An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups.

Human security is threatened by terrorism in the 21st century. A rapidly growing field of study aims to understand terrorist attack patterns for counter-terrorism policies. Existing research aimed at predicting terrorism from a single perspective, typically employing only background contextual information or past attacks of terrorist groups, has reached its limits. Here, we propose an integrated deep-learning framework that incorporates the background context of past attacked locations, social networks, and past actions of individual terrorist groups to discover the behavior patterns of terrorist groups. The results show that our framework outperforms the conventional base model at different spatio-temporal resolutions. Further, our model can project future targets of active terrorist groups to identify high-risk areas and offer other attack-related information in sequence for a specific terrorist group. Our findings highlight that the combination of a deep-learning approach and multi-scalar data can provide groundbreaking insights into terrorism and other organized violent crimes.

Exploring the worldwide impact of COVID-19 on conflict risk under climate change.

Objectives: Understand whether and how the COVID-19 pandemic affects the risk of different types of conflict worldwide in the context of climate change. Methodology: Based on the database of armed conflict, COVID-19, detailed climate, and non-climate data covering the period 2020-2021, we applied Structural Equation Modeling specifically to reorganize the links between climate, COVID-19, and conflict risk. Moreover, we used the Boosted Regression Tree method to simulate conflict risk under the influence of multiple factors. Findings: The transmission risk of COVID-19 seems to decrease as the temperature rises. Additionally, COVID-19 has a substantial worldwide impact on conflict risk, albeit regional and conflict risk variations exist. Moreover, when testing a one-month lagged effect, we find consistency across regions, indicating a positive influence of COVID-19 on demonstrations (protests and riots) and a negative relationship with non-state and violent conflict risk. Conclusion: COVID-19 has a complex effect on conflict risk worldwide under climate change. Implications: Laying the theoretical foundation of how COVID-19 affects conflict risk and providing some inspiration for the implementation of relevant policies.

Early detection of late blight in potato by whole-plant redox imaging.

Late blight caused by the oomycete Phytophthora infestans is a most devastating disease of potatoes (Solanum tuberosum). Its early detection is crucial for suppressing disease spread. Necrotic lesions are normally seen in leaves at 4 days post-inoculation (dpi) when colonized cells are dead, but early detection of the initial biotrophic growth stage, when the pathogen feeds on living cells, is challenging. Here, the biotrophic growth phase of P. infestans was detected by whole-plant redox imaging of potato plants expressing chloroplast-targeted reduction-oxidation sensitive green fluorescent protein (chl-roGFP2). Clear spots on potato leaves with a lower chl-roGFP2 oxidation state were detected as early as 2 dpi, before any visual symptoms were recorded. These spots were particularly evident during light-to-dark transitions, and reflected the mislocalization of chl-roGFP2 outside the chloroplasts. Image analysis based on machine learning enabled systematic identification and quantification of spots, and unbiased classification of infected and uninfected leaves in inoculated plants. Comparing redox with chlorophyll fluorescence imaging showed that infected leaf areas that exhibit mislocalized chl-roGFP2 also showed reduced non-photochemical quenching and enhanced quantum PSII yield (ΦPSII) compared with the surrounding leaf areas. The data suggest that mislocalization of chloroplast-targeted proteins is an efficient marker of late blight infection, and demonstrate how it can be utilized for non-destructive monitoring of the disease biotrophic stage using whole-plant redox imaging.

Use of thermal imaging and the photochemical reflectance index (PRI) to detect wheat response to elevated CO2 and drought.

The spectral-based photochemical reflectance index (PRI) and leaf surface temperature (Tleaf ) derived from thermal imaging are two indicative metrics of plant functioning. The relationship of PRI with radiation-use efficiency (RUE) and Tleaf with leaf transpiration could be leveraged to monitor crop photosynthesis and water use from space. Yet, it is unclear how such relationships will change under future high carbon dioxide concentrations ([CO2 ]) and drought. Here we established an [CO2 ] enrichment experiment in which three wheat genotypes were grown at ambient (400 ppm) and elevated (550 ppm) [CO2 ] and exposed to well-watered and drought conditions in two glasshouse rooms in two replicates. Leaf transpiration (Tr ) and latent heat flux (LE) were derived to assess evaporative cooling, and RUE was calculated from assimilation and radiation measurements on several dates along the season. Simultaneous hyperspectral and thermal images were taken at ~ $\unicode{x0007E}$ 1.5 m from the plants to derive PRI and the temperature difference between the leaf and its surrounding air ( ∆ $\unicode{x02206}$ Tleaf-air ). We found significant PRI and RUE and ∆ $\unicode{x02206}$ Tleaf-air and Tr correlations, with no significant differences among the genotypes. A PRI-RUE decoupling was observed under drought at ambient [CO2 ] but not at elevated [CO2 ], likely due to changes in photorespiration. For a LE range of 350 W m-2 , the ΔTleaf-air range was ~ $\unicode{x0007E}$ 10°C at ambient [CO2 ] and only ~ $\unicode{x0007E}$ 4°C at elevated [CO2 ]. Thicker leaves in plants grown at elevated [CO2 ] suggest higher leaf water content and consequently more efficient thermoregulation at high [CO2 ] conditions. In general, Tleaf was maintained closer to the ambient temperature at elevated [CO2 ], even under drought. PRI, RUE, ΔTleaf -air , and Tr decreased linearly with canopy depth, displaying a single PRI-RUE and ΔTleaf -air  Tr model through the canopy layers. Our study shows the utility of these sensing metrics in detecting wheat responses to future environmental changes.

Exploring the direct and indirect impacts of climate variability on armed conflict in South Asia.

Although numerous studies have examined the effects of climate variability on armed conflict, the complexity of these linkages requires deeper understanding to assess the causes and effects. Here, we assembled an extensive database of armed conflict, climate, and non-climate data for South Asia. We used structural equation modeling to quantify both the direct and indirect impacts of climate variability on armed conflict. We found that precipitation impacts armed conflict via direct and indirect effects which are contradictory in sign. Temperature affects armed conflict only through a direct path, while indirect effects were insignificant. Yet, an in-depth analysis of indirect effects showed that the net impact is weak due to two strong contradictory effects offsetting each other. Our findings illustrate the complex link between climate variability and armed conflict, highlighting the importance of a detailed analysis of South Asia's underlying mechanisms at the regional scale.

Early or late? The role of genotype phenology in determining wheat response to drought under future high atmospheric CO2 levels.

The combination of a future rise in atmospheric carbon dioxide concentration ([CO2 ]) and drought will significantly impact wheat production and quality. Genotype phenology is likely to play an essential role in such an effect. Yet, its response to elevated [CO2 ] and drought has not been studied before. Here we conducted a temperature-controlled glasshouse [CO2 ] enrichment experiment in which two wheat cultivars with differing maturity timings and life cycle lengths were grown under ambient (aCO2 approximately 400 µmol mol-1 ) and elevated (eCO2 approximately 550 µmol mol-1 ) [CO2 ]. The two cultivars, bred under dry and warm Mediterranean conditions, were well-watered or exposed to drought at 40% pot holding capacity. We aimed to explore water × [CO2 ] × genotype interaction in terms of phenology, physiology, and agronomic trait response. Our results show that eCO2 had a significant effect on plants grown under drought. eCO2 boosted the booting stage of the late-maturing genotype (cv. Ruta), thereby prolonging its booting-to-anthesis period by approximately 3 days (p < 0.05) while unaffecting the phenological timing of the early-maturing genotype (cv. Zahir). The prolonged period resulted in a much higher carbon assimilation rate, particularly during pre-anthesis (+87% for Ruta vs. +22% for Zahir under eCO2 ). Surprisingly, there was no eCO2 effect on transpiration rate and grain protein content in both cultivars and under both water conditions. The higher photosynthesis (and transpiration efficiency) of Ruta was not translated into higher aboveground biomass or grain yield, whereas both cultivars showed a similar increase of approximately 20% in these two traits at eCO2 under drought. Overall, Zahir, the cultivar that responded the least to eCO2, had a more efficient source-to-sink balance with a lower sink limitation than Ruta. The complex water × [CO2 ] × genotype interaction found in this study implies that future projections should account for multifactor interactive effects in modeling wheat response to future climate.

Dryland mechanisms could widely control ecosystem functioning in a drier and warmer world.

Responses of terrestrial ecosystems to climate change have been explored in many regions worldwide. While continued drying and warming may alter process rates and deteriorate the state and performance of ecosystems, it could also lead to more fundamental changes in the mechanisms governing ecosystem functioning. Here we argue that climate change will induce unprecedented shifts in these mechanisms in historically wetter climatic zones, towards mechanisms currently prevalent in dry regions, which we refer to as 'dryland mechanisms'. We discuss 12 dryland mechanisms affecting multiple processes of ecosystem functioning, including vegetation development, water flow, energy budget, carbon and nutrient cycling, plant production and organic matter decomposition. We then examine mostly rare examples of the operation of these mechanisms in non-dryland regions where they have been considered irrelevant at present. Current and future climate trends could force microclimatic conditions across thresholds and lead to the emergence of dryland mechanisms and their increasing control over ecosystem functioning in many biomes on Earth.

Modelling armed conflict risk under climate change with machine learning and time-series data.

Understanding the risk of armed conflict is essential for promoting peace. Although the relationship between climate variability and armed conflict has been studied by the research community for decades with quantitative and qualitative methods at different spatial and temporal scales, causal linkages at a global scale remain poorly understood. Here we adopt a quantitative modelling framework based on machine learning to infer potential causal linkages from high-frequency time-series data and simulate the risk of armed conflict worldwide from 2000-2015. Our results reveal that the risk of armed conflict is primarily influenced by stable background contexts with complex patterns, followed by climate deviations related covariates. The inferred patterns show that positive temperature deviations or precipitation extremes are associated with increased risk of armed conflict worldwide. Our findings indicate that a better understanding of climate-conflict linkages at the global scale enhances the spatiotemporal modelling capacity for the risk of armed conflict.

Six decades of warming and drought in the world's top wheat-producing countries offset the benefits of rising CO2 to yield.

Future atmospheric carbon-dioxide concentration ([CO2]) rise is expected to increase the grain yield of C3 crops like wheat even higher under drought. This expectation is based on small-scale experiments and model simulations based on such observations. However, this combined effect has never been confirmed through actual observations at the nationwide or regional scale. We present the first evidence that warming and drought in the world's leading wheat-producing countries offset the benefits of increasing [CO2] to wheat yield in the last six decades. Using country-level wheat yield census observations, [CO2] records, and gridded climate data in a statistical model based on a well-established methodology, we show that a [CO2] rise of ~ 98 µmol mol-1 increased the yield by 7% in the area of the top-twelve wheat-producing countries, while warming of 1.2 °C and water depletion of ~ 29 mm m-2 reduced the wheat grain yield by ~ 3% and ~ 1%, respectively, in the last six decades (1961-2019). Our statistical model corroborated the beneficial effect of [CO2] but contrasted the expected increase of grain yield under drought. Moreover, the increase in [CO2] barely offsets the adverse impacts of warming and drought in countries like Germany and France, with a net yield loss of 3.1% and no gain, respectively, at the end of the sampling period relative to the 1961-1965 baseline. In China and the wheat-growing areas of the former Soviet Union-two of the three largest wheat-producing regions-yields were ~ 5.5% less than expected from current [CO2] levels. Our results suggest shifting our efforts towards more experimental studies set in currently warm and dry areas and combining these with statistical and numerical modeling to improve our understanding of future impacts of a warmer and drier world with higher [CO2].

Worldwide continuous gap-filled MODIS land surface temperature dataset.

Satellite land surface temperature (LST) is vital for climatological and environmental studies. However, LST datasets are not continuous in time and space mainly due to cloud cover. Here we combine LST with Climate Forecast System Version 2 (CFSv2) modeled temperatures to derive a continuous gap filled global LST dataset at a spatial resolution of 1 km. Temporal Fourier analysis is used to derive the seasonality (climatology) on a pixel-by-pixel basis, for LST and CFSv2 temperatures. Gaps are filled by adding the CFSv2 temperature anomaly to climatological LST. The accuracy is evaluated in nine regions across the globe using cloud-free LST (mean values: R2 = 0.93, Root Mean Square Error (RMSE) = 2.7 °C, Mean Absolute Error (MAE) = 2.1 °C). The provided dataset contains day, night, and daily mean LST for the Eastern Mediterranean. We provide a Google Earth Engine code and a web app that generates gap filled LST in any part of the world, alongside a pixel-based evaluation of the data in terms of MAE, RMSE and Pearson's r.

Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series.

Fire risk mapping - mapping the probability of fire occurrence and spread - is essential for pre-fire management as well as for efficient firefighting efforts. Most fire risk maps are generated using static information on variables such as topography, vegetation density, and fuel instantaneous wetness. Satellites are often used to provide such information. However, long-term vegetation dynamics and the cumulative dryness status of the woody vegetation, which may affect fire occurrence and spread, are rarely considered in fire risk mapping. Here, we investigate the impact of two satellite-derived metrics that represent long-term vegetation status and dynamics on fire risk mapping - the long-term mean normalized difference vegetation index (NDVI) of the woody vegetation (NDVIW) and its trend (NDVIT). NDVIW represents the mean woody density at the grid cell, while NDVIT is the 5-year trend of the woody NDVI representing the long-term dryness status of the vegetation. To produce these metrics, we decompose time-series of satellite-derived NDVI following a method adjusted for Mediterranean woodlands and forests. We tested whether these metrics improve fire risk mapping using three machine learning (ML) algorithms (Logistic Regression, Random Forest, and XGBoost). We chose the 2007 wildfires in Greece for the analysis. Our results indicate that XGBoost, which accounts for variable interactions and non-linear effects, was the ML model that produced the best results. NDVIW improved the model performance, while NDVIT was significant only when NDVIW was high. This NDVIW-NDVIT interaction means that the long-term dryness effect is meaningful only in places of dense woody vegetation. The proposed method can produce more accurate fire risk maps than conventional methods and can supply important dynamic information that may be used in fire behavior models.

Using Landsat satellites to assess the impact of check dams built across erosive gullies on vegetation rehabilitation.

Gully erosion, a process of soil removal due to water accumulation and runoff, is a worldwide problem affecting agricultural lands. Building check dams perpendicular to the flow direction is one of the suggested control practices to stabilize this process. Though there are many studies on the effect of erosive controls on land stabilization, few examine its effect on the rehabilitation of vegetation. Here we use information from the satellites Landsat-7 (1999-2018) and Landsat-8 (2013-2018) to assess the effect of soil check dams built during 2012 across three gullies with distinct structures in a dryland area on vegetative cover and water status. We use a time series analysis technique to decompose Landsat-derived soil adjusted vegetation index (SAVI) into woody (SAVIW) and herbaceous (iSAVIH) contributions. The integral over the seasonal signal of the normalized difference water index (iNDWI) was used to assess changes in water status in the gully. We used herbaceous biomass collected in the field in 2014-2017 to validate iSAVIH as a proxy of herbaceous biomass. Our results show that following the construction of the check dams, the change in woody vegetation cover is best described by a sigmoid model with an increase of ~57% (95% CI: 39%-76%; p < 0.0001), while the herbaceous vegetation increases linearly at a rate of ~71% per year (95% CI: 48%-93% y-1; p < 0.0001). The correlation between iSAVIH and herbaceous biomass (R2 = 0.56; n = 16; p < 0.001) corroborates this increase. We found higher herbaceous productivity in the deeper gully compared to the shallower gullies but not statistically different increase rates. An increase in iNDWI of ~68% (95% CI: 43%-95%; p < 0.0001) likely implies an improved water infiltration rate that favored the vegetation expansion. Our satellite-based approach can be used to assess the impact of erosive control practices on vegetation rehabilitation in heterogeneous gullies.

Land surface phenology: What do we really 'see' from space?

Land surface phenology (LSP) provides bio-indication of ongoing climate change. It uses space-borne greenness proxies to monitor plant phenology at the landscape level from the regional to global scale. However, several unconsidered methodological and observational -related limitations may lead to misinterpretation of the satellite-derived signals. For instance, changes in species composition within a pixel could result in a change in the time series of the greenness proxy, due to the distinct phenology of the plant species involved. The change in the signal would then be misinterpreted as a phenological change while it is actually related to changes in species composition within the pixel. Other limitations include the selection of the smoothing technique and the method used to extract the LSP metrics. These not only may affect the timing of the LSP metrics but also the sign of the observed LSP change. Another and much less known limitation is related to the mixed signal from multi-canopy layers. Satellites may detect changes that corresponds to the understorey layer in complex vertical vegetation systems while the 'real' contribution of this layer (in terms of ecosystem functioning and dynamics) might be small compared to the undetected overstorey layer in cases of a late overstorey development. Here, some of the LSP basics are reviewed with emphasis on these (and other) potential sources of misinterpretation. Several aids to overcome these limitations, which include suggestions for multi methods analysis and the integration of information from satellite and ground-based sensors are provided alongside some prospective future LSP research directions.

Forests growing under dry conditions have higher hydrological resilience to drought than do more humid forests.

More frequent and intense droughts are projected during the next century, potentially changing the hydrological balances in many forested catchments. Although the impacts of droughts on forest functionality have been vastly studied, little attention has been given to studying the effect of droughts on forest hydrology. Here, we use the Budyko framework and two recently introduced Budyko metrics (deviation and elasticity) to study the changes in the water yields (rainfall minus evapotranspiration) of forested catchments following a climatic drought (2006-2010) in pine forests distributed along a rainfall gradient (P = 280-820 mm yr-1 ) in the Eastern Mediterranean (aridity factor = 0.17-0.56). We use a satellite-based model and meteorological information to calculate the Budyko metrics. The relative water yield ranged from 48% to 8% (from the rainfall) in humid to dry forests and was mainly associated with rainfall amount (increasing with increased rainfall amount) and bedrock type (higher on hard bedrocks). Forest elasticity was larger in forests growing under drier conditions, implying that drier forests have more predictable responses to drought, according to the Budyko framework, compared to forests growing under more humid conditions. In this context, younger forests were shown more elastic than older forests. Dynamic deviation, which is defined as the water yield departure from the Budyko curve, was positive in all forests (i.e., less-than-expected water yields according to Budyko's curve), increasing with drought severity, suggesting lower hydrological resistance to drought in forests suffering from larger rainfall reductions. However, the dynamic deviation significantly decreased in forests that experienced relatively cooler conditions during the drought period. Our results suggest that forests growing under permanent dry conditions might develop a range of hydrological and eco-physiological adjustments to drought leading to higher hydrological resilience. In the context of predicted climate change, such adjustments are key factors in sustaining forested catchments in water-limited regions.

A double-blind randomized trial: prophylactic vasopressin reduces hypotension after cardiopulmonary bypass.

BACKGROUND: Inhibition of angiotensin-converting enzyme (ACE) predisposes patients to vasodilatory hypotension after cardiopulmonary bypass (CPB). This hypotension has been correlated with arginine vasopressin deficiency and can be corrected by its replacement. In patients receiving ACE inhibition, we investigated whether initiation of vasopressin before CPB would diminish post-CPB hypotension and catecholamine use by avoiding vasopressin deficiency. METHODS: Cardiac surgical patients on ACE inhibitor therapy were randomized to receive vasopressin (0.03 U/min) (n = 13) or an equal volume of normal saline (n = 14) starting 20 minutes before CPB. RESULTS: Vasopressin did not change pre-CPB mean arterial pressure or pulmonary artery pressure. After CPB, the vasopressin group had a lower peak norepinephrine dose than the placebo group (4.6 +/- 2.5 versus 7.3 +/- 3.5 microg/min, p = 0.03), a shorter period on catecholamines (5 +/- 6 versus 11 +/- 7 hours, p = 0.03), fewer hypotensive episodes (1 +/- 1 versus 4 +/- 2, p < 0.01), and a shorter intensive care unit length of stay (1.2 +/- 0.4 versus 2.1 +/- 1.4 days, p = 0.03). CONCLUSIONS: In this cohort, prophylactic administration of vasopressin, at a dose without a vasopressor effect pre-CPB, reduced post-CPB hypotension and vasoconstrictor requirements, and was associated with a shorter intensive care unit stay.

Restoration of renal function in shock by perfusion of the renal artery with venous blood: a counterintuitive approach.

OBJECTIVE: Acute renal failure (ARF) in low-flow states may be reversed by increasing renal perfusion. When hemodynamics are maximized, renal perfusion can only be improved by shunting a higher proportion of cardiac output to the kidney; however, in low-flow states, this reduces already compromised systemic pressure and perfusion to other organs. Increasing perfusion using venous blood (VB) would be an attractive option because decreased systemic pressure and perfusion to other organs could be avoided. However, it is not known whether VB can provide adequate oxygen delivery to restore or maintain renal function. We studied whether antegrade VB perfusion of the kidney via the renal artery would restore urine output (UO) and glomerular filtration rate (GFR) in hypoperfused ARF. DESIGN: Shock was induced in six dogs via a hemorrhagic protocol resulting in a systolic blood pressure of 50-70 mm Hg, a mixed venous oxygen saturation of 25% to 40%, and a UO <10% of baseline. After 60 mins of shock, the left renal artery was cannulated under fluoroscopy and perfused at pressures of 100-150 mm Hg for 30 mins with VB drawn from the vena cava and delivered by an extracorporeal pump system. The right kidneys were controls and remained hypoperfused. RESULTS: All VB-perfused kidneys recovered renal function after a sustained period of shock and marked oliguria: UO from 0.7 +/- 1.6 mL/hr to 101 +/- 58 mL/hr (p <.01); GFR from approximately 0 to 70.3 +/- 55 mL/min (p =.04). The control kidneys' UO (0.7 +/- 1.6 mL/hr) and GFR (0 mL/min) remained unchanged throughout the study. The experimental kidneys were able to extract oxygen from VB (O2 saturation, 31 +/- 7% to 16 +/- 4%; p =.01). CONCLUSION: When flow is controlled, kidneys in hypoperfused ARF can extract sufficient oxygen from antegrade VB perfusion to restore renal function (UO and GFR).