Spatial spillovers of violent conflict amplify the impacts of climate variability on malaria risk in sub-Saharan Africa.

Africa carries a disproportionately high share of the global malaria burden, accounting for 94% of malaria cases and deaths worldwide in 2019. It is also a politically unstable region and the most vulnerable continent to climate change in recent decades. Knowledge about the modifying impacts of violent conflict on climate-malaria relationships remains limited. Here, we quantify the associations between violent conflict, climate variability, and malaria risk in sub-Saharan Africa using health surveys from 128,326 individuals, historical climate data, and 17,429 recorded violent conflicts from 2006 to 2017. We observe that spatial spillovers of violent conflict (SSVCs) have spatially distant effects on malaria risk. Malaria risk induced by SSVCs within 50 to 100 km from the households gradually increases from 0.1% (not significant, P>0.05) to 6.5% (95% CI: 0 to 13.0%). SSVCs significantly promote malaria risk within the average 20.1 to 26.9 °C range. At the 12-mo mean temperature of 22.5 °C, conflict deaths have the largest impact on malaria risk, with an approximately 5.8% increase (95% CI: 1.0 to 11.0%). Additionally, a pronounced association between SSVCs and malaria risk exists in the regions with 9.2 wet days per month. The results reveal that SSVCs increase population exposure to harsh environments, amplifying the effect of warm temperature and persistent precipitation on malaria transmission. Violent conflict therefore poses a substantial barrier to mosquito control and malaria elimination efforts in sub-Saharan Africa. Our findings support effective targeting of treatment programs and vector control activities in conflict-affected regions with a high malaria risk.

A Precipitation-Enhanced Emission (PEE) Strategy for Increasing the Brightness and Reducing the Liver Retention of NIR-II Fluorophores.

The lack of organic fluorophores with high quantum yields (QYs) and low liver retention in the second near-infrared (NIR-II) window has become a bottleneck in the bioimaging field. An approach to address these problems is proposed by encapsulating phosphorylated fluorescent dyes into biodegradable calcium phosphate nanoparticles. First, an NIR-II molecule, LJ-2P, is designed with increased water solubility by introducing two phosphate groups. Meanwhile, LJ-2P co-precipitates with calcium ions to form LJ-2P nanoparticles (NPs). The QYs of LJ-2P NPs in aqueous solution is increased by 36.57-fold to 5.12% compared with that of LJ-2P. This unique phenomenon is named as precipitation-enhanced emission (PEE), whose detailed mechanism is explored by femtosecond transient absorption. It is demonstrated that co-precipitation of LJ-2P with calcium ions changes the micro-environment, which restricts the molecular rotation and reduces the interaction of water molecules, especially the excited-state proton transfer. In addition, due to the pH-sensitive nature, more than 80% of the LJ-2P NPs are metabolized in the liver within 24 h. Based on the excellent optical properties and good biocompatibility, high-contrast vascular visualization and breast tumor detecting are achieved. This strategy can apply to other NIR-II fluorophores to achieve high QYs and low liver retention.

Simulating the spatiotemporal variations of oasis rural settlements in the upper reaches of rivers of arid regions in Xinjiang, China.

Rural settlements in oasis are primary habitations, and their changes are related to natural environment and anthropogenic activities. The spatiotemporal variations of rural settlements in an oasis are significant in arid regions. In this study, Qipan Township (QPT) and Yamansu Township (YMST) were chosen as a case study and validation case, respectively. Datasets, including Landsat images in 2002, 2010, and 2018, were collected. The cellular automata (CA)-agent-based model (ABM) and patch-generating land use simulation (PLUS) model were used to simulate the spatiotemporal dynamic variations of rural settlement and other land use types in the oasis in this study. Natural environmental, socioeconomic conditions, and human decision-making are the three driving factors that were used in the model. Human decision-making involves the actions of two types of agents: authority agent and resident agent. On the basis of land use data of 2002 and 2010, the rural settlement and other land use in 2018 were predicted using the CA-MAS and PLUS models. The following results were obtained: First, human decision-making behaviors were the leading factor in the changes of rural settlements in the CA-ABM model. Second, CA based on multiple random seed (CARS) of PLUS could better simulate the spatiotemporal variations of QPT rural settlements than CA-ABM and linear regression of PLUS. Similarly, CARS of PLUS also simulated the spatiotemporal evolution of rural settlements in YMST with high accuracy. Third, the areas of croplands, roads, and residential lands in QPT will expand to 20.7, 5.7, and 4.6 km2, respectively, in 2026, but the unused land will shrink, as predicted by CARS of PLUS. This study provides a scientific basis for the environmental protection of rural settlements in the oasis and sustainable settlement planning in arid regions.

Estimating Maize Yield in the Black Soil Region of Northeast China Using Land Surface Data Assimilation: Integrating a Crop Model and Remote Sensing.

Accurate yield estimation at the regional scale has always been a persistent challenge in the agricultural sector. With the vigorous emergence of remote sensing land surface observations in recent decades, data assimilation methodology has become an effective means to promote the accuracy and efficiency of yield estimation by integrating regional data and point-scale crop models. This paper focuses on the black soil area of Northeast China, a national strategic grain production base, applying the AquaCrop crop growth model to simulate the fractional vegetation cover (FVC) and maize yield from 2000 to 2020 and then forming a reliable FVC optimization dataset based on an ensemble Kalman filter (EnKF) assimilation algorithm with remote sensing products. Using the random forest model, the regression relationship between FVC and yield was established from the long-term time series data, which is crucial to achieve better yield estimation through the optimized FVC. The major findings include the following: (1) The R2 of the assimilated FVC and maize yield can reach 0.557. (2) When compared with the local statistical yield, our method reduced the mean absolute error (MAE) from 1.164 ton/ha (based on GLASS FVC products) to 1.004 ton/ha (based on the calibrated AquaCrop model) and then to 0.888 ton/ha (the result after assimilation). The above results show that we have proposed a yield estimation method to provide accurate yield estimations by combining data assimilation and machine learning. This study provided deep insights into understanding the variations in FVC and revealed the spatially explicit yield prediction ability from the time series land surface parameters, which has significant potential for optimizing water and soil resource management.

Influence of natural and anthropogenic controls on runoff in the Keriya River, central Tarim Basin, China.

The potential impact of natural factors on the runoff of intermittent rivers and ephemeral streams (IRES) has been largely ignored in the Tarim Basin, China. A representative example is the Keriya River. To quantify the long-term dynamic variations in lower reach surface runoff of IRES, river length, defined as the distance between a selected fix point along the perennial river segment to its dynamic, ephemeral end, was used as an indicator. Using a total of 272 remote sensing images, we digitized and measured the distance (river length) between the center of Yutian County and the river's end point on each image, and then calculated monthly inter-annual and intra-annual variations in length of the lower Keriya River from 2000 to 2019. Hydrometeorological data were combined with descriptors of anthropogenic disturbances to assess the relative influence of natural factors and anthropogenic disturbances on lower reach river runoff. The results showed that intra-annual variations in river length fluctuated seasonally, with the minimum value occurring in June; two main peaks occurred in March and August. The minimum June value in river length was closely linked to an increase in agricultural water demand and a decrease in upper reach runoff. The August peak in river length was related to the peak values in upper reach runoff and agricultural water demand; upper reach runoff made a significant contribution because the former was about 20% more than the latter in summer. The March peak corresponded to elevated lower reach groundwater levels and to the melting of ice along river channels. Inter-annual variations in river length were due to inter-annual variations in upper reach runoff and middle reach agricultural water use which increased slightly during the study period. Inter-annual variations in frequency and amplitude of the fluctuations in river length were mainly controlled by changes in upper reach runoff. The minimum in river length in 2009 was consistent with the low in upper reach runoff of the Keriya River and other rivers in the Tarim Basin. The most significant factors controlling variations in river length are natural in origin.

Spatial-temporal heterogeneity and meteorological factors of hand-foot-and-mouth disease in Xinjiang, China from 2008 to 2016.

The study aims to depict the temporal and spatial distributions of hand-foot-and-mouth disease (HFMD) in Xinjiang, China and reveal the relationships between the incidence of HFMD and meteorological factors in Xinjiang. With the national surveillance data of HFMD in Xinjiang and meteorological parameters in the study area from 2008 to 2016, in GeoDetector Model, we examined the effects of meteorological factors on the incidence of HFMD in Xinjiang, China, tested the spatial-temporal heterogeneity of HFMD risk, and explored the temporal-spatial patterns of HFMD through the spatial autocorrelation analysis. From 2008 to 2016, the HFMD distribution showed a distinct seasonal pattern and HFMD cases typically occurred from May to July and peaked in June in Xinjiang. Relative humidity, precipitation, barometric pressure and temperature had the more significant influences on the incidence of HFMD than other meteorological factors with the explanatory power of 0.30, 0.29, 0.29 and 0.21 (P<0.000). The interaction between any two meteorological factors had a nonlinear enhancement effect on the risk of HFMD. The relative risk in Northern Xinjiang was higher than that in Southern Xinjiang. Global spatial autocorrelation analysis results indicated a fluctuating trend over these years: the positive spatial dependency on the incidence of HFMD in 2008, 2010, 2012, 2014 and 2015, the negative spatial autocorrelation in 2009 and a random distribution pattern in 2011, 2013 and 2016. Our findings revealed the correlation between meteorological factors and the incidence of HFMD in Xinjiang. The correlation showed obvious spatiotemporal heterogeneity. The study provides the basis for the government to control HFMD based on meteorological information. The risk of HFMD can be predicted with appropriate meteorological factors for HFMD prevention and control.

Relationship between Air Pollutant Exposure and Gynecologic Cancer Risk.

Exposure to air pollution has been suggested to be associated with an increased risk of women's health disorders. However, it remains unknown to what extent changes in ambient air pollution affect gynecological cancer. In our case-control study, the logistic regression model was combined with the restricted cubic spline to examine the association of short-term exposure to air pollution with gynecological cancer events using the clinical data of 35,989 women in Beijing from December 2008 to December 2017. We assessed the women's exposure to air pollutants using the monitor located nearest to each woman's residence and working places, adjusting for age, occupation, ambient temperature, and ambient humidity. The adjusted odds ratios (ORs) were examined to evaluate gynecologic cancer risk in six time windows (Phase 1-Phase 6) of women's exposure to air pollutants (PM2.5, CO, O3, and SO2) and the highest ORs were found in Phase 4 (240 days). Then, the higher adjusted ORs were found associated with the increased concentrations of each pollutant (PM2.5, CO, O3, and SO2) in Phase 4. For instance, the adjusted OR of gynecological cancer risk for a 1.0-mg m-3 increase in CO exposures was 1.010 (95% CI: 0.881-1.139) below 0.8 mg m-3, 1.032 (95% CI: 0.871-1.194) at 0.8-1.0 mg m-3, 1.059 (95% CI: 0.973-1.145) at 1.0-1.4 mg m-3, and 1.120 (95% CI: 0.993-1.246) above 1.4 mg m-3. The ORs calculated in different air pollution levels accessed us to identify the nonlinear association between women's exposure to air pollutants (PM2.5, CO, O3, and SO2) and the gynecological cancer risk. This study supports that the gynecologic risks associated with air pollution should be considered in improved public health preventive measures and policymaking to minimize the dangerous effects of air pollution.

Coefficients optimization of the GLASS broadband emissivity based on FTIR and MODIS data over the Taklimakan Desert.

In this paper, the Taklimakan Desert land surface emissivity measured by portable Fourier Transform thermal InfraRed spectroscopy (FTIR) was used to re-estimate Global LAnd Surface Satellite (GLASS) BroadBand Emissivity (BBE) and Moderate Resolution Imaging Spectroradiometer (MODIS) BBE optimal coefficients equations. In addition, the revised BBE equations of both GLASS and MODIS with the optimized coefficients were obtained. Comparing the FTIR and MODIS BBE data with the values retrieved before and after the revised GLASS BBE equation, it was found that the revised GLASS BBE equation is more accurate than the original one. First, according to the error analysis with FTIR data, the value of R2 is increased from 0.4 to 0.9, and the Root Mean Square Error (RMSE) and Bias are reduced by 1 and 3 magnitude orders, respectively. Second, with MODIS BBE data, the value of R2 is increased from 0.6 to 0.9, the RMSE and Bias are reduced by 1 and 2 magnitude orders, respectively. Finally, the Taklimakan Desert BBE was calculated using the revised GLASS BBE equation. The results showed that the BBE values are between 0.890 and 0.920 in the desert center, between 0.920 and 0.950 in the sparse vegetation areas, and between 0.950 and 0.980 in the oasis edges.

Air pollution exposure associates with increased risk of neonatal jaundice.

Clinical experience suggests increased incidences of neonatal jaundice when air quality worsens, yet no studies have quantified this relationship. Here we reports investigations in 25,782 newborns showing an increase in newborn's bilirubin levels, the indicator of neonatal jaundice risk, by 0.076 (95% CI: 0.027-0.125), 0.029 (0.014-0.044) and 0.009 (95% CI: 0.002-0.016) mg/dL per µg/m3 for PM2.5 exposure in the concentration ranges of 10-35, 35-75 and 75-200 µg/m3, respectively. The response is 0.094 (0.077-0.111) and 0.161 (0.07-0.252) mg/dL per µg/m3 for SO2 exposure at 10-15 and above 15 µg/m3, respectively, and 0.351 (0.314-0.388) mg/dL per mg/m3 for CO exposure. Bilirubin levels increase linearly with exposure time between 0 and 48 h. Positive relationship between maternal exposure and newborn bilirubin level is also quantitated. The jaundice-pollution relationship is not affected by top-of-atmosphere incident solar irradiance and atmospheric visibility. Improving air quality may therefore be key to lowering the neonatal jaundice risk.

MicroRNA-140-5p aggravates hypertension and oxidative stress of atherosclerosis via targeting Nrf2 and Sirt2.

In the present study, the function of microRNA (miR)­140­5p on oxidative stress in mice with atherosclerosis was investigated. A reverse transcription­quantitative polymerase chain reaction assay was used to determine the expression of miR­140­5p. Oxidative stress kits and reactive oxygen species (ROS) kits were used to analyze alterations in oxidative stress and ROS levels. The alterations in protein expression were determined using western blot analysis and an immunofluorescence assay. miR­140­5p expression was increased in mice with atherosclerosis with hypertension. Consistently, miR­140­5p expression was also increased in mice with atherosclerosis. Upregulation of miR­140­5p increased oxidative stress and ROS levels by suppressing the protein expression of nuclear factor erythroid 2­related factor 2 (Nrf2), sirtuin 2 (Sirt2), Kelch­like enoyl­CoA hydratase­associated protein 1 (Keap1) and heme oxygenase 1 (HO­1) in vitro. By contrast, downregulation of miR­140­5p decreased oxidative stress and ROS levels by activating the protein expression of Nrf2, Sirt2, Keap1 and HO­1 in vitro. Sirt2 agonist or Nrf2 agonist inhibited the effects of miR­140­5p on oxidative stress in vitro. Collectively, these results suggested that miR­140­5p aggravated hypertension and oxidative stress of mice with atherosclerosis by targeting Nrf2 and Sirt2.

A Study on Optimal Strategy in Relative Radiometric Calibration for Optical Sensors.

Based on the analysis of three main factors involved in the relative radiometric calibration for optical sensors, namely: the number of radiance level; the number of measurements at each level; and the radiance level grouping method, an optimal strategy is presented in this paper for relative radiometric calibration. First, the maximization to the possible extent of either the number of the radiance level or the number of measurements at each level can improve the precision of the calibration results, where the recommended number of measurements is no less than 20. Second, when the number of the radiance level is divisible by four, dividing all the levels evenly into four groups by intensity gradient order and conducting averages for each group could achieve calibration results with the highest precision, which is higher than the result of no grouping or any other grouping method with the mean square error being 2 2 M n / I T (where M n is the mean square error of noise in the calibration data, I is the number of the radiance level, and T is the number of measurements for each level. In this case, the first two factors had an equivalent effect and showed their strongest effect on the precision. Third, when the calibration data were not evenly divided, the number of measurements demonstrated a stronger effect than the number of the radiance level. These cognitions are helping to achieve more precise relative radiometric calibration of optical sensors.

[A Forward Kernel Function for Fitting in situ Measured Snow Bidirectional Reflectance Factor].

Modelling and fitting the reflectance anisotropy of land surfaces is one of the most important issues in remote sensing studies. In the traditional linear kernel-driven model, the most widely used kernel functions are derived from radiative transfer model of vegetation canopy. Therefore, it is not validate to represent the forward scattering effect of snow/ice surfaces. We proposed a method by adding a forward kernel function to the traditional linear kernel-driven model, and validate it with in situ measured bidirectional reflectance factor (BRF) data. The validation results show that this method is efficient for fitting the BRF of snow/ice surfaces (R2=0.997 5, RMSE=0.022 6). We also compared it with empirical functions and the traditional linear kernel-driven model. The results show that: (1) The fitting results of linear kernel-driven model are better than those of empirical functions; (2) The fitting results can be significantly improved by adding the forward kernel function; (3) The fitting results of the improved linear kernel-driven model are stable at different wavelengths.

[Retrieval of Copper Pollution Information from Hyperspectral Satellite Data in a Vegetation Cover Mining Area].

Heavy metal mining activities have caused the complex influence on the ecological environment of the mining regions. For example, a large amount of acidic waste water containing heavy metal ions have be produced in the process of copper mining which can bring serious pollution to the ecological environment of the region. In the previous research work, bare soil is mainly taken as the research target when monitoring environmental pollution, and thus the effects of land surface vegetation have been ignored. It is well known that vegetation condition is one of the most important indictors to reflect the ecological change in a certain region and there is a significant linkage between the vegetation spectral characteristics and the heavy metal when the vegetation is effected by the heavy metal pollution. It means the vegetation is sensitive to heavy metal pollution by their physiological behaviors in response to the physiological ecology change of their growing environment. The conventional methods, which often rely on large amounts of field survey data and laboratorial chemical analysis, are time consuming and costing a lot of material resources. The spectrum analysis method using remote sensing technology can acquire the information of the heavy mental content in the vegetation without touching it. However, the retrieval of that information from the hyperspectral data is not an easy job due to the difficulty in figuring out the specific band, which is sensitive to the specific heavy metal, from a huge number of hyperspectral bands. Thus the selection of the sensitive band is the key of the spectrum analysis method. This paper proposed a statistical analysis method to find the feature band sensitive to heavy metal ion from the hyperspectral data and to then retrieve the metal content using the field survey data and the hyperspectral images from China Environment Satellite HJ-1. This method selected copper ion content in the leaves as the indicator of copper pollution level, using stepwise multiple linear regression and cross validation on the dataset which is consisting of 44 groups of copper ion content information in the polluted vegetation leaves from Dexing Copper Mine in Jiangxi Province to build up a statistical model by also incorporating the HJ-1 satellite images. This model was then used to estimate the copper content distribution over the whole research area at Dexing Copper Mine. The result has shown that there is strong statistical significance of the model which revealed the most sensitive waveband to copper ion is located at 516 nm. The distribution map illustrated that the copper ion content is generally in the range of 0-130 mg · kg⁻¹ in the vegetation covering area at Dexing Copper Mine and the most seriously polluted area is located at the South-east corner of Dexing City as well as the mining spots with a higher value between 80 and 100 mg · kg⁻¹. This result is consistent with the ground observation experiment data. The distribution map can certainly provide some important basic data on the copper pollution monitoring and treatment.

Satellite-based analysis of evapotranspiration and water balance in the grassland ecosystems of Dryland East Asia.

The regression tree method is used to upscale evapotranspiration (ET) measurements at eddy-covariance (EC) towers to the grassland ecosystems over the Dryland East Asia (DEA). The regression tree model was driven by satellite and meteorology datasets, and explained 82% and 76% of the variations of ET observations in the calibration and validation datasets, respectively. The annual ET estimates ranged from 222.6 to 269.1 mm yr(-1) over the DEA region with an average of 245.8 mm yr(-1) from 1982 through 2009. Ecosystem ET showed decreased trends over 61% of the DEA region during this period, especially in most regions of Mongolia and eastern Inner Mongolia due to decreased precipitation. The increased ET occurred primarily in the western and southern DEA region. Over the entire study area, water balance (the difference between precipitation and ecosystem ET) decreased substantially during the summer and growing season. Precipitation reduction was an important cause for the severe water deficits. The drying trend occurring in the grassland ecosystems of the DEA region can exert profound impacts on a variety of terrestrial ecosystem processes and functions.

[Temporal variation analysis for spectral reflectance of maize leaves using a fitting method].

The present study is to investigate the temporal variation patterns through the dataset of spectral reflectance of maize leaf using a fitting method. In the field experiment, 1 261 pieces of spectral reflectance of maize leaves at different leaf positions were measured every day during its life cycle. After signal/noise analysis, the visible and near infrared (VNIR) band (400-960 nm) was selected in this study. The spectral reflectance was fitted using a spectral scatter diagram (SSD) method. Seven fitting coefficients were employed to denote the temporal variation patterns of maize leaf, which can also be fitted by bi-variate quadratic functions. The comparison of the fitted results with the original measurement data shows that the fitting results are reasonably good, where for 98.7% leaves r is larger than 0.99, and for 80.9% leaves RMSE is less than 0.001 5. All the fitted spectral reflectance is compared with the original measurement data, where r is 0.997 8, and RMSE is 0.0105. The results show that this method is particularly suitable for presenting the temporal variation patterns of the spectral reflectance of maize leaves.

[A novel method for extracting leaf-level solar-induced fluorescence of typical crops under Cu stress].

The leaf-level solar-induced fluorescence changes when the typical crops are under Cu stress, which can be considered as a sensitive indicator to estimate the stress level. In the present study, wheat (Triticum aestivum L.), pea (Pisum sativum L.) and Chinese cabbage (Brassica campestris L.) were selected and cultured with copper solutions or copper polluted soil with different Cu stress. The apparent reflectance of leaves was measured by an ASD Fieldspec spectrometer and an integrating sphere. As the apparent reflectance was seldom affected by the fluorescence emission at 580-650 and 800-1000 nm, so the apparent solar-induced fluorescence can be separated from the apparent reflectance based on PROSPECT model. The re-absorption effect of chlorophyll was corrected by three methods, called GM (Gitelson et al.'s model), AM (Agati et al.'s model) and LM (Lagorio et al.'s model). After the re-absorption correction, the solar-induced fluorescence under different Cu stress was obtained, and a positive relationship was found between the height of far RED fluorescence (FRF) and the copper contents in leaves.

[Study on the spectrum response of Brassica Campestris L leaf to the zinc pollution].

In the present paper, the spectrum response of Brassica Campestris L leaf to the stress of heavy metal zinc pollution was studied in three spectral rangess of the red edge position (680-740 nm), the visible spectrum (460-680 nm) and the near infrared spectrum (750-1000 nm). The results indicate that the Zn content in cabbage leaves increases and the chlorophyll level reduces with the increase in Zn concentration in soil. With the Zn content of Brassica Campestris L leaves increasing, the leaf spectral reflectivity in visible light (A1) and the range of red edge shift (S) ascends, the the leaf spectral reflectivity in the near infrared light (A2) decreases. The three indices of A1, A2 and S are fitted much linearly with the logarithm of zinc content in Brassica Campestris L leaves with the high squared regression coefficients of 0.942, 0.981 and 0.969 respectively. The regression models are reliable to estimate the zinc content in Brassica Campestris L leaves.

[Studies of relationships between Cu pollution and spectral characteristics of TritiZnm aestivum L].

The effect of Cu pollution on the Cu uptake by wheat, the characteristics of chlorophyll concentration, and the visible-near infrared spectra were studied under the condition of solution culture, and the relationships among the three indices were discussed. The results indicate that the content of Cu in TritiZnm aestivum L. increases and the concentration of chlorophyll reduces with the increase of Cu in solution. The spectral characteristics also take on the disciplinary diversification (the spectral reflectivity ascended in the visible light band and decreased in the near infrared band; the range of red edge shift decreased) with the increase of Cu in solution.