how developments in technologies, institutional arrangements and the education of health professionals are providing new opportunities for translating climate information into a new resource for health-sector decision-making.

In this chapter we describe how climate variables are measured (e.g., ground observation, remote sensing or modelled data), how the data are collected and how they are shared. We discuss the advantages and disadvantages of different data sources and highlight the potential advantages of climate data made available through National Meteorological and Hydrological Services. The use of observed climate monitoring products in the development of early warning systems is highlighted

This study explores the predictive skill of seasonal rainfall characteristics for the first rainy (and planting) season, May–June, in Central America. Statistical predictive models were built using a Model Output Statistics (MOS) technique based on canonical correlation analysis, in which variables that forecast with the Climate Forecast System version 2 (CFSv2) were used as candidate predictors for the observed total precipitation, frequency of rainy days and mean number of extremely dry and wet events in the season. CFSv2 initializations from February to April were explored. The CFSv2 variables used in the study consist of rainfall, as in a typical MOS technique, and a combination of low-level winds and convective available potential energy (CAPE), a blend that has been previously shown to be a good predictor for convective activity. The highest predictive skill was found for the seasonal frequency of rainy days, followed by the mean frequency of dry events. In terms of candidate predictors, the zonal transport of CAPE (uCAPE) at 925 hPa offers higher skill across Central America than rainfall, which is attributed in part to the high model uncertainties associated with precipitation in the region. As expected, dynamical model predictors initialized in February provide lower skill than those initialized later. Nonetheless, the skill is comparable for March and April initializations. These results suggest that the National Meteorological and Hydrological Services in Central America, and the Central American Regional Climate Outlook Forum, can produce earlier more skilful forecasts for May–June rainfall characteristics than previously stated.

In this chapter, we explore the potential value of seasonal climate forecasts to the health community. We begin by considering why the general weather conditions over a season might be predictable, before examining how seasonal forecasts are made, and why they are presented in a different way to weather forecasts. We then examine where and when seasonal forecasts work best, and emphasize that it is possible to make useful seasonal forecasts for health outcomes only in some parts of the world and for certain times of the year (and possibly only for some years). We also review some of the main sources of seasonal forecasts.

In writing this book, we have prioritized shorter timescales of weather and climate variability because these have most direct relevance to the types of operational and planning decisions made in the health sector. However, in this chapter, we focus on longer-term health decisions that require climate knowledge and information at timescales beyond a year to multiple decades (up to 50 years). This time span incorporates both natural decadal variability and the influences of anthropogenic climate change. It is an extremely challenging timeframe at which to work because of the very limited operational predictive skill at multiannual timescales, and limited confidence about how rainfall may change on longer timescales. Despite these challenges, the timescales under consideration, especially the next five to 20 years, are particularly important when considering how to adapt to climate change. Here we discuss what we do and do not know about the climate in the coming years. We then focus on the practical use of climate information in understanding observed climate impacts on health as well as the prediction of multi-annual to multi-decadal risks. The multi-annual timescale is often referred to as ‘decadal’, even though the forecasts are for periods less than a decade.

Additional file 1. Additional tables, figures, and additional text.

The global production of cereal straw as an agricultural by-product presents a significant source of biomass, which could be used as feedstock for the production of second generation biofuels by fermentation. The production of sugars for fermentation is an important measure of straw quality and in its suitability for biofuel production. In this paper, we present a characterization of straw digestibility from a wide range of cereal. Our main objective is to evaluate the variability of fermentable sugars released from different species including wheat (Triticum durum L., Triticum aestivum L.), barley (Hordeum vulgare L.) and triticale (X Triticosecale Wittmack). To this end, we adapted a saccharification method (IAS Method) capable of detecting significant differences of released sugars between cultivars and species, while using separately another method that would serve as a control and with which we could contrast our results (CNAP method). ANOVA analyses revealed that barley has a higher saccharification potential than wheat and triticale and shows more variation between genotypes. Thus, populations derived from crosses among them such as Steptoe × Morex and OWB Dominant × OWB Recessive hold potential for the identification of genetic basis for saccharification-related traits. The correlation of glucose released between the two methods was moderate (R2 = 0.57). An evaluation of the inter- and intra- specific correlation between a number of chemical and agronomical parameters and saccharification suggests that the cell wall thickness and lignin content in straw could be used in breeding programs for the improvement of the saccharification potential. Finally, the lack of correlation between grain yield and saccharification suggests that it would be possible to make a selection of genotypes for dual purpose, low recalcitrance and grain yield.

In this chapter we consider a range of factors that need to be taken into account when seeking to use climate information to improve health decision-making. Identifying causal mechanisms that link climate drivers with specific health issues is an important starting point for policy-makers. Matching decision time-horizons to climate information in a way that takes account of scale issues, uncertainties in the underlying data and modelling approaches as well as institutional barriers to knowledge and data sharing is also critical. And of course, all of this is dependent on a solid understanding of the climate information (including its limitations) that is available to health decision-makers. A researcher may be satisfied with a simple times-series of climate data from an authoritative source; a decision-maker needs to know that the climate information is robust, available for routine use and scalable (i.e., can be used over the entire region of interest).

In 2014, Jamaica entered one of the worst droughts in past 40 years. The drought was associated with the El Niño of 2014-15. Losses to agriculture and to farmers’ livelihoods were substantial, but not all farmers suffered equally. This chapter describes a seasonal drought forecast service that was developed by the Jamaican Meteorological Service (JMS) and the Rural and Agricultural Development Agency (RADA) to help farmers anticipate and prepare for drought. The service, which integrated new technical scientific information, interactive farmers’ forums, and various ways of communicating the information, grew directly out of Jamaica’s stakeholder-driven climate policy process. Jamaica’s success offers a useful example for how high-level planning, such as National Adaptation Plan processes and Nationally Determined Contributions can trigger actions that offer tangible benefits to vulnerable actors critical to sustaining key components of a country’s economy.

In this chapter, we focus on key concepts that health professionals need to understand in order to use climate data and information effectively. We explain the distinction between climate and weather and introduce some basic principles on the physics of the climate. We then go on to describe the character and behaviour of three key variables, namely temperature, precipitation and humidity. Descriptions of a range of other relevant important climate variables follow. Included is a section on storms that describes how temperature, wind and rainfall interact to create devastating extreme events. The final section discusses how data for different variables can be aggregated in time and space, and identifies challenges that emerge in the process.

Additional file 1: Table S1. Number of sequences, OTUs and alpha indices from Ion Torrent sequencing of 16S and 18S amplicons of samples. Table S2. Number of raw, rRNA and quality filtered sequences from the RNA-seq metatranscriptomics. Table S3. Number of unassigned and assigned spectra through analysis of biotin-labelled (BF) and supernatant (SNT) fractions of the wheat straw cultures.

World Meteorological Organization (WMO) guidance on verification of operational seasonal forecasts.

This chapter describes how climate varies by location, by considering the effects of altitude, latitude and other aspects of geography on the climate. Weather and climate can vary considerably at almost all timescales, but the amount they do vary differs considerably from place-to-place, and at different times of the year. Sea-surface temperature anomalies affect evaporation and heating or cooling of the overlying air, and the effect can last for weeks, months or even longer, because it takes so much energy to change the temperature of water. Spatial and temporal variations of temperature are much simpler than those of rainfall at virtually all scales. It is important to understand these scales of variability in space and time to obtain some idea of the necessary resolution of data for any analyses of climate–health relationships. The North Atlantic Oscillation is a large-scale pattern of natural climate variability characterized by a seesaw difference in air pressure between the Azores and Iceland.