Human footprints in the Global South accelerate biomass carbon loss in ecologically sensitive regions.

Human activities have placed significant pressure on the terrestrial biosphere, leading to ecosystem degradation and carbon losses. However, the full impact of these activities on terrestrial biomass carbon remains unexplored. In this study, we examined changes in global human footprint (HFP) and human-induced aboveground biomass carbon (AGBC) losses from 2000 to 2018. Our findings show an increasing trend in HFP globally, resulting in the conversion of wilderness areas to highly modified regions. These changes have altered global biomes' habitats, particularly in tropical and subtropical regions. We also found accelerated AGBC loss driven by HFP expansion, with a total loss of 19.99 ± 0.196 PgC from 2000 to 2018, especially in tropical regions. Additionally, AGBC is more vulnerable in the Global South than in the Global North. Human activities threaten natural habitats, resulting in increasing AGBC loss even in strictly protected areas. Therefore, scientifically guided planning of future human activities is crucial to protect half of Earth through mitigation and adaptation under future risks of climate change and global urbanization.

Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model.

Green leaf area index (LAI) is an important variable related to crop growth. Accurate and timely information on LAI is essential for developing suitable field management strategies to mitigate risk and boost yield. Several remote sensing (RS) based methods have been recently developed to estimate LAI at the regional scale. However, the performance of these methods tends to be affected by the quality of RS data, especially when time-series LAI are required. For crop LAI estimation, supplementary growth information from crop model is helpful to address this issue. In this study, we focus on the regional-scale LAI estimations of spring maize for the entire growth season. Using time-series multispectral RS data acquired by an unmanned aerial vehicle (UAV) and the World Food Studies (WOFOST) crop model, three methods were applied at different crop growth stages: empirical method using vegetation index (VI), data assimilation method and hybrid method. The VI-based method and assimilation method were used to generate time-series LAI estimations for the whole crop growth season. Then, a hybrid method specially for the late-stage LAI retrieval was developed by integrating WOFOST model and data assimilation. Using field-collected LAI data in Hongxing Farm in 2014, the performances of these three methods were evaluated. At the early stage, the VI-based method (R2 = 0.63, RMSE = 0.16, n = 36) achieved higher accuracy than the assimilation method (R2 = 0.54, RMSE = 0.52, n = 36), whereas at the mid stage, the assimilation method (R2 = 0.63, RMSE = 0.46, n = 28) showed higher accuracy than the VI-based method (R2 = 0.41, RMSE = 0.51, n = 28). At the late stage, the hybrid method yielded the highest accuracy (R2 = 0.63, RMSE = 0.46, n = 29), compared with the VI-based method (R2 = 0.19, RMSE = 0.43, n = 28) and the assimilation method (R2 = 0.20, RMSE = 0.44, n = 29). Based on the results above, we considered a combination of the three methods, i.e., the VI-based method for the early stage, the assimilation method for the mid stage, and the hybrid method for the late stage, as an ideal strategy for spring-maize LAI estimation for the entire growth season of 2014 in Hongxing Farm, and the accuracy of the combined method over the whole growth season is higher than that of any single method.

[Comprehensive evaluation of postpartum depression and correlations between postpartum depression and serum levels of homocysteine in Chinese women].

OBJECTIVE: To investigate whether the level of homocysteine in patients with postpartum depression is associated with depression index. METHODS: A total of 43 women with postpartum depression or with potential postpartum depression, who visited the psychological clinic of Maternal and Child Health Hospital of Hunan Province from June, 2012 to April, 2014, were enrolled in this study. They were evaluated by the Edinburgh Postnatal Depression Scale and Hamilton Depression Scale. Chinese Classification of Mental Disorder (the third edition) was used for their diagnosis. The depressive index was calculated by Edinburgh Postnatal Depression Scale, Hamilton Depression Scale, and clinical symptom scores, which was used to assess the level of depressive symptoms. The level of homocysteine in serum was detected by chemoluminescent method. Meanwhile, another 31 women, who visited the hospital without postpartum depression, were used as controls to compare with the 43 patients. RESULTS: The homocysteine level in the women with postpartum depression was significantly higher than that in the control group [(10.09 ± 3.59) µmol/L vs (8.57 ± 1.59) µmol/L, t=12.392, P=0.001]. The depression index was positively correlated with the level of homocysteine (r=0.231, P<0.05). CONCLUSION: The level of serum homocysteine is associated with postpartum depression, suggesting that the level of serum homocysteine might be a risk biomarker for postpartum depression.

Radar vegetation indices for monitoring surface vegetation: Developments, challenges, and trends.

Monitoring surface vegetation is essential for environmental protection, disaster prevention, and carbon sequestration in forests. However, optical remote-sensing methods and their derivative technologies typically fail to fully meet this requirement due to constraints such as lighting and weather. Radar vegetation indices (RVIs), developed based on microwave remote-sensing data, describe the dielectric properties and morphological structure of vegetation and have been applied for vegetation monitoring at various scales. This technical review is the first to systematically summarize RVIs; it analyzes and discusses their principles, developments, categories and applications, and provides a comprehensive guide for their use. Additionally, the challenges faced by RVIs, as well as their applicability, were analyzed, and future improvements and development trends were carefully projected. The selection of RVIs must consider the type of data used, the terrain and location of the study area, and the major vegetation types. The effectiveness of RVIs applied to vegetation monitoring can be affected by various factors, including index performance, sensor type, study area, and data type and quality. These factors reduce the reliability and robustness of results, as well as guide the improvement direction of RVIs. The development of technologies, such as artificial intelligence, in remote sensing offers new possibilities for RVIs, enabling the removal of background scattering, improvement in interpretation accuracy, and reduction in application thresholds. Additionally, the development trends in high resolution, multi-polarization, multi-base, multi-dimensional, and networked synthetic aperture radar (SAR) and their satellite platforms offer data support for the next generation of RVIs. The rapid development of RVIs strongly supports the use of surface vegetation monitoring and terrestrial ecosystem research.

A 30-m annual corn residue coverage dataset from 2013 to 2021 in Northeast China.

Crop residue cover plays a key role in the protection of black soil by covering the soil in the non-growing season against wind erosion and chopping for returning to the soil to increase organic matter in the future. Although there are some studies that have mapped the crop residue coverage by remote sensing technique, the results are mainly on a small scale, limiting the generalizability of the results. In this study, we present a novel corn residue coverage (CRC) dataset for Northeast China spanning the years 2013-2021. The aim of our dataset is to provide a basis to describe and monitor CRC for black soil protection. The accuracy of our estimation results was validated against previous studies and measured data, demonstrating high accuracy with a coefficient of determination (R2) of 0.7304 and root mean square error (RMSE) of 0.1247 between estimated and measured CRC in field campaigns. In addition, it is the first of its kind to offer the longest time series, enhancing its significance in long-term monitoring and analysis.

Mapping annual 10-m soybean cropland with spatiotemporal sample migration.

China, as the world's biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering and collating ground survey data for different crops. We proposed a spatiotemporal migration method leveraging vegetation indices' temporal characteristics. This method uses a feature space of six integrals from the crops' phenological curves and a concavity-convexity index to distinguish soybean and non-soybean samples in cropland. Using a limited number of actual samples and our method, we extracted features from optical time-series images throughout the soybean growing season. The cloud and rain-affected data were supplemented with SAR data. We then used the random forest algorithm for classification. Consequently, we developed the 10-meter resolution ChinaSoybean10 maps for the ten primary soybean-producing provinces from 2019 to 2022. The map showed an overall accuracy of about 93%, aligning significantly with the statistical yearbook data, confirming its reliability. This research aids soybean growth monitoring, yield estimation, strategy development, resource management, and food scarcity mitigation, and promotes sustainable agriculture.

Iron Oxide Nanoparticles Decorated with Functional Peptides for a Targeted siRNA Delivery to Glioma Cells.

Glioma is a deadly form of brain cancer, and the difficulty of treating glioma is exacerbated by the chemotherapeutic resistance developed in the tumor cells over the time of treatment. siRNA can be used to silence the gene responsible for the increased resistance, and sensitize the glioma cells to drugs. Here, iron oxide nanoparticles functionalized with peptides (NP-CTX-R10) were used to deliver siRNA to silence O6-methylguanine-DNA methyltransferase (MGMT) to sensitize tumor cells to alkylating drug, Temozolomide (TMZ). The NP-CTX-R10 could complex with siRNA through electrostatic interactions and was able to deliver the siRNA to different glioma cells. The targeting ligand chlorotoxin and cell penetrating peptide polyarginine (R10) enhanced the transfection capability of siRNA to a level comparable to commercially available Lipofectamine. The NP-siRNA was able to achieve up to 90% gene silencing. Glioma cells transfected with NP-siRNA targeting MGMT showed significantly elevated sensitivity to TMZ treatment. This nanoparticle formulation demonstrates the ability to protect siRNA from degradation and to efficiently deliver the siRNA to induce therapeutic gene knockdown.

Iron Oxide Nanoparticle-Mediated mRNA Delivery to Hard-to-Transfect Cancer Cells.

mRNA-based therapeutics have emerged as a promising strategy for cancer treatment. However, the effective delivery of mRNA into hard-to-transfect cancer cells remains a significant challenge. This study introduces a novel approach that utilizes iron oxide nanoparticles (NPs) synthesized through a layer-by-layer (LbL) method for safe and efficient mRNA delivery. The developed NPs consist of an iron oxide core modified with a thin charge-bearing layer, an mRNA middle layer, and an outer layer composed of perfluorinated polyethyleneimine with heparin (PPH), which facilitates efficient mRNA delivery. Through a comparative analysis of four nanoparticle delivery formulations, we investigated the effects of the iron oxide core's surface chemistry and surface charge on mRNA complexation, cellular uptake, and mRNA release. We identified an optimal and effective mRNA delivery platform, namely, (IOCCP)-mRNA-PPH, capable of transporting mRNA into various hard-to-transfect cancer cell lines in vitro. The (IOCCP)-mRNA-PPH formulation demonstrated significant enhancements in cellular internalization of mRNA, facilitated endosomal escape, enabled easy mRNA release, and exhibited minimal cytotoxicity. These findings suggest that (IOCCP)-mRNA-PPH holds great promise as a solution for mRNA therapy against hard-to-transfect cancers.

A comprehensive analysis of the crop effect on the urban-rural differences in land surface phenology.

The response of land surface phenology (LSP) to the urban heat island effect (UHI) is a useful biological indicator for understanding how vegetated ecosystems will be affected by future climate warming. However, vegetation cover in rural areas is often dominated by cultivated land, whose phenological timing is considerably influenced by agricultural managements (e.g., timing of sowing and harvesting), leading to biased conclusions derived from the urban-rural LSP differences. To demonstrate this problem, we investigated the crop influence on the phenological response to a warmer environment resulting from the UHI effect. We partitioned cities in the United States into cultivated and non-cultivated categories according to the proportion of crops in rural areas. We then built continuous buffer zones starting from the urban boundary to explore the urban-rural LSP differences considering the UHI effect on them. The results suggest crop inclusion is likely to lead to >14 days of urban-rural differences at both the start of the season (SOS) and the end of the season (EOS) between cultivated and non-cultivated cities. The temperature sensitivity (ST) of SOS is overestimated by approximately 2.7 days/°C, whereas the EOS is underestimated by 3.6 days/°C. Removing crop-dominated pixels (i.e., above 50 %) can minimize the influence of crop planting/harvesting on LSP and derive reliable results. We, therefore, suggest explicit consideration of crop impacts in future studies of phenological differences between urban and rural areas and the UHI effect on LSP in urban domains, as presented by this comprehensive study.

Mapping annual 10-m maize cropland changes in China during 2017-2021.

China contributed nearly one-fifth of the world maize production over the past few years. Mapping the distributions of maize cropland in China is crucial to ensure global food security. Nonetheless, 10 m maize cropland maps in China are still unavailable, restricting the promotion of sustainable agriculture. In this paper, we collect numerous samples to produce annual 10-m maize cropland maps in China from 2017 to 2021 with a machine learning based classification framework. To overcome the temporal variations of plants, the proposed framework takes Sentinel-2 sequence images as input and utilizes deep neural networks and random forest as classifiers to map maize in a zone-specific way. The generated maps have an overall accuracy (OA) spanning from 0.87 to 0.95 and the maize-cultivated areas estimated by the maps are highly consistent with the records in statistical yearbooks (R2 varying from 0.83 to 0.95). To the best of our knowledge, this is the first annual 10-m maize maps across China, which largely facilitates the sustainable agriculture development in China dominated by smallholder farmlands.

A method for estimating yield of maize inbred lines by assimilating WOFOST model with Sentinel-2 satellite data.

Maize is the most widely planted food crop in China, and maize inbred lines, as the basis of maize genetic breeding and seed breeding, have a significant impact on China's seed security and food safety. Satellite remote sensing technology has been widely used for growth monitoring and yield estimation of various crops, but it is still doubtful whether the existing remote sensing monitoring means can distinguish the growth difference between maize inbred lines and hybrids and accurately estimate the yield of maize inbred lines. This paper explores a method for estimating the yield of maize inbred lines based on the assimilation of crop models and remote sensing data, initially solves the problem. At first, this paper analyzed the WOFOST(World Food Studies)model parameter sensitivity and used the MCMC(Markov Chain Monte Carlo) method to calibrate the sensitive parameters to obtain the parameter set of maize inbred lines differing from common hybrid maize; then the vegetation indices were selected to establish an empirical model with the measured LAI(Leaf Area Index) at three key development stages to obtain the remotely sensed estimated LAI; finally, the yield of maize inbred lines in the study area was estimated and mapped pixel by pixel using the EnKF(Ensemble Kalman Filter) data assimilation algorithm. Also, this paper compares a method of assimilation by setting a single parameter. Instead of the WOFOST parameter optimization process, a parameter representing the growth weakness of the inbred lines was set in WOFOST to distinguish the inbred lines from the hybrids. The results showed that the yield estimated by the two methods compared with the field measured yield data had R2: 0.56 and 0.18, and RMSE: 684.90 Kg/Ha and 949.95 Kg/Ha, respectively, which proved that the crop growth model of maize inbred lines established in this study combined with the data assimilation method could initially achieve the growth monitoring and yield estimation of maize inbred lines.

Structural complexity biases vegetation greenness measures.

Vegetation 'greenness' characterized by spectral vegetation indices (VIs) is an integrative measure of vegetation leaf abundance, biochemical properties and pigment composition. Surprisingly, satellite observations reveal that several major VIs over the US Corn Belt are higher than those over the Amazon rainforest, despite the forests having a greater leaf area. This contradicting pattern underscores the pressing need to understand the underlying drivers and their impacts to prevent misinterpretations. Here we show that macroscale shadows cast by complex forest structures result in lower greenness measures compared with those cast by structurally simple and homogeneous crops. The shadow-induced contradictory pattern of VIs is inevitable because most Earth-observing satellites do not view the Earth in the solar direction and thus view shadows due to the sun-sensor geometry. The shadow impacts have important implications for the interpretation of VIs and solar-induced chlorophyll fluorescence as measures of global vegetation changes. For instance, a land-conversion process from forests to crops over the Amazon shows notable increases in VIs despite a decrease in leaf area. Our findings highlight the importance of considering shadow impacts to accurately interpret remotely sensed VIs and solar-induced chlorophyll fluorescence for assessing global vegetation and its changes.

Association between single-nucleotide polymorphisms of DNMT3L and infertility with azoospermia in Chinese men.

The gene for DNA methyltransferase 3-like protein (DNMT3L) is essential for normal spermatogenesis and may be involved with spermatogenetic impairment and male infertility. To explore the possible association between the DNMT3L gene and male infertility, this study investigated allele, genotype and haplotype frequencies of three single nucleotide polymorphism (SNP) loci, rs2070565, rs2276248 and rs7354779, of DNMT3L in 233 infertile patients with azoospermia and 249 fertile controls from a population of Chinese men using polymerase chain reaction/restriction fragment length polymorphism. Results showed that the frequencies of allele A (20.6% versus 14.9%; P = 0.022) and the allele A carrier (GA + AA; 37.8% versus 28.1%; P = 0.027) in azoospermic patients were significantly higher than those in controls at the rs2070565 locus. The haplotype AAA frequency was significantly higher (18.1% versus 12.4%; P = 0.02) while the haplotype GAA frequency was significantly lower (53.2% versus 62.1%; P = 0.007) in infertile patients compared with fertile controls. These results indicated that SNP rs2070565, as well as haplotypes AAA and GAA, may be associated with male infertility and suggest that DNMT3L may contribute to azoospermia susceptibility in humans.

Chitosan-Crosslinked Low Molecular Weight PEI-Conjugated Iron Oxide Nanoparticle for Safe and Effective DNA Delivery to Breast Cancer Cells.

Breast cancer has attracted tremendous research interest in treatment development as one of the major threats to public health. The use of non-viral carriers for therapeutic DNA delivery has shown promise in treating various cancer types, including breast cancer, due to their high DNA loading capacity, high cell transfection efficiency, and design versatility. However, cytotoxicity and large sizes of non-viral DNA carriers often raise safety concerns and hinder their applications in the clinic. Here we report the development of a novel nanoparticle formulation (termed NP-Chi-xPEI) that can safely and effectively deliver DNA into breast cancer cells for successful transfection. The nanoparticle is composed of an iron oxide core coated with low molecular weight (800 Da) polyethyleneimine crosslinked with chitosan via biodegradable disulfide bonds. The NP-Chi-xPEI can condense DNA into a small nanoparticle with the overall size of less than 100 nm and offer full DNA protection. Its biodegradable coating of small-molecular weight xPEI and mildly positive surface charge confer extra biocompatibility. NP-Chi-xPEI-mediated DNA delivery was shown to achieve high transfection efficiency across multiple breast cancer cell lines with significantly lower cytotoxicity as compared to the commercial transfection agent Lipofectamine 3000. With demonstrated favorable physicochemical properties and functionality, NP-Chi-xPEI may serve as a reliable vehicle to deliver DNA to breast cancer cells.

A dataset of winter wheat aboveground biomass in China during 2007-2015 based on data assimilation.

As a key variable to characterize the process of crop growth, the aboveground biomass (AGB) plays an important role in crop management and production. Process-based models and remote sensing are two important scientific methods for crop AGB estimation. In this study, we combined observations from agricultural meteorological stations and county-level yield statistics to calibrate a process-based crop growth model for winter wheat. After that, we assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015. The validation using ground measurements also suggests the derived AGB dataset agrees well with the filed observations, i.e., the R2 is above 0.9, and the root mean square error (RMSE) reaches 1,377 kg·ha-1. Compared to county-level statistics during 2007-2015, the ranges of R2, RMSE, and mean absolute percentage error (MAPE) are 0.73~0.89, 953~1,503 kg·ha-1, and 8%~12%, respectively. We believe our dataset can be helpful for relevant studies on regional agricultural production management and yield estimation.

A global record of annual terrestrial Human Footprint dataset from 2000 to 2018.

Human Footprint, the pressure imposed on the eco-environment by changing ecological processes and natural landscapes, is raising worldwide concerns on biodiversity and ecological conservation. Due to the lack of spatiotemporally consistent datasets of Human Footprint over a long temporal span, many relevant studies on this topic have been limited. Here, we mapped the annual dynamics of the global Human Footprint from 2000 to 2018 using eight variables that reflect different aspects of human pressures. The accuracy assessment revealed a good agreement between our mapped results and the previously developed datasets in different years. We found more than two million km2 of wilderness (i.e., regions with Human Footprint values below one) were lost over the past two decades. The biome dominated by mangroves experienced the most significant loss (i.e., above 5%) of wilderness, likely attributed to intensified human activities in coastal areas. The derived annual and spatiotemporally consistent global Human Footprint can be a fundamental dataset for many relevant studies about human activities and natural resources.

Electrospun nanofibers for 3-D cancer models, diagnostics, and therapy.

As one of the leading causes of global mortality, cancer has prompted extensive research and development to advance efficacious drug discovery, sustained drug delivery and improved sensitivity in diagnosis. Towards these applications, nanofibers synthesized by electrospinning have exhibited great clinical potential as a biomimetic tumor microenvironment model for drug screening, a controllable platform for localized, prolonged drug release for cancer therapy, and a highly sensitive cancer diagnostic tool for capture and isolation of circulating tumor cells in the bloodstream and for detection of cancer-associated biomarkers. This review provides an overview of applied nanofiber design with focus on versatile electrospinning fabrication techniques. The influence of topographical, physical, and biochemical properties on the function of nanofiber assemblies is discussed, as well as current and foreseeable barriers to the clinical translation of applied nanofibers in the field of oncology.

Mapping the Corn Residue-Covered Types Using Multi-Scale Feature Fusion and Supervised Learning Method by Chinese GF-2 PMS Image.

The management of crop residue covering is a vital part of conservation tillage, which protects black soil by reducing soil erosion and increasing soil organic carbon. Accurate and rapid classification of corn residue-covered types is significant for monitoring crop residue management. The remote sensing technology using high spatial resolution images is an effective means to classify the crop residue-covered areas quickly and objectively in the regional area. Unfortunately, the classification of crop residue-covered area is tricky because there is intra-object heterogeneity, as a two-edged sword of high resolution, and spectral confusion resulting from different straw mulching ways. Therefore, this study focuses on exploring the multi-scale feature fusion method and classification method to classify the corn residue-covered areas effectively and accurately using Chinese high-resolution GF-2 PMS images in the regional area. First, the multi-scale image features are built by compressing pixel domain details with the wavelet and principal component analysis (PCA), which has been verified to effectively alleviate intra-object heterogeneity of corn residue-covered areas on GF-2 PMS images. Second, the optimal image dataset (OID) is identified by comparing model accuracy based on the fusion of different features. Third, the 1D-CNN_CA method is proposed by combining one-dimensional convolutional neural networks (1D-CNN) and attention mechanisms, which are used to classify corn residue-covered areas based on the OID. Comparison of the naive Bayesian (NB), random forest (RF), support vector machine (SVM), and 1D-CNN methods indicate that the residue-covered areas can be classified effectively using the 1D-CNN-CA method with the highest accuracy (Kappa: 96.92% and overall accuracy (OA): 97.26%). Finally, the most appropriate machine learning model and the connected domain calibration method are combined to improve the visualization, which are further used to classify the corn residue-covered areas into three covering types. In addition, the study showed the superiority of multi-scale image features by comparing the contribution of the different image features in the classification of corn residue-covered areas.

The divergent response of vegetation phenology to urbanization: A case study of Beijing city, China.

Characterizing the relationship between vegetation phenology and urbanization indicators is essential to understand the impacts of human activities on urban ecosystems. In this study, we explored the response of vegetation phenology to urbanization in Beijing (China) during 2001-2018, using impervious surface area (ISA) and the information of urban-rural gradients (i.e., concentric rings from the urban core to surrounding rural areas) as the urbanization indicators. We found the change rates of vegetation phenology in urban areas are 1.3 and 1.1 days per year for start of season (SOS) and end of season (EOS), respectively, about three times faster than that in forest. Moreover, we found a divergent response of SOS with the increase of ISA, which differs from previous results with advanced SOS in the urban environment than surrounding rural areas. This might be attributed to the mixed land cover types and the thermal environment caused by the urban heat island in the urban environment. Similarly, a divergent pattern of phenological indicators along the urban-rural gradient shows a non-linear response of vegetation phenology to urbanization. These findings provide new insights into the complicated interactions between vegetation phenology and urban environments. High-resolution weather data are required to support process-based vegetation phenology models in the future, particularly under different global urbanization and climate change scenarios.

Cytostatic hypothermia and its impact on glioblastoma and survival.

Patients with glioblastoma (GBM) have limited options and require novel approaches to treatment. Here, we studied and deployed nonfreezing "cytostatic" hypothermia to stunt GBM growth. This growth-halting method contrasts with ablative, cryogenic hypothermia that kills both neoplastic and infiltrated healthy tissue. We investigated degrees of hypothermia in vitro and identified a cytostatic window of 20° to 25°C. For some lines, 18 hours/day of cytostatic hypothermia was sufficient to halt division in vitro. Next, we fabricated an experimental tool to test local cytostatic hypothermia in two rodent GBM models. Hypothermia more than doubled median survival, and all rats that successfully received cytostatic hypothermia survived their study period. Unlike targeted therapeutics that are successful in preclinical models but fail in clinical trials, cytostatic hypothermia leverages fundamental physics that influences biology broadly. It is a previously unexplored approach that could provide an additional option to patients with GBM by halting tumor growth.