Ancillary Data Research Articles

Cataclysmic variables from Sloan Digital Sky Survey - V (2020-2023) identified using machine learning.

Abstract SDSS-V is carrying out a dedicated survey for white dwarfs, single and in binaries, and we report the analysis of the spectroscopy of 504 cataclysmic variables (CVs) and CV candidates obtained during the first 34 months of observations of SDSS-V. We developed a convolutional neural network (CNN) to aid with the identification of CV candidates among the over 2 million SDSS-V spectra obtained with the BOSS spectrograph. The CNN reduced the number of spectra that required visual inspection to ≃ 2 per cent of the total. We identified 776 CV spectra among the CNN-selected candidates, plus an additional 27 CV spectra that the CNN misclassified, but that were found serendipitously by human inspection of the data. Analysing the SDSS-V spectroscopy and ancillary data of the 504 CVs in our sample, we report 61 new CVs, spectroscopically confirm 248 and refute 13 published CV candidates, and we report 82 new or improved orbital periods. We discuss the completeness and possible selection biases of the machine learning methodology, as well as the effectiveness of targeting CV candidates within SDSS-V. Finally, we re-assess the space density of CVs, and find 1.2 × 10−5 pc−3.

Reconstruction of Fine-Spatial-Resolution FY-3D-Based Vegetation Indices to Achieve Farmland-Scale Winter Wheat Yield Estimation via Fusion with Sentinel-2 Data

The spatial resolution (250–1000 m) of the FY-3D MERSI is too coarse for agricultural monitoring at the farmland scale (20–30 m). To achieve the winter wheat yield (WWY) at the farmland scale, based on FY-3D, a method framework is developed in this work. The enhanced deep convolutional spatiotemporal fusion network (EDCSTFN) was used to perform a spatiotemporal fusion on the 10 day interval FY-3D and Sentinel-2 vegetation indices (VIs), which were compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). In addition, a BP neural network was built to calculate the farmland-scale WWY based on the fused VIs, and the Aqua MODIS gross primary productivity product was used as ancillary data for WWY estimation. The results reveal that both the EDCSTFN and ESTARFM achieve satisfactory precision in the fusion of the Sentinel-2 and FY-3D VIs; however, when the period of spatiotemporal data fusion is relatively long, the EDCSTFN can achieve greater precision than ESTARFM. Finally, the WWY estimation results based on the fused VIs show remarkable correlations with the WWY data at the county scale and provide abundant spatial distribution details about the WWY, displaying great potential for accurate farmland-scale WWY estimations based on reconstructed fine-spatial-temporal-resolution FY-3D data.

The functions and factors governing fungal communities and diversity in agricultural waters: insights into the ecosystem services aquatic mycobiota provide

IntroductionFungi are essential to the aquatic food web, nutrient cycling, energy flow, and ecosystem regulation. Fungal community structures in water can be influenced by adjacent terrestrial environments, which drive and control some ecosystem services they provide. However, the roles of freshwater fungal communities remain underexplored compared to bacterial communities in this context.MethodsWe assessed the impact of anthropological and environmental factors on freshwater mycobiota in an agriculturally dominated water basin in eastern Ontario, Canada. We undertook bi-weekly surface water sampling from 2016 to 2021 and conducted fungal internal transcribed spacer 2 (ITS2) metabarcoding on the samples, complemented by ancillary data, including water physicochemical properties, upstream land use, hydrology, and weather conditions.ResultsOur study yielded 6,571 OTUs from 503 water samples, spanning 15 fungal phyla, dominated by Ascomycota, Basidiomycota, and Chytridiomycota. Agricultural land use was associated with decreased mycobiota alpha diversity and distinct fungal communities were observed at agricultural drainage ditch and mixed-land use sites compared to a forested site that had minimal anthropogenic activities in catchment. Notably, river discharge emerged as a predominant influencer of both community diversity and composition, likely amplified by precipitation-induced erosion and drainage from adjacent terrestrial environments.DiscussionWater physicochemical properties, including stream fungicide levels, explained a small proportion of the variation in mycobiota communities, underscoring the significance of unmeasured factors, alongside stochastic community assembly processes. Nevertheless, stream mycobiota demonstrated functional resilience for critical ecological processes under different environmental conditions. Altogether, these results highlight the complex interplay of factors influencing the freshwater mycobiota, which is essential for elevated understanding of the ecosystem services these fungi provide.

UVCANDELS: Catalogs of Photometric Redshifts and Galaxy Physical Properties

The UltraViolet imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) program provides deep Hubble Space Telescope (HST) F275W and F435W imaging over four CANDELS fields (GOODS-N, GOODS-S, COSMOS, and Extended Groth Strip). We combine this newly acquired UV imaging with existing HST imaging from CANDELS as well as existing ancillary data to obtain robust photometric redshifts and reliable estimates for galaxy physical properties for over 150,000 galaxies in the ∼430 arcmin2 UVCANDELS area. Here, we leverage the power of the new UV photometry to not only improve the photometric redshift measurements in these fields, but also constrain the full redshift probability distribution combining multiple redshift-fitting tools. Furthermore, using the full UV-to-IR photometric data set, we measure the galaxy physical properties by fitting templates from population synthesis models with two different parameterizations (flexible and fixed form) of the star formation histories (SFHs). Compared to the flexible SFH parameterization, we find that the fixed-form SFHs systematically underestimate the galaxy stellar masses, both at the low-mass (≲109 M ⊙) and high-mass (≳1010 M ⊙) end, by as much as ∼0.5 dex. This underestimation is primarily due the limited ability of fixed-form SFH parameterization to simultaneously capture the chaotic nature of star formation in these galaxies.

The Dark Energy Survey Supernova Program: Light Curves and 5 Yr Data Release

We present griz photometric light curves for the full 5 yr of the Dark Energy Survey Supernova (DES-SN) program, obtained with both forced point-spread function photometry on difference images (DiffImg) performed during survey operations, and scene modelling photometry (SMP) on search images processed after the survey. This release contains 31,636 DiffImg and 19,706 high-quality SMP light curves, the latter of which contain 1635 photometrically classified SNe that pass cosmology quality cuts. This sample spans the largest redshift (z) range ever covered by a single SN survey (0.1 < z < 1.13) and is the largest single sample from a single instrument of SNe ever used for cosmological constraints. We describe in detail the improvements made to obtain the final DES-SN photometry and provide a comparison to what was used in the 3 yr DES-SN spectroscopically confirmed Type Ia SN sample. We also include a comparative analysis of the performance of the SMP photometry with respect to the real-time DiffImg forced photometry and find that SMP photometry is more precise, more accurate, and less sensitive to the host-galaxy surface brightness anomaly. The public release of the light curves and ancillary data can be found at github.com/des-science/DES-SN5YR and doi:10.5281/zenodo.12720777.

Mapping Forest Carbon Stock Distribution in a Subtropical Region with the Integration of Airborne Lidar and Sentinel-2 Data

Forest carbon stock is an important indicator reflecting a forest ecosystem’s structures and functions. Its spatial distribution is valuable for managing natural resources, protecting ecosystems and biodiversity, and further promoting sustainability, but accurately mapping the forest carbon stock distribution in a large area is a challenging task. This study selected Changting County, Fujian Province, as a case study to explore a method to map the forest carbon stock distribution using the integration of airborne Lidar, Sentinel-2, and ancillary data in 2022. The Bayesian hierarchical modeling approach was used to estimate the local forest carbon stock based on airborne Lidar data and field measurements, and then the random forest approach was used to develop a regional forest carbon stock estimation model based on the Sentinel-2 and ancillary data. The results indicated that the Lidar-based carbon stock distribution effectively provided sample plots with good spatial representativeness for modeling regional carbon stock with a coefficient of determination (R2) of 0.7 and root mean square error (RMSE) of 12.94 t/ha. The average carbon stocks were 48.55 t/ha, 55.51 t/ha, and 57.04 t/ha for Masson pine, Chinese fir, and broadleaf forests, respectively. The carbon stock in non-conservation regions was 15.2–16.1 t/ha higher than that in conservation regions. This study provides a promising method through the use of airborne Lidar data as a linkage between sample plots and Sentinel-2 data to map the regional carbon stock distribution in those subtropical regions where serious soil erosion has led to a relatively sparse forest canopy density. The results are valuable for local government to make scientific decisions for promoting ecosystem restoration due to water and soil erosion.

Association of anthropogenic heat with asthma and related symptoms among children in China: A novel index reflecting climate change

BackgroundAnthropogenic heat (AH) is defined as the significant release of waste heat into the environment due to human activities, serving as a controllable heat source contributing to global climate change. However, epidemiological evidence establishing a clear association between AH and childhood asthma is currently lacking. ObjectivesTo explore the relationship between children's exposure to AH and asthma, as well as its related symptoms. MethodsThis population-based cross-sectional study, part of the National Chinese Children Health Study from 2012 to 2018, involved 188,145 children aged 6 to 18 years. We used multisource remote sensing images and ancillary data to estimate AH exposure. Data on asthma symptoms were collected through validated self-reported questionnaires. A generalized linear mixed model was applied to determine the associations. ResultsOur findings indicate a positive correlation between AH exposure and asthma risk in children. An interquartile range (IQR) increase in total AH was linked to higher odds of current asthma (OR: 1.15, 95 % CI: 1.10, 1.20) after adjusting for covariates. Categorizing AH by source, industrial AH exhibited the strongest effect, with an increased risk of current asthma (OR: 1.16, 95 % CI: 1.11, 1.22). Notably, younger children exhibited stronger associations between AH exposure and asthma-related symptoms, with boys showing heightened susceptibility, particularly for persistent cough. ConclusionThis study suggests that exposure to AH may elevate the risk of asthma and related symptoms, particularly in boys and younger children. Providing a foundation for developing practical strategies to mitigate the adverse impacts of global warming on respiratory health, while also guiding the formulation and evaluation of climate action and public health policies, and supporting sustainable urban development.

CAVITY: Calar Alto Void Integral-field Treasury surve. I. First public data release

The Calar Alto Void Integral-field Treasury surveY (CAVITY) is a legacy project aimed at characterising the population of galaxies inhabiting voids, which are the most under-dense regions of the cosmic web, located in the Local Universe. This paper describes the first public data release (DR1) of CAVITY, comprising science-grade optical data cubes for the initial 100 out of a total of sim 300 galaxies in the Local Universe ($0.005 &lt; z &lt; 0.050$). These data were acquired using the integral-field spectrograph PMAS/PPak mounted on the 3.5m telescope at the Calar Alto observatory. The DR1 galaxy sample encompasses diverse characteristics in the color–magnitude space, morphological type, stellar mass, and gas ionisation conditions, providing a rich resource for addressing key questions in galaxy evolution through spatially resolved spectroscopy. The galaxies in this study were observed with the low-resolution V500 set-up, spanning the wavelength range 3745–7500 with a spectral resolution of 6.0 (FWHM). Here, we describe the data reduction and characteristics and data structure of the CAVITY datasets essential for their scientific utilisation, highlighting such concerns as vignetting effects, as well as the identification of bad pixels and management of spatially correlated noise. We also provide instructions for accessing the CAVITY datasets and associated ancillary data through the project's dedicated database.

Forecast of CMB TB and EB correlations for AliCPT-1

The correlations between T, E modes and B modes in cosmic microwave background (CMB) radiation, which are expected to vanish under parity symmetry, have become a sensitive probe of the new physics beyond the standard model. In this paper, we forecast the estimation of TB and EB cross power spectra using NILC and cILC on AliCPT-1 simulations together with Planck HFI and WMAP K maps as ancillary data. We find that, NILC performs better than cILC on measuring TB and EB correlations in light of its lower uncertainties. In terms of the birefringence angle estimation without assuming systematic errors, the combination of CMB TB and EB spectra from NILC cleaned simulations could reach a sensitivity of |β| < 0.058∘ with 2σ significance for the first observing season of AliCPT. Tripling the survey duration will improve this sensitivity to |β| < 0.041∘.

An enhanced Standardized Precipitation–Evapotranspiration Index (SPEI) drought-monitoring method integrating land surface characteristics

Abstract. Atmospheric evaporative demand is a key metric for monitoring agricultural drought. Existing ways of estimating evaporative demand in drought indices do not faithfully represent the constraints imposed by land surface characteristics and become less accurate over nonuniform land surfaces. This study proposes incorporating surface vegetation characteristics, such as vegetation dynamics data, aerodynamic parameters, and physiological parameters, into existing potential-evapotranspiration (PET) methods. This approach is implemented across the continental United States (CONUS) for the period from 1981–2017 and is tested using a recently developed drought index, the Standardized Precipitation–Evapotranspiration Index (SPEI). We show that activating realistic maximum surface conductance and aerodynamic conductance could improve the prediction of soil moisture dynamics and drought impacts by 29 %–41 % on average compared to more simple, widely used methods. We also demonstrate that this is especially effective in forests and humid regions, with improvements of 86 %–89 %. Our approach only requires a minimal amount of ancillary data while allowing for both historical reconstruction and real-time drought forecasting. This offers a physically meaningful yet easy-to-implement way to account for vegetation control in drought indices.

An Exploration of IFLA LRM for Literature Data Representation

The digital humanities have witnessed a clear development in recent years due partly to their adoption of Semantic Web and linked data technologies and the creation of knowledge bases. In this work, we target the creation of an ontology and knowledge base for literature data representation based on the IFLA Library Reference Model (LRM). IFLA LRM is the main model for book-related data, allowing for a fine representation of the various layers that constitute a book. However, by design, it doesn’t deal with some aspects usually available in literature databases, such as information about authors, literary awards or book themes. As a result, LRM requires some extensions to be able to represent ancillary data. Another challenge is the querying of IFLA LRM knowledge bases, with a performance cost that comes with the fine-grained expressivity of the LRM model, which creates longer and therefore typically slower SPARQL queries. In this work, we propose an extension to the IFLA LRM ontology called IFLA LRM* that targets these limitations including a connection to the vocabulary Schema.org and to the taxonomies Thema and Dewey Decimal, and the representation of literary awards. We also present a practical case study on using our extended model to create a Quebec literature knowledge base, discussing the interest of our extensions.

NOEMA formIng Cluster survEy (NICE): Characterizing eight massive galaxy groups at 1.5 &lt; z &lt; 4 in the COSMOS field

The NOrthern Extended Millimeter Array (NOEMA) formIng Cluster survEy (NICE) is a NOEMA large programme targeting 69 massive galaxy group candidates at z &gt; 2 over six deep fields with a total area of 46 deg2. Here we report the spectroscopic confirmation of eight massive galaxy groups at redshifts 1.65 ≤ z ≤ 3.61 in the Cosmic Evolution Survey (COSMOS) field. Homogeneously selected as significant overdensities of red IRAC sources that have red Herschel colours, four groups in this sample are confirmed by CO and [CI] line detections of multiple sources with NOEMA 3 mm observations, three are confirmed with Atacama Large Millimeter Array (ALMA) observations, and one is confirmed by Hα emission from Subaru/FMOS spectroscopy. Using rich ancillary data in the far-infrared and sub-millimetre, we constructed the integrated far-infrared spectral energy distributions for the eight groups, obtaining a total infrared star formation rate (SFR) of 260–1300 M⊙ yr−1. We adopted six methods for estimating the dark matter masses of the eight groups, including stellar mass to halo mass relations, overdensity with galaxy bias, and NFW profile fitting to radial stellar mass densities. We find that the radial stellar mass densities of the eight groups are consistent with a NFW profile, supporting the idea that they are collapsed structures hosted by a single dark matter halo. The best halo mass estimates are log(Mh/M⊙) = 12.8 − 13.7 with a general uncertainty of 0.3 dex. Based on the halo mass estimates, we derived baryonic accretion rates (BARs) of (1 − 8)×103 M⊙/yr for this sample. Together with massive groups in the literature, we find a quasi-linear correlation between the integrated SFR/BAR ratio and the theoretical halo mass limit for cold streams, Mstream/Mh, with SFR/BAR = 10−0.46 ± 0.22(Mstream/Mh)0.71 ± 0.16 with a scatter of 0.40 dex. Furthermore, we compared the halo masses and the stellar masses with simulations, and find that the halo masses of all structures are consistent with those of progenitors of Mh(z = 0) &gt; 1014 M⊙ galaxy clusters, and that the most massive central galaxies have stellar masses consistent with those of the brightest cluster galaxy progenitors in the TNG300 simulation. Above all, the results strongly suggest that these massive structures are in the process of forming massive galaxy clusters via baryonic and dark matter accretion.

A novel global grid model for soil moisture retrieval considering geographical disparity in spaceborne GNSS-R

Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture (SM) retrieval. However, the accuracy of global SM retrieval using a single model is limited due to the complexity of land surface. Introducing redundant ancillary data may also result in over-reliance problems. Therefore, we propose a method for SM retrieval that considers geographical disparities using the data from Cyclone GNSS (CYGNSS) observations and Soil Moisture Active and Passive (SMAP) product. Based on the CYGNSS effective reflectivity and ancillary datasets of SMAP, we establish five models for each grid with different parameters to achieve global SM retrieval. Subsequently, an optimal model, determined by the performance indicator, is used for SM retrieval. The results show that the root mean square error SRMSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_{\\mathrm{RMSE}}$$\\end{document} with the improved method is decreased by 9.1% using SMAP SM as reference with the SRMSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_{\\mathrm{RMSE}}$$\\end{document} = 0.040 cm3/cm3 compared with using single reflectivity-temperature-vegetation method. Additionally, using the in-situ SM of International Soil Moisture Network as reference, the overall correlation coefficient R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R$$\\end{document} and SRMSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_{\\mathrm{RMSE}}$$\\end{document} values with the improved method are 0.80 and 0.064 cm3/cm3, respectively. The average R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R$$\\end{document} of the chosen sites is increased by 22.7%, and the average SRMSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_{\\mathrm{RMSE}}$$\\end{document} is decreased by 8.7%. The results indicate that the improved method can better retrieve SM in both global and local scales without redundant auxiliary data.

ALMA reveals a dust-obscured galaxy merger at cosmic noon

Context. Galaxy mergers play a critical role in galaxy evolution. They alter the size, morphology, dynamics, and composition of galaxies. Galaxy mergers have so far mostly been identified through visual inspection of their rest-frame optical and near-IR (NIR) emission. Dust can obscure this emission, however, resulting in the misclassification of mergers as single galaxies and in an incorrect interpretation of their baryonic properties. Aims. Having serendipitously discovered a dust-obscured galaxy merger at z = 1.17, we aim to determine the baryonic properties of the two merging galaxies, including the star formation rate (SFR) and the stellar, molecular gas and dust masses. Methods. Using Band 3 and 6 observations from the Atacama Large Millimeter and submillimeter Array (ALMA) and ancillary data, we studied the morphology of this previously misclassified merger. We deblended the emission, derived the gas masses from CO observations, and modeled the spectral energy distributions to determine the properties of each galaxy. Using the rare combination of ALMA CO(2–1), CO(5–4) and dust-continuum (rest-frame 520 μm) observations, we provide insight into the gas and dust content and into the properties of the interstellar medium of each merger component. Results. We find that only one of the two galaxies is highly obscured by dust, but both are massive (&gt; 1010.5 M⊙) and highly star forming (SFR = 60 − 900 M⊙/yr), have a moderate-to-short depletion time (tdepl &lt; 0.7 Gyr) and a high gas fraction (fgas ≥ 1). Conclusions. These properties can be interpreted as the positive impact of the merger. With this serendipitous discovery, we highlight the power of (sub)millimeter observations to identify and characterise the individual components of obscured galaxy mergers.

Spatio-temporal assessment of land use and land cover dynamics in Urmia lake basin of Iran: A bi-directional approach using optical and radar data on the Google Earth Engine platform

Sustainable development, a cornerstone in responsible land resource management, occupies a critical role in global efforts to balance economic progress with ecological preservation. This research explores the intricate dynamics of sustainable development and land resource management in the Lake Urmia watershed, northwestern Iran, with a specific focus on Land Use and Land Cover (LULC) alterations. Leveraging cutting-edge methodologies within the Google Earth Engine (GEE) framework, the study employs the Random Forest (RF) model, and multi-sensor satellite data to achieve meticulous LULC classification from the years 2016 to 2020.This strategy incorporates satellite images from Sentinel-1 (S1), Sentinel-2 (S2), coupled with ancillary data, and the implementation of the RF algorithm. It highlighted the intricate synergy between optical and Synthetic Aperture Radar (SAR) data for LULC classification in the Lake Urmia watershed. The Overall Accuracy (OA) values ranging from 88.28 % to 92.00 % affirmed the robustness of the RF model, demonstrating its potential for research objectives. In addition, Dynamic World, a near real-time global dataset, was applied to the study area, provides a convenient view to detect changes from the years 2016 to 2023, while enabling comparison with the widely used RF algorithm in past studies. Notably, this study goes beyond methodological developments to examine the concrete consequences of the observed changes. The decline in water bodies within the Urmia Lake basin and the parallel expansion of cultivated lands underscore the profound impact of human activities, particularly agricultural practices, on the ecological equilibrium. As we navigate the intricate terrain of sustainable development, this research serves as a clarion call for remote sensing approaches to monitor and manage land resources, emphasizing the delicate balance between economic growth and ecological preservation.

The Right Triangle Model: Overcoming the Sparse Data Problem in Thermal/Optical Remote Sensing of Soil Moisture

The simplicity of the so-called triangle method allows estimates of evapotranspiration and soil water content to be made without ancillary data external to the image and with just a few simple algebraic calculations. Drawing on many examples in the literature showing that the pixel distribution in temperature/fractional vegetation cover (NDVI) space closely resembles a right triangle, this paper shows that adoption of a right triangle shape further simplifies the triangle model. Moreover, it allows one to mostly avoid the problem of sparse or low-resolution data. A time dimension can be included showing that trajectories inside the triangle can provide additional information on root zone soil water content. After discussing some of the ambiguities in the triangle method, and the advantageous properties of the right triangle, a proposal is made to illuminate the relationship between thermal/optical measurements and root zone water content within the right triangle framework.

Clinical symptoms and faecal shedding of SARS-CoV-2 RNA among hospitalized COVID-19 patients: Implication for transmission.

SARS-CoV-2 shedding in human stool has been suggested as a probable route for faeco-oral transmission of the virus due to the availing evidence on the infectivity and pathogenicity of similar highly infectious respiratory viruses. Determining association of SARS-CoV-2 shedding in stools and presenting clinical status might be useful for prediction of the viral transmission spectrum and disease outcome. This study involved a descriptive cross-sectional survey of 91 consenting hospitalized, confirmed COVID-19 patients in Infectious Disease Isolation Centre, Oyo State, Nigeria. Socio-demographic characteristics and other ancillary data were collected from patient's hospital records with the aid of a structured investigator administered questionnaire. The laboratory detection of SARS-CoV-2 RNA in the stool of patients was performed using RT-PCR method. 27 (29.7%) of the 91 COVID-19 patients shed SARS-CoV-2 in their stools. The frequency of male (38.3%) patients shedding the virus in stools was higher than female (12.9%) patients (P = 0.012). Higher proportion of patients who had symptoms (41.2%) at admission shed the virus in their stool (P <0.007); particularly, fever (0.001), fatigue (0.003), headache (0.003), catarrh (0.001), and loss of smell (0.009). The frequency of viral shedding in stool was higher among patients with loss of taste (p = 0.028). Viral shedding in stool was significantly associated with low CT values (47.2%) and moderate CT value (21.4%) (P<0.05). Multivariate analysis showed that patients with moderate CT-value (OR = 0.28, 95% CI: 0.08-0.94, P = 0.039) and high CT-value (OR = 0.08, 95% CI: 0.01-0.80, P = 0.033) were less likely to shed the virus in stool. The gastrointestinal tract could be a route of SARS-CoV-2 transmission irrespective of the patients' clinical status. The low and moderate CT values of the nasopharyngeal swab is associated with shedding of the virus in patients' stools, although infectivity will depend on viral activity obtainable from further laboratory test analyses, such as viral culture.

An update of data compilation on the biological response to ocean acidification and overview of the OA-ICC data portal

Abstract. The number of studies investigating the effects of ocean acidification on marine organisms and communities increases every year. Results are not easily comparable since the carbonate chemistry and ancillary data are not always reported in similar units and scales and are not calculated using similar sets of constants. To facilitate data comparison, a data compilation hosted by the PANGAEA Data Publisher was initiated in 2008 and is updated on a regular basis (https://doi.org/10.1594/PANGAEA.962556; Ocean Acidification International Coordination Centre (OA-ICC), 2023). By November 2023, a total of 1501 datasets (over 25 million data points) from 1554 papers had been archived. To easily filter and access relevant biological response data from this compilation, a user-friendly portal (https://oa-icc.ipsl.fr, last access: 9 November 2023) was launched in 2018. Here, we present an update of this data compilation since its second description by Yang et al. (2016) and provide an overview of the OA-ICC portal for ocean acidification biological response data, launched in 2018. Most of the study sites from which data have been archived are in the North Atlantic Ocean, North Pacific Ocean, South Pacific Ocean, and Mediterranean Sea, while polar oceans are still relatively poorly represented. Mollusca and Cnidaria are still the best-represented taxonomic groups. The biological processes most reported in the datasets are growth and morphology. Other variables that can potentially be affected by ocean acidification and are often reported include calcification/dissolution, primary production/photosynthesis, and biomass/abundance. The majority of the compiled datasets have considered ocean acidification as a single stressor, but their relative contribution has decreased from 68 % before 2015 to 57 % today, showing a clear tendency towards more data archived from multifactorial studies.

Hyperspectral characterization of vegetation in hemiboreal, boreal and Arctic peatlands using a geographically extensive field dataset

Northern peatlands store up to 25% of global soil organic carbon and function as important hotspots for biodiversity. However, they are facing degradation from climate change driven by human activities as well as anthropogenic land use changes, up to the point of endangering the ecosystems' functioning and the storage of soil organic carbon entailed within them. The surface vegetation of northern peatlands is an important indicator of the ecosystem's functioning and ecohydrology, highlighting the importance of its large-scale, continuous monitoring. Approaches utilizing hyperspectral data for monitoring vegetation health and species composition can also be applied to peatland vegetation. To support the development of methods for interpreting hyperspectral satellite data from peatlands, we conducted a comprehensive in situ study of hemiboreal, boreal, sub-Arctic and Arctic peatland vegetation. We measured the reflectance spectra (350–2500 nm), soil moisture, and various vegetation-related attributes from a total of 446 vegetation plots in Estonia and Finland, from a 1500 km south-north interval. We then investigated (i) the spectral variation in surface vegetation of hemiboreal, boreal, sub-Arctic and Arctic peatlands and (ii) explored its connection to plant functional types (PFTs) and soil moisture, as well as evaluated the potential of hyperspectral data in estimating PFT cover using simple vegetation indices and partial least square (PLS) regression. Our results indicate that (i) the best spectral regions to retrieve information regarding the PFT vary greatly especially between vascular plants and bryophytes, (ii) the reflectance at an individual wavelength as well as simple vegetational index can, to some extent, predict the PFT, and that (iii) the PLS regression can predict the PFT with good accuracy. Overall, our findings demonstrate the potential of using hyperspectral data in monitoring PFTs in northern peatlands. The spectral library and the ancillary data from the peatland sites collected for this study are available as open data.

Inferring building height from footprint morphology data

As cities continue to grow globally, characterizing the built environment is essential to understanding human populations, projecting energy usage, monitoring urban heat island impacts, preventing environmental degradation, and planning for urban development. Buildings are a key component of the built environment and there is currently a lack of data on building height at the global level. Current methodologies for developing building height models that utilize remote sensing are limited in scale due to the high cost of data acquisition. Other approaches that leverage 2D features are restricted based on the volume of ancillary data necessary to infer height. Here, we find, through a series of experiments covering 74.55 million buildings from the United States, France, and Germany, it is possible, with 95% accuracy, to infer building height within 3 m of the true height using footprint morphology data. Our results show that leveraging individual building footprints can lead to accurate building height predictions while not requiring ancillary data, thus making this method applicable wherever building footprints are available. The finding that it is possible to infer building height from footprint data alone provides researchers a new method to leverage in relation to various applications.