Effects of fullerenes on a freshwater benthic community: toxicity and implications for ecological functions and services

Production of engineered carbon-based nanomaterials (CNM) is rising, with increased risk of release to the environment during production, transportation, use, and disposal. This trend highlights a need to understand potential impacts of CNM on the natural environment. Fullerenes (n-C60) are insoluble in water, and form aggregates that settle quickly, suggesting higher relative vulnerability of aquatic benthic ecosystems. This study aimed to determine eco-toxicity of fullerene and its functionalized derivatives on functionally representative benthic organisms, and evaluate how the potential lethal and sub-lethal effects of fullerene on these organisms indirectly impact benthic ecosystem function, including decomposition, primary productivity and nutrient cycling. We conducted chronic and acute traditional laboratory toxicity tests and a microcosm experiment in natural sediments. Standard toxicity tests indicated that population growth of Lumbriculus variegatus was reduced at 25 to 150 mg C60 kg-1, but C70 and the fullerene derivative C60-PCBM did not affect growth or weight of organisms in artificial sediments at 25 mg kg-1. Survivorship and growth were lower in natural sediments with historic contamination, but there was no measurable additive influence of C60. Photosynthesis by the benthic diatom Nitzschia palea was inhibited in the presence of C60, and at high exposure chlorophyll a increased. L. variegatus had strong effects on benthic ecosystem function, especially metabolism and nitrogen cycling, but C60 ≤ 30 mg kg-1 sediment did not influence the role of L. variegatus in driving benthic processes. These observations suggest that at moderate to high concentrations under ideal conditions, C60 may directly impact benthic organisms. However, under natural conditions with low to moderate concentrations, C60 does not indirectly impact the ecosystem processes maintained by such organisms. These results are a step further towards a better understanding of potential impacts of CNMs on aquatic ecosystems, and can aid in the development of regulatory policies

Estimating grassland biomass and livestock carrying capacity using Sentinel data to strengthen grazing management on local to national scales in Armenia

Livestock farming is an important part of the Armenian agricultural development strategy. The agricultural sector employs more than one third of Armenia’s labor force and accounts for 13% of GDP, hence threats to livestock and pastures can significantly impact livelihoods. For sustaining and developing that sector, fodder provision from grasslands is a key factor. Grasslands constitute 39% of the total territory of Armenia and 57% of the agricultural lands. Apart from resources for livestock, they provide important areas for biodiversity and ecosystem services. The condition of natural pastures and grasslands, however, is being deteriorated due to anthropogenic pressure and unsustainable management practices, leading to overgrazing and erosion. These risks are potentially further aggravated through climatic changes such as more frequent droughts, heat waves, and lack of snow cover. Hence, the setup of an integrated management approach for local decision-making becomes important, emphasizing the need of robust and up-to-date spatial data. In the context of the “GrassAM” project conducted by DLR and GIZ, we aimed at mapping grassland extent, grasslands types, grassland above ground biomass (AGB) and livestock carrying capacities at 10 m spatial resolution in the entire country of Armenia in the year 2020. In order to create a grassland mask for Armenia, a land use and land cover (LULC) classification was carried out using in situ data together with Sentinel-1, Sentinel-2, and digital elevation (DEM) data in a random forest classification approach implemented on Google Earth Engine. 400 sample points of 7 classes (“pasture”, “meadow”, “other grasslands”, “annual arable land”, “perennial arable land”, “bushland”, “bare soil”) were collected by the partner organization ICARE during summer 2020, distributed over all districts and ecological zones. To complement the classification, additional points were sampled on screen for the class “water”. Urban and forest areas were masked using DLR’s World Settlement Footprint 2015 [1] at 10 m resolution as well as the Hansen Global Forest Change maps [2] at 30 m resolution, respectively. The resulting classification achieved an overall accuracy of 80%, while the grassland area was slightly overestimated with 79% user’s accuracy and 92% producer’s accuracy. Of the 400 in situ sites, 147 pasture and meadow points also included wet and dry AGB samples. These measurements have been collected in one 30 x 30 cm plot per field (mowing at 2 cm height), which was assumed to be representative for the surrounding 30 x 30 m. The fresh plant mass was placed in a paper container, labeled and weighed with precision of 0.1 grams. Samples were then dried at room temperature for 48 - 72 hours and weighed again. The measured green AGB ranges from 1.733 – 27.800 kg/ha, with a mean of 12.367 kg/ha, and dry AGB ranges from 1.011 – 14.300 kg/ha, with a mean of 5.416 kg/ha. The AGB measurements were split 60/40 in training and validation data. To create a spatially balanced training data, the selection of training samples was based on spatial allocation of points in hexagon tessellation (1 point per grid cell; 2 points if there are more than four samples are available per cell). Biomass was modeled in a next step using a random forest regression model. The training samples have been used to test a set of 730 different geospatial features (monthly statistics and bi-weekly interpolated features of B2 - B12 Sentinel-2 bands and of eight vegetation indices, elevation, slope, monthly mean temperature at 2 m, monthly precipitation sums) as predictors using a Sequential Forward Feature Selection. Six features (Sentienl-2 mid-June and mid-July NDVI, Band 12 median, May precipitation, June temperature, elevation) were selected and achieved a R-square of 0.66 with an RMSE of 4.013 kg/ha for green AGB. The country-wide biomass maps are the basis to model grassland carrying capacity, i.e. the maximum number of cattle equivalent animals that can be sustained in a given grassland area in a season. AGB was multiplied with a proper use factor of 0.65 as it was suggested by [3] to estimate the available fodder. This amount is divided by the daily requirement of fodder per animal unit (equivalent of 400 kg live weight of cows) multiplied by pasture season length. For both quantities, landscape-zone specific assumptions have been made, resulting in an optimal stocking density of 1- 3 animals per hectare. Test for improving biomass and carrying capacity models as well as the input data sets are still ongoing. The resulting maps, that characterize the allowable grazing pressure on a country-wide scale, could be used to improve grassland management and to increase the resilience of grassland ecosystems to future climate conditions. [1] Marconcini, M., Metz-Marconcini, A., Üreyen, S., Palacios-Lopez, D., Hanke, W., Bachofer, F., Zeidler, J., Esch, T., Gorelick, N., Kakarla, A., & Strano, E. (2020). Outlining Where Humans Live –The World Settlements Footprint 2015. Scientific Data7(242). doi.org/10.1038/s41597-020-00580-5. [2] Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. [3] de Leeuw, J. Rizayeva, A., Namazov, E., Bayramov, E., Marshall, M. T., Etzold, J., Neudert,R. (2019): Application of the MODIS MOD 17 Net Primary Production product in grassland carrying capacity assessment, International Journal of Applied Earth Observation and Geoinformation 78, 66-76, https://doi.org/10.1016/j.jag.2018.09.014

DATA MINING IN ORGANIC GEOCHEMISTRY: CASE STUDY IN POTIGUAR BASIN: Mineração de dados na Geoquímica Orgânica: estudo de caso na Bacia Potiguar

The amount of data from geochemical analysis using samples collected in oil wells grows simultaneously to the investment in the exploration and production sector. On the other hand, the treatment and interpretation of these results are still very dependent on experts and demand time. With the generation of extensive databases, data mining presents itself as a good alternative to explore them through statistical methods and computational algorithms, providing technological differential and agility to the system. In an experimental way, with data from 200 oils from the Potiguar Basin, these tools were implemented, with the consequent suggestion of a workflow that would, in the end, return a reasonable accuracy in predicting their genetic classification. Using multidimensional scaling (MDS) and clustering (dendrogram and k-means types) from 60 initial attributes, the optimal set was reduced to 26. Applying Machine Learning, 92.50% of median accuracy were obtained in the Decision Tree algorithm, 95.00% in Random Forest and 87.50% in Artificial Neural Network. Comparing to an analysis previously presented at the pertinent literature, the benefits in terms of efficiency can be realized with the adoption of the methodology herein proposed.   Keywords: Organic geochemistry; Data Mining; Multivariate Statistics; Workflow.A quantidade de dados provenientes de análises geoquímicas de amostras coletadas em poços de petróleo cresce simultaneamente ao investimento no setor de exploração e produção. Por outro lado, o tratamento e a interpretação desses resultados ainda é muito dependente de especialistas, e demanda tempo. Com a geração de extensas bases de dados, a mineração de dados se apresenta como uma boa alternativa para explorá-los por meio de métodos estatísticos e computacionais, proporcionando diferencial tecnológico e agilidade ao sistema. De forma experimental, com dados de 200 óleos da Bacia Potiguar, essas ferramentas foram implementadas, com a consequente sugestão de um fluxo de trabalho que, ao final, pôde retornar uma precisão razoável na previsão da classificação genética das amostras. Usando escalonamento multidimensional (MDS) e agrupamentos (dos tipos dendrograma e k-means) de 60 atributos iniciais, o conjunto ideal foi reduzido para 26. Aplicando aprendizado de máquinas, 92,50% de acurácia mediana foram obtidos no algoritmo de Árvore de Decisão, 95,00% na Floresta Aleatória e 87,50% em Rede Neural Artificial. Comparando a uma análise previamente apresentada na literatura pertinente, os benefícios em termos de eficiência podem ser percebidos com a adoção da metodologia aqui proposta.   Palavras-chave: Geoquímica Orgânica; Mineração de dados; Estatística multivariada; Fluxo de Trabalho

Deep Learning on Synthetic Data Enables the Automatic Identification of Deficient Forested Windbreaks in the Paraguayan Chaco

Abstract: The Paraguayan Chaco is one of the most rapidly deforested areas in Latin America, mainly due to cattle ranching. Continuously forested windbreaks between agricultural areas and forest patches within these areas are mandatory to minimise the impact that the legally permitted logging has on the ecosystem. Due to the large area of the Paraguayan Chaco, comprehensive in situ monitoring of the integrity of these landscape elements is almost impossible. Satellite-based remote sensing offers excellent prerequisites for large-scale land cover analyses. However, traditional methods mostly focus on spectral and texture information while dismissing the geometric context of landscape features. Since the contextual information is very important for the identification of windbreak gaps and central forests, a deep learning-based detection of relevant landscape features in satellite imagery could solve the problem. However, deep learning methods require a large amount of labelled training data, which cannot be collected in sufficient quantity in the investigated area. This study presents a methodology to automatically classify gaps in windbreaks and central forest patches using a convolutional neural network (CNN) entirely trained on synthetic imagery. In a two-step approach, we first used a random forest (RF) classifier to derive a binary forest mask from Sentinel-1 and -2 images for the Paraguayan Chaco in 2020 with a spatial resolution of 10 m. We then trained a CNN on a synthetic data set consisting of purely artificial binary images to classify central forest patches and gaps in windbreaks in the forest mask. For both classes, the CNN achieved an F1 value of over 70%. The presented method is among the first to use synthetically generated training images and class labels to classify natural landscape elements in remote sensing imagery and therewith particularly contributes to the research on the detection of natural objects such as windbreaks

Global observations of fine-scale ocean surface topography with the surface water and ocean topography (SWOT) mission

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in [citation], doi:[doi]. Morrow, R., Fu, L., Ardhuin, F., Benkiran, M., Chapron, B., Cosme, E., d'Ovidio, F., Farrar, J. T., Gille, S. T., Lapeyre, G., Le Traon, P., Pascual, A., Ponte, A., Qiu, B., Rascle, N., Ubelmann, C., Wang, J., & Zaron, E. D. Global observations of fine-scale ocean surface topography with the surface water and ocean topography (SWOT) mission. Frontiers in Marine Science, 6(232),(2019), doi:10.3389/fmars.2019.00232.The future international Surface Water and Ocean Topography (SWOT) Mission, planned for launch in 2021, will make high-resolution 2D observations of sea-surface height using SAR radar interferometric techniques. SWOT will map the global and coastal oceans up to 77.6∘ latitude every 21 days over a swath of 120 km (20 km nadir gap). Today’s 2D mapped altimeter data can resolve ocean scales of 150 km wavelength whereas the SWOT measurement will extend our 2D observations down to 15–30 km, depending on sea state. SWOT will offer new opportunities to observe the oceanic dynamic processes at scales that are important in the generation and dissipation of kinetic energy in the ocean, and that facilitate the exchange of energy between the ocean interior and the upper layer. The active vertical exchanges linked to these scales have impacts on the local and global budgets of heat and carbon, and on nutrients for biogeochemical cycles. This review paper highlights the issues being addressed by the SWOT science community to understand SWOT’s very precise sea surface height (SSH)/surface pressure observations, and it explores how SWOT data will be combined with other satellite and in situ data and models to better understand the upper ocean 4D circulation (x, y, z, t) over the next decade. SWOT will provide unprecedented 2D ocean SSH observations down to 15–30 km in wavelength, which encompasses the scales of “balanced” geostrophic eddy motions, high-frequency internal tides and internal waves. This presents both a challenge in reconstructing the 4D upper ocean circulation, or in the assimilation of SSH in models, but also an opportunity to have global observations of the 2D structure of these phenomena, and to learn more about their interactions. At these small scales, ocean dynamics evolve rapidly, and combining SWOT 2D SSH data with other satellite or in situ data with different space-time coverage is also a challenge. SWOT’s new technology will be a forerunner for the future altimetric observing system, and so advancing on these issues today will pave the way for our future.The authors were mostly funded through the NASA Physical Oceanography Program and the CNES/TOSCA programs for the SWOT and OSTST Science teams. AnP acknowledges support from the Spanish Research Agency and the European Regional Development Fund (Award No. CTM2016-78607-P). AuP acknowledges support from the ANR EQUINOx (ANR-17-CE01-0006-01)

Supporting self-management for patients with Interstitial Lung Diseases:Utility and acceptability of digital devices

INTRODUCTION: Patients diagnosed with Interstitial Lung Diseases (ILD) use devices to self-monitor their health and well-being. Little is known about the range of devices, selection, frequency and terms of use and overall utility. We sought to quantify patients' usage and experiences with home digital devices, and further evaluate their perceived utility and barriers to adaptation.METHODS: A team of expert clinicians and patient partners interested in self-management approaches designed a 48-question cross-sectional electronic survey; specifically targeted at individuals diagnosed with ILD. The survey was critically appraised by the interdisciplinary self-management group at Royal Devon University Hospitals NHS Foundation Trust during a 6-month validation process. The survey was open for participation between September 2021 and December 2022, and responses were collected anonymously. Data were analysed descriptively for quantitative aspects and through thematic analysis for qualitative input.RESULTS: 104 patients accessed the survey and 89/104 (86%) reported a diagnosis of lung fibrosis, including 46/89 (52%) idiopathic pulmonary fibrosis (IPF) with 57/89 (64%) of participants diagnosed &gt;3 years and 59/89 (66%) female. 52/65(80%) were in the UK; 33/65 (51%) reported severe breathlessness medical research council MRC grade 3-4 and 32/65 (49%) disclosed co-morbid arthritis or joint problems. Of these, 18/83 (22%) used a hand- held spirometer, with only 6/17 (35%) advised on how to interpret the readings. Pulse oximetry devices were the most frequently used device by 35/71 (49%) and 20/64 (31%) measured their saturations more than once daily. 29/63 (46%) of respondents reported home-monitoring brought reassurance; of these, for 25/63 (40%) a feeling of control. 10/57 (18%) felt it had a negative effect, citing fluctuating readings as causing stress and 'paranoia'. The most likely help-seeking triggers were worsening breathlessness 53/65 (82%) and low oxygen saturation 43/65 (66%). Nurse specialists were the most frequent source of help 24/63 (38%). Conclusion: Patients can learn appropriate technical skills, yet perceptions of home-monitoring are variable; targeted assessment and tailored support is likely to be beneficial.</p

Global Observations of Fine-Scale Ocean Surface Topography With the Surface Water and Ocean Topography (SWOT) Mission

The future international Surface Water and Ocean Topography (SWOT) Mission, planned for launch in 2021, will make high-resolution 2D observations of sea-surface height using SAR radar interferometric techniques. SWOT will map the global and coastal oceans up to 77.6 latitude every 21 days over a swath of 120 km (20 km nadir gap). Today’s 2D mapped altimeter data can resolve ocean scales of 150 km wavelength whereas the SWOT measurement will extend our 2D observations down to 15–30 km, depending on sea state. SWOT will offer new opportunities to observe the oceanic dynamic processes at scales that are important in the generation and dissipation of kinetic energy in the ocean, and that facilitate the exchange of energy between the ocean interior and the upper layer. The active vertical exchanges linked to these scales have impacts on the local and global budgets of heat and carbon, and on nutrients for biogeochemical cycles. This review paper highlights the issues being addressed by the SWOT science community to understand SWOT’s very precise sea surface height (SSH)/surface pressure observations, and it explores how SWOT data will be combined with other satellite and in situ data and models to better understand the upper ocean 4D circulation (x, y, z, t) over the next decade. SWOT will provide unprecedented 2D ocean SSH observations down to 15–30 km in wavelength, which encompasses the scales of “balanced” geostrophic eddy motions, high-frequency internal tides and internal waves. Frontiers in Marine Science | www.frontiersin.org 1 May 2019 | Volume 6 | Article 232 Morrow et al. SWOT Fine-Scale Global Ocean Topography This presents both a challenge in reconstructing the 4D upper ocean circulation, or in the assimilation of SSH in models, but also an opportunity to have global observations of the 2D structure of these phenomena, and to learn more about their interactions. At these small scales, ocean dynamics evolve rapidly, and combining SWOT 2D SSH data with other satellite or in situ data with different space-time coverage is also a challenge. SWOT’s new technology will be a forerunner for the future altimetric observing system, and so advancing on these issues today will pave the way for our future

Refinement of the associations between risk of colorectal cancer and polymorphisms on chromosomes 1q41 and 12q13.13

In genome-wide association studies (GWASs) of colorectal cancer, we have identified two genomic regions in which pairs of tagging-single nucleotide polymorphisms (tagSNPs) are associated with disease; these comprise chromosomes 1q41 (rs6691170, rs6687758) and 12q13.13 (rs7163702, rs11169552). We investigated these regions further, aiming to determine whether they contain more than one independent association signal and/or to identify the SNPs most strongly associated with disease. Genotyping of additional sample sets at the original tagSNPs showed that, for both regions, the two tagSNPs were unlikely to identify a single haplotype on which the functional variation lay. Conversely, one of the pair of SNPs did not fully capture the association signal in each region. We therefore undertook more detailed analyses, using imputation, logistic regression, genealogical analysis using the GENECLUSTER program and haplotype analysis. In the 1q41 region, the SNP rs11118883 emerged as a strong candidate based on all these analyses, sufficient to account for the signals at both rs6691170 and rs6687758. rs11118883 lies within a region with strong evidence of transcriptional regulatory activity and has been associated with expression of PDGFRB mRNA. For 12q13.13, a complex situation was found: SNP rs7972465 showed stronger association than either rs11169552 or rs7136702, and GENECLUSTER found no good evidence for a two-SNP model. However, logistic regression and haplotype analyses supported a two-SNP model, in which a signal at the SNP rs706793 was added to that at rs11169552. Post-GWAS fine-mapping studies are challenging, but the use of multiple tools can assist in identifying candidate functional variants in at least some cases

The CIRCORT database: Reference ranges and seasonal changes in diurnal salivary cortisol derived from a meta-dataset comprised of 15 field studies

Diurnal salivary cortisol profiles are valuable indicators of adrenocortical functioning in epidemiological research and clinical practice. However, normative reference values derived from a large number of participants and across a wide age range are still missing. To fill this gap, data were compiled from 15 independently conducted field studies with a total of 104,623 salivary cortisol samples obtained from 18,698 unselected individuals (mean age: 48.3 years, age range: 0.5–98.5 years, 39% females). Besides providing a descriptive analysis of the complete dataset, we also performed mixed-effects growth curve modeling of diurnal salivary cortisol (i.e., 1–16 h after awakening). Cortisol decreased significantly across the day and was influenced by both, age and sex. Intriguingly, we also found a pronounced impact of sampling season with elevated diurnal cortisol in spring and decreased levels in autumn. However, the majority of variance was accounted for by between-participant and between-study variance components. Based on these analyses, reference ranges (LC/MS–MS calibrated) for cortisol concentrations in saliva were derived for different times across the day, with more specific reference ranges generated for males and females in different age categories. This integrative summary provides important reference values on salivary cortisol to aid basic scientists and clinicians in interpreting deviations from the normal diurnal cycle

Diagnostic delay for giant cell arteritis – a systematic review and meta-analysis

Background Giant cell arteritis (GCA), if untreated, can lead to blindness and stroke. The study’s objectives were to (1) determine a new evidence-based benchmark of the extent of diagnostic delay for GCA and (2) examine the role of GCA-specific characteristics on diagnostic delay. Methods Medical literature databases were searched from inception to November 2015. Articles were included if reporting a time-period of diagnostic delay between onset of GCA symptoms and diagnosis. Two reviewers assessed the quality of the final articles and extracted data from these. Random-effects meta-analysis was used to pool the mean time-period (95% confidence interval (CI)) between GCA symptom onset and diagnosis, and the delay observed for GCA-specific characteristics. Heterogeneity was assessed by I 2 and by 95% prediction interval (PI). Results Of 4128 articles initially identified, 16 provided data for meta-analysis. Mean diagnostic delay was 9.0 weeks (95% CI, 6.5 to 11.4) between symptom onset and GCA diagnosis (I 2 = 96.0%; P < 0.001; 95% PI, 0 to 19.2 weeks). Patients with a cranial presentation of GCA received a diagnosis after 7.7 (95% CI, 2.7 to 12.8) weeks (I 2 = 98.4%; P < 0.001; 95% PI, 0 to 27.6 weeks) and those with non-cranial GCA after 17.6 (95% CI, 9.7 to 25.5) weeks (I 2 = 96.6%; P < 0.001; 95% PI, 0 to 46.1 weeks). Conclusions The mean delay from symptom onset to GCA diagnosis was 9 weeks, or longer when cranial symptoms were absent. Our research provides an evidence-based benchmark for diagnostic delay of GCA and supports the need for improved public awareness and fast-track diagnostic pathways