Global area boom for greenhouse cultivation revealed by satellite mapping.

Greenhouse cultivation has been expanding rapidly in recent years, yet little knowledge exists on its global extent and expansion. Using commercial and freely available satellite data combined with artificial intelligence techniques, we present a global assessment of greenhouse cultivation coverage and map 1.3 million hectares of greenhouse infrastructures in 2019, a much larger extent than previously estimated. Our analysis includes both large (61%) and small-scale (39%) greenhouse infrastructures. Examining the temporal development of the 65 largest clusters (>1,500 ha), we show a recent upsurge in greenhouse cultivation in the Global South since the 2000s, including a dramatic increase in China, accounting for 60% of the global coverage. We emphasize the potential of greenhouse infrastructures to enhance food security but raise awareness of the uncertain environmental and social implications that may arise from this expansion. We further highlight the gap in spatio-temporal datasets for supporting future research agendas on this critical topic.

The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe.

Trees are an integral part in European landscapes, but only forest resources are systematically assessed by national inventories. The contribution of urban and agricultural trees to national-level carbon stocks remains largely unknown. Here we produced canopy cover, height and above-ground biomass maps from 3-meter resolution nanosatellite imagery across Europe. Our biomass estimates have a systematic bias of 7.6% (overestimation; R = 0.98) compared to national inventories of 30 countries, and our dataset is sufficiently highly resolved spatially to support the inclusion of tree biomass outside forests, which we quantify to 0.8 petagrams. Although this represents only 2% of the total tree biomass, large variations between countries are found (10% for UK) and trees in urban areas contribute substantially to national carbon stocks (8% for the Netherlands). The agreement with national inventory data, the scalability, and spatial details across landscapes, including trees outside forests, make our approach attractive for operational implementation to support national carbon stock inventory schemes.

No genetic causal association between dental caries and Alzheimer's disease: a bidirectional two-sample Mendelian randomization analysis.

Background: An increasing number of observational studies have suggested an association between dental caries and Alzheimer's disease (AD). The association between dental caries and Alzheimer's disease may be mediated by confounders or reverse causality. In this study, we conducted bidirectional two-sample Mendelian randomization (MR) to estimate the bidirectional causality between dental caries and AD. Materials and Methods: Genome-wide association study (GWAS) summary statistics of dental caries were extracted from a published meta-analysis which included a total of 487,823 participants. GWAS datasets of AD and AD onset age were obtained from the FinnGen bank. A bidirectional two-sample analysis was performed to explore the causality between dental caries and AD. Results: For the dental caries-AD causality estimation, there was no significant association between dental caries and AD, neither with the AD GWASs from the FinnGen database (OR: 1.041, p = 0.874) nor with those from the International Genomics of Alzheimer's Project (OR: 1.162, p = 0.409). In addition, the genetic susceptibility to dental caries was not related to the onset age of AD. No causality existed between dental caries and early-onset AD (OR: 0.515, p = 0.302) or late-onset AD (OR: 1.329, p = 0.347). For the AD-dental caries relationship, no causality was detected by the IVW method (OR: 1.000, p = 0.717). Findings from other MR methods were consistent. The pleiotropy test and sensitivity analysis confirmed the validity of these MR results. Conclusions: In this bidirectional MR study, robust evidence to support a bidirectional causal effect between dental caries and AD from the GWAS results within large-scale European-descent populations was absent. Having dental caries would not alter the onset age of AD.

The value of serum albumin concentration in predicting functional outcome of status epilepticus: An observational study.

OBJECTIVE: Status epilepticus (SE) is a common neurological emergency with unsatisfying prognoses, and accurate prediction of functional outcome is beneficial in clinical decision-making. The relationship between serum albumin concentration and outcome of SE patients has yet to be unveiled. METHODS: Clinical profiles of SE patients admitted to Xiangya Hospital, Central South University, from April 2017 to November 2020, were analyzed retrospectively. Outcomes of SE patients at discharge were divided into two groups based on the modified Rankin Scale (mRS): favorable outcome (mRS: 0-3) and unfavorable outcome (mRS: 4-6). RESULTS: Fifty-one patients were enrolled. Unfavorable functional outcome at discharge was reported in 60.8% (31/51). Serum albumin concentration at admission and the Encephalitis-NCSE-Diazepam resistance-Image abnormalities-Tracheal intubation (END-IT) score remained independent predictors for functional outcome of SE patients. A lower albumin concentration at admission and higher END-IT score indicated a higher chance of unfavorable outcome for SE patients. The cut-off value of serum albumin to predict unfavorable outcome was 35.2 g/L, with a sensitivity of 67.7% and specificity of 85.0%, and an area under the receiver operating characteristic curve (ROC) of .738 (95% CI: .600-.876, p = .004). The preferable END-IT score with optimal sensitivity (74.2%) and specificity (60%) was 2 and the area under the ROC was .742, with 95% CI of .608-.876 (p = .004). SIGNIFICANCE: Serum albumin concentration at admission and the END-IT score are two independent predictive factors for short-term outcome of SE patients, moreover, the serum albumin concentration is not inferior to the END-IT score in indicating functional outcome at discharge.

Biomarkers in cerebrospinal fluid for amyotrophic lateral sclerosis phenotypes.

OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease involving both upper and lower motor neurons. The motor phenotypes of ALS are highly clinically heterogeneous, and the underlying mechanisms are poorly understood. METHODS: A comparative proteomic analysis was performed in the cerebrospinal fluid (CSF) of bulbar-onset (BO) and spinal-onset (SO) ALS patients and controls (n = 14). Five biomarker candidates were selected from a differentially regulated protein pool, and further validation was performed in a larger independent cohort (n = 92) using enzyme-linked immunosorbent assay (ELISA). RESULTS: A total of 1732 CSF proteins were identified, and 78 differentially expressed proteins were found among BO-ALS patients, SO-ALS patients, and controls. Five promising biomarker candidates were selected for further validation, and lipopolysaccharide-binding protein (LBP) and HLA class II histocompatibility antigen, DR alpha chain (HLA-DRA) were validated. CSF LBP levels were increased in ALS patients compared with controls and higher in BO-ALS versus SO-ALS. The increased CSF LBP levels were correlated with the revised ALS Functional Scale (ALSFRS-R) score. CSF HLA-DRA levels were specifically elevated in BO-ALS patients, and there was no significant difference between SO-ALS patients and controls. Increased HLA-DRA expression was correlated with decreased survival. INTERPRETATION: Our data shows that elevated CSF LBP is a good biomarker for ALS and correlates with clinical severity, and increased HLA-DRA is a specific biomarker for BO-ALS and may predict short survival. It also suggests that the microglial pathway and HLA-II-related adaptive immunity may be differentially involved in ALS phenotypes and may be new therapeutic targets for ALS.

More than one quarter of Africa's tree cover is found outside areas previously classified as forest.

The consistent monitoring of trees both inside and outside of forests is key to sustainable land management. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we use the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. Our prototype map of 2019 (RMSE = 9.57%, bias = -6.9%). demonstrates that a precise assessment of all tree-based ecosystems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover in state-of-the-art maps, such as in croplands and grassland. Such accurate mapping of tree cover down to the level of individual trees and consistent among countries has the potential to redefine land use impacts in non-forest landscapes, move beyond the need for forest definitions, and build the basis for natural climate solutions and tree-related studies.

Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale.

Sustainable tree resource management is the key to mitigating climate warming, fostering a green economy, and protecting valuable habitats. Detailed knowledge about tree resources is a prerequisite for such management but is conventionally based on plot-scale data, which often neglects trees outside forests. Here, we present a deep learning-based framework that provides location, crown area, and height for individual overstory trees from aerial images at country scale. We apply the framework on data covering Denmark and show that large trees (stem diameter >10 cm) can be identified with a low bias (12.5%) and that trees outside forests contribute to 30% of the total tree cover, which is typically unrecognized in national inventories. The bias is high (46.6%) when our results are evaluated against all trees taller than 1.3 m, which involve undetectable small or understory trees. Furthermore, we demonstrate that only marginal effort is needed to transfer our framework to data from Finland, despite markedly dissimilar data sources. Our work lays the foundation for digitalized national databases, where large trees are spatially traceable and manageable.

Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda.

Trees sustain livelihoods and mitigate climate change but a predominance of trees outside forests and limited resources make it difficult for many tropical countries to conduct automated nation-wide inventories. Here, we propose an approach to map the carbon stock of each individual overstory tree at the national scale of Rwanda using aerial imagery from 2008 and deep learning. We show that 72% of the mapped trees are located in farmlands and savannas and 17% in plantations, accounting for 48.6% of the national aboveground carbon stocks. Natural forests cover 11% of the total tree count and 51.4% of the national carbon stocks, with an overall carbon stock uncertainty of 16.9%. The mapping of all trees allows partitioning to any landscapes classification and is urgently needed for effective planning and monitoring of restoration activities as well as for optimization of carbon sequestration, biodiversity and economic benefits of trees.

An unexpectedly large count of trees in the West African Sahara and Sahel.

A large proportion of dryland trees and shrubs (hereafter referred to collectively as trees) grow in isolation, without canopy closure. These non-forest trees have a crucial role in biodiversity, and provide ecosystem services such as carbon storage, food resources and shelter for humans and animals1,2. However, most public interest relating to trees is devoted to forests, and trees outside of forests are not well-documented3. Here we map the crown size of each tree more than 3 m2 in size over a land area that spans 1.3 million km2 in the West African Sahara, Sahel and sub-humid zone, using submetre-resolution satellite imagery and deep learning4. We detected over 1.8 billion individual trees (13.4 trees per hectare), with a median crown size of 12 m2, along a rainfall gradient from 0 to 1,000 mm per year. The canopy cover increases from 0.1% (0.7 trees per hectare) in hyper-arid areas, through 1.6% (9.9 trees per hectare) in arid and 5.6% (30.1 trees per hectare) in semi-arid zones, to 13.3% (47 trees per hectare) in sub-humid areas. Although the overall canopy cover is low, the relatively high density of isolated trees challenges prevailing narratives about dryland desertification5-7, and even the desert shows a surprisingly high tree density. Our assessment suggests a way to monitor trees outside of forests globally, and to explore their role in mitigating degradation, climate change and poverty.

A Low-Complexity Compressed Sensing Reconstruction Method for Heart Signal Biometric Recognition.

Biometric systems allow recognition and verification of an individual through his or her physiological or behavioral characteristics. It is a growing field of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method that has been proposed in recent years. The sampling and compression of data is completed synchronously, avoiding waste of resources and meeting the requirements of small size and limited power consumption of wearable portable devices. In this work, a compression reconstruction method based on compression sensing was studied using bioelectric signals, which aimed to increase the limited resources of portable remote bioelectric signal recognition equipment. Using electrocardiograms (ECGs) and photoplethysmograms (PPGs) of heart signals as research data, an improved segmented weak orthogonal matching pursuit (OMP) algorithm was developed to compress and reconstruct the signals. Finally, feature values were extracted from the reconstructed signals for identification and analysis. The accuracy of the proposed method and the practicability of compression sensing in cardiac signal identification were verified. Experiments showed that the reconstructed ECG and PPG signal recognition rates were 95.65% and 91.31%, respectively, and that the residual value was less than 0.05 mV, which indicates that the proposed method can be effectively used for two bioelectric signal compression reconstructions.