Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Prenatal Ambient Air Pollution Exposure With Placental Mitochondrial DNA Copy Number, Telomere Length and Preeclampsia

Background: Studies have shown that ambient air pollution is linked to preeclampsia (PE), possibly via generation of oxidative stress in the placenta. Telomere length and mitochondrial DNA copy number (mtDNAcn) are sensitive to oxidative stress damage. Objective: To study the association between prenatal exposure to ambient nitrogen oxides (NOx, a marker for traffic-related air pollution), and PE, as well as potential mediation effects by placental telomere length and mtDNAcn. Methods: This is a cross-sectional study of 42 preeclamptic and 95 arbitrarily selected normotensive pregnant women with gestational ambient NOx exposure assessment in southern Scania, Sweden. Hourly concentrations of NOx were estimated at the residential addresses by a Gaussian-plume dispersion model with 100 × 100 m spatial resolutions and aggregated into trimester-specific mean concentrations. Placental relative mtDNAcn and telomere length were measured using qPCR. Linear and logistic regression models were used to investigate associations, adjusted for perinatal and seasonal characteristics. Results: Exposure was categorized into low and high exposures by median cut-offs during first [11.9 μg/m3; interquartile range (IQR) 7.9, 17.9], second (11.6 μg/m3; IQR: 7.1, 21.1), third trimesters (11.9 μg/m3; IQR: 7.7, 19.5) and entire pregnancy (12.0 μg/m3; IQR: 7.6, 20.1). Increased risk of PE was found for high prenatal NOx exposure during the first trimester (OR 4.0; 95% CI: 1.4, 11.1; p = 0.008), and entire pregnancy (OR 3.7; 95% CI: 1.3, 10.4; p = 0.012). High exposed group during the first trimester had lower placental relative mtDNAcn compared with low exposed group (−0.20; 95% CI: −0.36, −0.04; p = 0.01). Changes in relative mtDNAcn did not mediate the association between prenatal NOx exposure and PE. No statistically significant association was found between placental relative telomere length, prenatal NOx exposure and PE. Conclusion: In this region with relatively low levels of air pollution, ambient NOx exposure during the first trimester was associated with reduced placental relative mtDNAcn and an increased risk of PE. However, we did not find any evidence that mtDNAcn or TL mediated the association between air pollution and PE. Future research should further investigate the role of mtDNAcn for pregnancy complications in relation to exposure to ambient air pollution during pregnancy.
Worries, Preparedness, and Perceived Impact of Covid-19 Pandemic on Nurses' Mental Health

Background: In times of global public health emergency, such as the COVID-19 pandemic, nurses stand at the front line, working in close contact with infected individuals. Being actively engaged in fighting against COVID-19 exposes nurses to a high risk of being infected but can also have a serious impact on their mental health, as they are faced with excessive workload and emotional burden in many front-line operating contexts. Purpose: The aim of the study is to analyze how risk factors such as perceived impact, preparedness to the pandemic, and worries were associated with mental health outcomes (crying, rumination and stress) in nurses. Methods: A cross-sectional study design was performed via an online questionnaire survey. Participants included 894 registered nurses from Italy. Participation was voluntary and anonymous. Multiple binary logistic regression was carried out to analyze the relationship between risk factors and health outcomes. Results: Increased job stress was related to higher levels of rumination about the pandemic (OR = 4.04, p < 0.001), job demand (OR = 2.00, p < 0.001), impact on job role (OR = 2.56, p < 0.001), watching coworkers crying at work (OR = 1.50, p < 0.05), non-work-related concerns (OR = 2.28, p < 0.001), and fear of getting infected (OR = 2.05, p < 0.001). Job stress (OR = 2.52, p < 0.01), rumination (OR = 2.28, p < 0.001), and watching colleagues crying (OR = 7.92, p < 0.001) were associated with crying at work. Rumination was associated with caring for patients who died of COVID-19 (OR = 1.54, p < 0.05), job demand (OR = 1.70, p < 0.01), watching colleagues crying (OR = 1.81, p < 0.001), non-work-related worries (OR = 1.57, p < 0.05), and fear of getting infected (OR = 2.02, p < 0.001). Conclusions: The psychological impact that this pandemic may cause in the medium/long term could be greater than the economical one. This is the main challenge that health organizations will have to face in the future. This study highlights that the perceived impact and worries about the pandemic affect nurses' mental health and can impact on their overall effectiveness during the pandemic. Measures to enhance nurses' protection and to lessen the risk of depressive symptoms and post-traumatic stress should be planned promptly.
Infection of Two Heterologous Mycoviruses Reduces the Virulence of Valsa mali, a Fungal Agent of Apple Valsa Canker Disease

Mycovirus infection has been widely shown to attenuate the virulence of phytopathogenic fungi. Valsa mali is an agriculturally important fungus that causes Valsa canker disease in apple trees. In this study, two unrelated mycoviruses [Cryphonectria hypovirus 1 (CHV1, genus Hypovirus, and single-stranded RNA) and Mycoreovirus 1 (MyRV1, genus Mycoreovirus, double-stranded RNA)] that originated from Cryphonectria parasitica (chestnut blight fungus) were singly or doubly introduced into V. mali via protoplast fusion. CHV1 and MyRV1 stably infected V. mali and caused a reduction in fungal vegetative growth and virulence. Co-infection of both viruses further reduced the virulence of V. mali but compromised the stability of CHV1 infection and horizontal transmission through hyphal anastomosis. Infections of MyRV1 and, to a lesser extent, CHV1 up-regulated the transcript expression of RNA silencing-related genes in V. mali. The accumulation of CHV1 (but not MyRV1) was elevated by the knockdown of dcl2, a key gene of the RNA silencing pathway. Similarly, the accumulation of CHV1 and the efficiency of the horizontal transmission of CHV1 during co-infection was restored by the knockdown of dcl2. Thus, CHV1 and MyRV1 are potential biological control agents for apple Valsa canker disease, but co-infection of both viruses has a negative effect on CHV1 infection in V. mali due to the activation of antiviral RNA silencing by MyRV1 infection.
Effects of Incorporating Measured Leaf Optical Properties in Land Surface Models

Leaf optical properties (LOPs, i.e., leaf reflectance and transmittance), as a fundamental property of vegetation, are a key parameter in the canopy radiative transfer process. LOPs have a direct impact on the surface solar radiation partition and further affect surface flux exchanges. Recent works have provided reliable LOP data and mentioned that notable differences exist between the prescribed LOP values in current land surface models and measured LOP values, especially in the near-infrared (NIR) band. To evaluate the effects of different LOP values in land surface modeling, we ran two land surface models (the Community Land Model and the Common Land Model) with their default prescribed and measured values to examine the differences in simulated surface radiation partitions and fluxes. Our analyses show that differences in LOP values can lead to a large discrepancy in albedo, radiation partition, sensible heat flux and net radiation simulations. By using the measured LOP values, in the boreal forest zone, Southeast China, and the eastern United States, both models have a significantly increased surface albedo in the NIR band, with the difference exceeding 10% during JJA. Thus, the measured LOP values can improve the negative albedo bias in the boreal forest zone during summertime. Moreover, both models simulate less net radiation with a maximum reduction of 11 W / m 2 when incorporating the measured LOP values. Therefore, the total sensible heat flux can be reduced by as much as 11 W / m 2 . The results of this study emphasize that different LOP values can have a considerable effect on the surface radiation budget and sensible heat flux simulations which need attention in land surface model development. However, in current offline simulations, the measured LOP values cause slight changes in land surface temperatures and gross primary productivity (GPP).
The Impacts of Self-Construal and Perceived Risk on Technology Readiness

This paper explores how different self-construals interact with perceived risk and influence tourism consumers’ technology readiness toward tourism mobile apps. The study used 284 tourist participants to show that tourism consumers, regardless of self-construal type, have a negative perception of technology readiness when they perceive a high level of risk. Furthermore, those with independent self-construal have a more positive perception of technology readiness than those with an interdependent self-construal when they perceive a low level of risk. The results also show that technology readiness mediates the relationship between self-construal and use intention toward mobile apps. Based on these results, we proposed the following practical implications. First, companies need to find and lower potential risks that can be perceived in tourism mobile apps. They must also deliver different messages according to consumers’ self-construal. Companies should provide independents with information related to the positive benefits obtained through the mobile app, and interdependents should be provided with information that reduces perceived losses.
Improved Hydrological Loading Models in South America: Analysis of GPS Displacements Using M-SSA

Environmental loading, in particular from continental water storage changes, induces geodetic station displacements up to several centimeters for the vertical components. We investigate surface deformation due to loading processes in South America using a set of 247 permanent GPS (Global Positioning System) stations for the 2003–2016 period and compare them to loading estimates from global circulation models. Unfortunately, some of the hydrological components, and in particular surface waters, may be missing in hydrological models. This is especially an issue in South America where almost half of the seasonal water storage variations are due to surface water changes, e.g., rivers and floodplains. We derive river storage variations by rerouting runoffs of global hydrology models, allowing a better agreement with the mass variations observed from GRACE (Gravity Recovery and Climate Experiment) mission. We extract coherent seasonal GPS displacements using Multichannel Singular Spectrum Analysis (M-SSA) and show that modeling the river storage induced loading effects significantly improve the agreement between observed vertical and horizontal displacements and loading models. Such an agreement has been markedly achieved in the Amazon basin. Whilst the initial models only explained half of the amplitude of GPS, the new ones compensate for these gaps and remain consistent with GRACE.
Enhanced Pulsed-Source Localization with 3 Hydrophones: Uncertainty Estimates

The uncertainty behavior of an enhanced three-dimensional (3D) localization scheme for pulsed sources based on relative travel times at a large-aperture three-hydrophone array is studied. The localization scheme is an extension of a two-hydrophone localization approach based on time differences between direct and surface-reflected arrivals, an approach with significant advantages, but also drawbacks, such as left-right ambiguity, high range/depth uncertainties for broadside sources, and high bearing uncertainties for endfire sources. These drawbacks can be removed by adding a third hydrophone. The 3D localization problem is separated into two, a range/depth estimation problem, for which only the hydrophone depths are needed, and a bearing estimation problem, if the hydrophone geometry in the horizontal is known as well. The refraction of acoustic paths is taken into account using ray theory. The condition for existence of surface-reflected arrivals can be relaxed by considering arrivals with an upper turning point, allowing for localization at longer ranges. A Bayesian framework is adopted, allowing for the estimation of localization uncertainties. Uncertainty estimates are obtained through analytic predictions and simulations and they are compared against two-hydrophone localization uncertainties as well as against two-dimensional localization that is based on direct arrivals.
Earth's Time-Variable Gravity from GRACE Follow-On K-Band Range-Rates and Pseudo-Observed Orbits

During its science phase from 2002–2017, the low-low satellite-to-satellite tracking mission Gravity Field Recovery And Climate Experiment (GRACE) provided an insight into Earth’s time-variable gravity (TVG). The unprecedented quality of gravity field solutions from GRACE sensor data improved the understanding of mass changes in Earth’s system considerably. Monthly gravity field solutions as the main products of the GRACE mission, published by several analysis centers (ACs) from Europe, USA and China, became indispensable products for quantifying terrestrial water storage, ice sheet mass balance and sea level change. The successor mission GRACE Follow-On (GRACE-FO) was launched in May 2018 and proceeds observing Earth’s TVG. The Institute of Geodesy (IfE) at Leibniz University Hannover (LUH) is one of the most recent ACs. The purpose of this article is to give a detailed insight into the gravity field recovery processing strategy applied at LUH; to compare the obtained gravity field results to the gravity field solutions of other established ACs; and to compare the GRACE-FO performance to that of the preceding GRACE mission in terms of post-fit residuals. We use the in-house-developed MATLAB-based GRACE-SIGMA software to compute unconstrained solutions based on the generalized orbit determination of 3 h arcs. K-band range-rates (KBRR) and kinematic orbits are used as (pseudo)-observations. A comparison of the obtained solutions to the results of the GRACE-FO Science Data System (SDS) and Combination Service for Time-variable Gravity Fields (COST-G) ACs, reveals a competitive quality of our solutions. While the spectral and spatial noise levels slightly differ, the signal content of the solutions is similar among all ACs. The carried out comparison of GRACE and GRACE-FO KBRR post-fit residuals highlights an improvement of the GRACE-FO K-band ranging system performance. The overall amplitude of GRACE-FO post-fit residuals is about three times smaller, compared to GRACE. GRACE-FO post-fit residuals show less systematics, compared to GRACE. Nevertheless, the power spectral density of GRACE-FO and GRACE post-fit residuals is dominated by similar spikes located at multiples of the orbital and daily frequencies. To our knowledge, the detailed origin of these spikes and their influence on the gravity field recovery quality were not addressed in any study so far and therefore deserve further attention in the future. Presented results are based on 29 monthly gravity field solutions from June 2018 until December 2020. The regularly updated LUH-GRACE-FO-2020 time series of monthly gravity field solutions can be found on the website of the International Centre for Global Earth Models (ICGEM) and in LUH’s research data repository. These operationally published products complement the time series of the already established ACs and allow for a continuous and independent assessment of mass changes in Earth’s system.
Hyperspectral Image Classification across Different Datasets: A Generalization to Unseen Categories

With the rapid developments of hyperspectral imaging, the cost of collecting hyperspectral data has been lower, while the demand for reliable and detailed hyperspectral annotations has been much more substantial. However, limited by the difficulties of labelling annotations, most existing hyperspectral image (HSI) classification methods are trained and evaluated on a single hyperspectral data cube. It brings two significant challenges. On the one hand, many algorithms have reached a nearly perfect classification accuracy, but their trained models are hard to generalize to other datasets. On the other hand, since different hyperspectral datasets are usually not collected in the same scene, different datasets will contain different classes. To address these issues, in this paper, we propose a new paradigm for HSI classification, which is training and evaluating separately across different hyperspectral datasets. It is of great help to labelling hyperspectral data. However, it has rarely been studied in the hyperspectral community. In this work, we utilize a three-phase scheme, including feature embedding, feature mapping, and label reasoning. More specifically, we select a pair of datasets acquired by the same hyperspectral sensor, and the classifier learns from one dataset and then evaluated it on the other. Inspired by the latest advances in zero-shot learning, we introduce label semantic representation to establish associations between seen categories in the training set and unseen categories in the testing set. Extensive experiments on two pairs of datasets with different comparative methods have shown the effectiveness and potential of zero-shot learning in HSI classification.
Application and Evaluation of a Deep Learning Architecture to Urban Tree Canopy Mapping

Urban forest is a dynamic urban ecosystem that provides critical benefits to urban residents and the environment. Accurate mapping of urban forest plays an important role in greenspace management. In this study, we apply a deep learning model, the U-net, to urban tree canopy mapping using high-resolution aerial photographs. We evaluate the feasibility and effectiveness of the U-net in tree canopy mapping through experiments at four spatial scales—16 cm, 32 cm, 50 cm, and 100 cm. The overall performance of all approaches is validated on the ISPRS Vaihingen 2D Semantic Labeling dataset using four quantitative metrics, Dice, Intersection over Union, Overall Accuracy, and Kappa Coefficient. Two evaluations are performed to assess the model performance. Experimental results show that the U-net with the 32-cm input images perform the best with an overall accuracy of 0.9914 and an Intersection over Union of 0.9638. The U-net achieves the state-of-the-art overall performance in comparison with object-based image analysis approach and other deep learning frameworks. The outstanding performance of the U-net indicates a possibility of applying it to urban tree segmentation at a wide range of spatial scales. The U-net accurately recognizes and delineates tree canopy for different land cover features and has great potential to be adopted as an effective tool for high-resolution land cover mapping.
Quantitative Retrieval of Volcanic Sulphate Aerosols from IASI Observations

We developed a new retrieval algorithm based on the Infrared Atmospheric Sounding Interferometer (IASI) observations, called AEROIASI-H2SO4, to measure the extinction and mass concentration of sulphate aerosols (binary solution droplets of sulphuric acid and water), with moderate random uncertainties (typically ∼35% total uncertainty for column mass concentration estimations). The algorithm is based on a self-adapting Tikhonov–Phillips regularization method. It is here tested over a moderate-intensity eruption of Mount Etna volcano (18 March 2012), Italy, and is used to characterise this event in terms of the spatial distribution of the retrieved plume. Comparisons with simultaneous and independent aerosol optical depth observations from MODIS (Moderate Resolution Imaging Spectroradiometer), SO2 plume observations from IASI and simulations with the CHIMERE chemistry/transport model show that AEROIASI-H2SO4 correctly identifies the volcanic plume horizontal morphology, thus providing crucial new information towards the study of volcanic emissions, volcanic sulphur cycle in the atmosphere, plume evolution processes, and their impacts. Insights are given on the possible spectroscopic evidence of the presence in the plume of larger-sized particles than previously reported for secondary sulphate aerosols from volcanic eruptions.
Towards Vine Water Status Monitoring on a Large Scale Using Sentinel-2 Images

Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water supply at key development stages in case of severe drought. With climate change and the decrease of water availability, some vineyard regions face difficulties because of unsuitable variety, wrong vine management or due to the limited water access. Decision support tools are therefore required to optimize water use or to adapt agronomic practices. This study aimed at monitoring vine water status at a large scale with Sentinel-2 images. The goal was to provide a solution that would give spatialized and temporal information throughout the season on the water status of the vines. For this purpose, thirty six plots were monitored in total over three years (2018, 2019 and 2020). Vine water status was measured with stem water potential in field measurements from pea size to ripening stage. Simultaneously Sentinel-2 images were downloaded and processed to extract band reflectance values and compute vegetation indices. In our study, we tested five supervised regression machine learning algorithms to find possible relationships between stem water potential and data acquired from Sentinel-2 images (bands reflectance values and vegetation indices). Regression model using Red, NIR, Red-Edge and SWIR bands gave promising result to predict stem water potential (R2=0.40, RMSE=0.26).
Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC Data

The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater properties, a fundamental understanding of the uncertainties of normalized water-leaving radiance (nLw) products introduced by atmospheric correction algorithms is necessarily required. This paper presents the uncertainties by accessing GOCI-derived nLw products generated by two commonly used operational atmospheric algorithms, the Korea Ocean Satellite Center (KOSC) standard atmospheric algorithm adopted in GOCI Data Processing System (GDPS) and the NASA standard atmospheric algorithm implemented in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package), with Aerosol Robotic Network Ocean Color (AERONET-OC) provided nLw data. The nLw data acquired from the GOCI sensor based on two algorithms and four AERONET-OC sites of Ariake, Ieodo, Socheongcho, and Gageocho from October 2011 to March 2019 were obtained, matched, and analyzed. The GDPS-generated nLw data are slightly better than that with SeaDAS at visible bands; however, the mean percentage relative errors for both algorithms at blue bands are over 30%. The nLw data derived by GDPS is of better quality both in clear and turbid water, although underestimation is observed at near-infrared (NIR) band (865 nm) in turbid water. The nLw data derived by SeaDAS are underestimated in both clear and turbid water, and the underestimation worsens toward short visible bands. Moreover, both algorithms perform better at noon (02 and 03 Universal Time Coordinated (UTC)), and worse in the early morning and late afternoon. It is speculated that the uncertainties in nLw measurements arose from aerosol models, NIR water-leaving radiance correction method, and bidirectional reflectance distribution function (BRDF) correction method in corresponding atmospheric correction procedure.