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Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. We present a novel method to fully reconstruct MODIS daily LST products for central Europe at 1 km resolution and globally, at 3 arc-min. We combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. The reconstructed MODIS LST for central Europe was calibrated to air temperature data through linear models that yielded R2 values around 0.8 and RMSE of 0.5 K. This new method proves to scale well for both local and global reconstruction. We show examples for the identification of extreme events to demonstrate the ability of these new LST products to capture and represent spatial and temporal details. A time series of global monthly average, minimum and maximum LST data and long-term averages is freely available for download.

Temperature is a main driver for most ecological processes, and temperature time series provide key environmental indicators for various applications and research fields. High spatial and temporal resolutions are crucial for detailed analyses in various fields of research. A disadvantage of temperature data obtained by satellites is the occurrence of gaps that must be reconstructed. Here, we present a new method to reconstruct high-resolution land surface temperature (LST) time series at the continental scale gaining 250-m spatial resolution and four daily values per pixel. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. This newly developed reconstruction method has been applied to greater Europe, resulting in complete daily coverage for eleven years. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. We illustrate a series of applications in this paper. Our dataset is available online for download as time aggregated derivatives for direct usage in GIS-based applications.

A variety of organisms have evolved mechanisms to detect and respond to light, in which the response is mediated by protein structural changes after photon absorption. The initial step is often the photoisomerization of a conjugated chromophore. Isomerization occurs on ultrafast time scales and is substantially influenced by the chromophore environment. Here we identify structural changes associated with the earliest steps in the trans-to-cis isomerization of the chromophore in photoactive yellow protein. Femtosecond hard x-ray pulses emitted by the Linac Coherent Light Source were used to conduct time-resolved serial femtosecond crystallography on photoactive yellow protein microcrystals over a time range from 100 femtoseconds to 3 picoseconds to determine the structural dynamics of the photoisomerization reaction.

West Nile Virus (WNV) is a globally important mosquito borne virus, with significant implications for human and animal health. The emergence and spread of new lineages, and increased pathogenicity, is the cause of escalating public health concern. Pinpointing the environmental conditions that favour WNV circulation and transmission to humans is challenging, due both to the complexity of its biological cycle, and the under-diagnosis and reporting of epidemiological data. Here, we used remote sensing and GIS to enable collation of multiple types of environmental data over a continental spatial scale, in order to model annual West Nile Fever (WNF) incidence across Europe and neighbouring countries. Multi-model selection and inference were used to gain a consensus from multiple linear mixed models. Climate and landscape were key predictors of WNF outbreaks (specifically, high precipitation in late winter/early spring, high summer temperatures, summer drought, occurrence of irrigated croplands and highly fragmented forests). Identification of the environmental conditions associated with WNF outbreaks is key to enabling public health bodies to properly focus surveillance and mitigation of West Nile virus impact, but more work needs to be done to enable accurate predictions of WNF risk.

Bat echolocation is primarily used for orientation and foraging but also holds great potential for social communication. The communicative function of echolocation calls is still largely unstudied, especially in the wild. Eavesdropping on vocal signatures encoding social information in echolocation calls has not, to our knowledge, been studied in free-living bats so far. We analysed echolocation calls of the polygynous bat Saccopteryx bilineata and found pronounced vocal signatures encoding sex and individual identity. We showed experimentally that free-living males discriminate approaching male and female conspecifics solely based on their echolocation calls. Males always produced aggressive vocalizations when hearing male echolocation calls and courtship vocalizations when hearing female echolocation calls; hence, they responded with complex social vocalizations in the appropriate social context. Our study demonstrates that social information encoded in bat echolocation calls plays a crucial and hitherto underestimated role for eavesdropping conspecifics and thus facilitates social communication in a highly mobile nocturnal mammal.

Rift Valley fever (RVF) is a vector-borne zoonotic disease with recurrent epidemic and epizootic outbreaks in Mauritania caused by the RVF virus (RVFV). In recent years, outbreaks have occurred with increasingly shorter inter-epidemic periods. The primary objective of this study was to utilise a high-resolution spatiotemporal model and identify the drivers and ecological suitability for RVFV infections, as well as areas for RVF outbreaks and emergence in humans and animals, respectively, in Mauritania. We used geolocated data from 2019 to 2023 for modelling, including human RVF cases confirmed by viral RNA detection, animal cases identified through serology or viral RNA detection, and mosquito samples in which the virus was detected by RNA analysis. Negative RVFV results were used as absence (or background) data to represent an environmental contrast between places with and without cases. Duplicates of occurrences at the exact location were kept, as multiple cases in the same place indicate a potentially higher risk. The main drivers of RVFV infection were the precipitation of the current and the preceding month of the outbreaks, followed by the average daily temperature of the current month of the outbreaks. August, September, and October were the most ecologically favourable months for RVFV infection, starting in the country’s southeastern regions and expanding to the entire southern area by September and October. The RVF outbreak potential was highest in the wet season, between August and October, in most of the south and western parts of the country. Although the RVF outbreak potential is substantially reduced during the dry season, some smaller areas in Mauritania have a relatively high outbreak potential throughout the year, and some of these areas are also located further north. These results can be used to improve sentinel active surveillance and establish an early warning model for RVFV infections in Mauritania, enabling the setting of appropriate control measures to prevent future RVF outbreaks and minimise human and animal losses.

Any place the human species inhabits is inevitably modified by them. One of the first features that appear everywhere, in urban areas as well as in the countryside or deep forests, are roads. Further, roads and streets in general reflect their omnipresent and significant role in our lives through the flow of goods, people, and even culture and information. However, their contribution to the public is highly influenced by their surface. Yet, research on automated road surface classification from remotely sensed data is peculiarly scarce. This work investigates the capacities of chosen convolutional neural networks (fully convolutional network (FCN), U-Net, SegNet, DeepLabv3+) on this task. We find that convolutional neural network (CNN) are capable of distinguishing between compact (asphalt, concrete) and modular (paving stones, tiles) surfaces for both roads and sidewalks on aerial data of spatial resolution of 10 cm. U-Net proved its position as the best-performing model among the tested ones, reaching an overall accuracy of nearly 92%. Furthermore, we explore the influence of adding a near-infrared band to the basic red green blue (RGB) scenes and stress where it should be used and where avoided. Overfitting strategies such as dropout and data augmentation undergo the same examination and clearly show their pros and cons. Convolutional neural networks are also compared to single-pixel based random forests and show indisputable advantage of the context awareness in convolutional neural networks, U-Net reaching almost 25% higher accuracy than random forests. We conclude that convolutional neural networks and U-Net in particular should be considered as suitable approaches for automated semantic segmentation of road surfaces on aerial imagery, while common overfitting strategies should only be used under particular conditions.

BACKGROUND: West Nile Virus (WNV) is an emerging global health threat. Transmission risk is strongly related to the abundance of mosquito vectors, typically Culex pipiens in Europe. Early-warning predictors of mosquito population dynamics would therefore help guide entomological surveillance and thereby facilitate early warnings of transmission risk. METHODS: We analysed an 11-year time series (2001 to 2011) of Cx. pipiens mosquito captures from the Piedmont region of north-western Italy to determine the principal drivers of mosquito population dynamics. Linear mixed models were implemented to examine the relationship between Cx. pipiens population dynamics and environmental predictors including temperature, precipitation, Normalized Difference Water Index (NDWI) and the proximity of mosquito traps to urban areas and rice fields. RESULTS: Warm temperatures early in the year were associated with an earlier start to the mosquito season and increased season length, and later in the year, with decreased abundance. Early precipitation delayed the start and shortened the length of the mosquito season, but increased total abundance. Conversely, precipitation later in the year was associated with a longer season. Finally, higher NDWI early in the year was associated with an earlier start to the season and increased season length, but was not associated with abundance. Proximity to rice fields predicted higher total abundance when included in some models, but was not a significant predictor of phenology. Proximity to urban areas was not a significant predictor in any of our models. Predicted variations in start of the season and season length ranged from one to three weeks, across the measured range of variables. Predicted mosquito abundance was highly variable, with numbers in excess of 1000 per trap per year when late season temperatures were low (average 21°C) to only 150 when late season temperatures were high (average 30°C). CONCLUSIONS: Climate data collected early in the year, in conjunction with local land use, can be used to provide early warning of both the timing and magnitude of mosquito outbreaks. This potentially allows targeted mosquito control measures to be implemented, with implications for prevention and control of West Nile Virus and other mosquito borne diseases.

Brazil has a large share of hydropower in its electricity matrix. Since hydropower depends on water availability, it is particularly vulnerable to drought events, making the Brazilian electricity matrix vulnerable to climate change. Starting in 2005, Brazil opened the matrix to new renewable sources, including sugarcane-based electricity. Sugarcane is known for its resilience to short dry spells. Over the last decades, its production area moved from the coastal plains of the Atlantic Forest biome to the savannahs of the Cerrado biome, which is characterised by a five- to six month-long dry season. The sugarcane-based electricity system is highly dynamic and complex due to the interlinkages, dependencies, and cascading impacts between its agricultural and industrial subsystems. This paper applies the risk framework proposed by the IPCC to assess climate-change-driven drought risks to sugarcane electricity generation systems to identify their strengths and weaknesses, considering the system dynamics and linkages. Our methodology aims to understand and characterize drought in the agriculture as well as industrial subsystems and offers a specific understanding of the system by using indicators tailored to sugarcane-based electricity generation. Our results underline the relevance of actions at different levels of management. Initiatives, such as regional weather forecasts specifically for agriculture, and measures to increase industrial water-use efficiency were identified to be essential to reduce the drought risk. Actions from farmers and mill owners, supported and guided by the government at different levels, have the potential to increase the resilience of the system. For example, the implementation of small dams was identified by local actors as a promising intervention to adapt to the long dry seasons; however, they need to be implemented based on a proper technical assessment in order to locate these dams in suitable places. Moreover, the results show that creating and maintaining small water reservoirs to enable the adoption of deficit-controlled irrigation technology contribute to reducing the overall drought risk of the sugarcane-based electricity generation system.

Lipidic cubic phases (LCPs) have emerged as successful matrixes for the crystallization of membrane proteins. Moreover, the viscous LCP also provides a highly effective delivery medium for serial femtosecond crystallography (SFX) at X-ray free-electron lasers (XFELs). Here, the adaptation of this technology to perform serial millisecond crystallography (SMX) at more widely available synchrotron microfocus beamlines is described. Compared with conventional microcrystallography, LCP-SMX eliminates the need for difficult handling of individual crystals and allows for data collection at room temperature. The technology is demonstrated by solving a structure of the light-driven proton-pump bacteriorhodopsin (bR) at a resolution of 2.4 Å. The room-temperature structure of bR is very similar to previous cryogenic structures but shows small yet distinct differences in the retinal ligand and proton-transfer pathway.