Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images.

Accurate estimation of fractional vegetation cover (FVC) is essential for crop growth monitoring. Currently, satellite remote sensing monitoring remains one of the most effective methods for the estimation of crop FVC. However, due to the significant difference in scale between the coarse resolution of satellite images and the scale of measurable data on the ground, there are significant uncertainties and errors in estimating crop FVC. Here, we adopt a Strategy of Upscaling-Downscaling operations for unmanned aerial systems (UAS) and satellite data collected during 2 growing seasons of winter wheat, respectively, using backpropagation neural networks (BPNN) as support to fully bridge this scale gap using highly accurate the UAS-derived FVC (FVCUAS) to obtain wheat accurate FVC. Through validation with an independent dataset, the BPNN model predicted FVC with an RMSE of 0.059, which is 11.9% to 25.3% lower than commonly used Long Short-Term Memory (LSTM), Random Forest Regression (RFR), and traditional Normalized Difference Vegetation Index-based method (NDVI-based) models. Moreover, all those models achieved improved estimation accuracy with the Strategy of Upscaling-Downscaling, as compared to only upscaling UAS data. Our results demonstrate that: (1) establishing a nonlinear relationship between FVCUAS and satellite data enables accurate estimation of FVC over larger regions, with the strong support of machine learning capabilities. (2) Employing the Strategy of Upscaling-Downscaling is an effective strategy that can improve the accuracy of FVC estimation, in the collaborative use of UAS and satellite data, especially in the boundary area of the wheat field. This has significant implications for accurate FVC estimation for winter wheat, providing a reference for the estimation of other surface parameters and the collaborative application of multisource data.

Bidirectional association between infectious gastroenteritis and inflammatory bowel disease: a population-based study.

BACKGROUND: Intertwined association between infectious gastroenteritis (IGE) and inflammatory bowel disease (IBD) has not been investigated clearly. We aimed to examine the bidirectional association between IGE and IBD. METHODS: A bidirectional study using the Taiwan National Health Insurance Research Database was designed. Through a case-control design, we identified 2899 new IBD cases during 2006-2017 and matched to 28,990 non-IBD controls. We used conditional logistic regression model to estimate odds ratios (OR) of IBD for previous IGE in different exposure time-windows within 5-years before IBD diagnosis and Poisson regression model to estimate incidence rate ratio (IRR) of subsequent IGE for IBD group to non-IBD group. RESULTS: The mean age at the initial IBD diagnosis was 41 years. More IBD patients (21.49%) than controls (12.60%) had been exposed to IGE during > 6 months to 5 years before IBD diagnosis, the OR of IBD for IGE was 1.89 [95% confidence interval: 1.69-2.11]. Excess OR decreased as IGE exposure time before the index date increased. More IGE episodes were associated with additional increase in IBD risk (OR: 1.64, 2.19, 2.57, 3.50, and 4.57 in patients with 1, 2, 3, 4, and ≥ 5 IGE episodes, respectively). The IRR of having IGE for IBD group to non-IBD group was 2.42 before IBD diagnosis and increased to 5.74 after IBD diagnosis. CONCLUSIONS: These findings suggested an IGE-IBD bidirectional association. More attention is needed for physicians to develop preventive strategies and be aware of the higher risk of subsequent IGE in IBD patients.

Cross-validation of the peppermint benchmarking experiment across three analytical platforms.

The Peppermint Experiment is a breath analysis benchmarking initiative that seeks to address the lack of inter-comparability of outcomes across independent breath biomarker studies. In this experiment, the washout profiles of volatile terpene constituents of encapsulated peppermint oil (mainlyα-pinene,ß-pinene, limonene and 1,8-cineole) in exhaled breath are characterized through a series of measurements at defined sampling intervals up to 6 h after ingestion of the capsule. In the present work, the Peppermint Experiment was carried out on a cohort of volunteers (n= 11) that provided breath samples in three sittings on different days (i.e. triplicates per volunteer) for concurrent analysis by three different analytical platforms. These platforms were proton transfer reaction-time-of-flight-mass spectrometry (PTR-TOFMS) interfaced with a buffered end-tidal (BET) breath sampler, gas chromatography-ion mobility spectrometry (GC-IMS) in conjunction with a compatible handheld direct breath sampler, and thermal desorption comprehensive two-dimensional gas chromatography-time-of-flight-mass spectrometry (TD-GC×GC-TOFMS) with a Respiration Collection forin-vitroAnalysis (ReCIVA) system for trapping breath volatiles onto adsorbent tubes. Regression analysis yielded mean washout times across the cohort of 448 min (PTR-TOFMS and GC-IMS) and 372 min (TD-GC×GC-TOFMS), which are in good alignment with published benchmark values. Large variations in washout profiles were observed at the individuals level, both between (inter-individual) and within (intra-individual) participants, indicating high variability in the degree of absorption, distribution, metabolism and excretion of volatile terpenes in the body within individuals and across the cohort. The comparably low inter-instrument variability indicates that differences in benchmark values from independent studies reported in the literature are driven by biological variability rather than different performances between sampling methods or analytical platforms.

Silencing miRNA-324-3p protects against cerebral ischemic injury via regulation of the GATA2/A1R axis.

Previous studies have suggested that miR-324-3p is related to the pathophysiology of cerebral ischemia, but the mechanism underlying this relationship is unclear. In this study, we found that miR-324-3p expression was decreased in patients with acute ischemic stroke and in in vitro and in vivo models of ischemic stroke. miR-324-3p agomir potentiated ischemic brain damage in rats subjected to middle cerebral artery occlusion, as indicated by increased infarct volumes and cell apoptosis rates and greater neurological deficits. In a PC12 cell oxygen-glucose deprivation/reoxygenation model, a miR-324-3p mimic decreased cell viability and expression of the anti-apoptotic protein BCL2 and increased expression of the pro-apoptotic protein BAX and rates of cell apoptosis, whereas treatment with a miR-324-3p inhibitor had the opposite effects. Silencing miR-324-3p increased adenosine A1 receptor (A1R) expression through regulation of GATA binding protein 2 (GATA2). These findings suggest that silencing miR-324-3p reduces ischemic brain damage via the GATA2/A1R axis.

Wheat yield estimation using remote sensing data based on machine learning approaches.

Accurate predictions of wheat yields are essential to farmers'production plans and to the international trade in wheat. However, only poor approximations of the productivity of wheat crops in China can be obtained using traditional linear regression models based on vegetation indices and observations of the yield. In this study, Sentinel-2 (multispectral data) and ZY-1 02D (hyperspectral data) were used together with 15709 gridded yield data (with a resolution of 5 m × 5 m) to predict the winter wheat yield. These estimates were based on four mainstream data-driven approaches: Long Short-Term Memory (LSTM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Regression (SVR). The method that gave the best estimate of the winter wheat yield was determined, and the accuracy of the estimates based on multispectral and hyperspectral data were compared. The results showed that the LSTM model, for which the RMSE of the estimates was 0.201 t/ha, performed better than the RF (RMSE = 0.260 t/ha), GBDT (RMSE = 0.306 t/ha), and SVR (RMSE = 0.489 t/ha) methods. The estimates based on the ZY-1 02D hyperspectral data were more accurate than those based on the 30-m Sentinel-2 data: RMSE = 0.237 t/ha for the ZY-1 02D data, which is about a 5% improvement on the RSME of 0.307 t/ha for the 30-m Sentinel-2 data. However, the 10-m Sentinel-2 data performed even better, giving an RMSE of 0.219 t/ha. In addition, it was found that the greenness vegetation index SR (simple ratio index) outperformed the traditional vegetation indices. The results highlight the potential of the shortwave infrared bands to replace the visible and near-infrared bands for predicting crop yields Our study demonstrates the advantages of the deep learning method LSTM over machine learning methods in terms of its ability to make accurate estimates of the winter wheat yield.

Estimation of wheat tiller density using remote sensing data and machine learning methods.

The tiller density is a key agronomic trait of winter wheat that is essential to field management and yield estimation. The traditional method of obtaining the wheat tiller density is based on manual counting, which is inefficient and error prone. In this study, we established machine learning models to estimate the wheat tiller density in the field using hyperspectral and multispectral remote sensing data. The results showed that the vegetation indices related to vegetation cover and leaf area index are more suitable for tiller density estimation. The optimal mean relative error for hyperspectral data was 5.46%, indicating that the results were more accurate than those for multispectral data, which had a mean relative error of 7.71%. The gradient boosted regression tree (GBRT) and random forest (RF) methods gave the best estimation accuracy when the number of samples was less than around 140 and greater than around 140, respectively. The results of this study support the extension of the tested methods to the large-scale monitoring of tiller density based on remote sensing data.

Dex-Aco coating simultaneously increase the biocompatibility and transfection efficiency of cationic polymeric gene vectors.

Cationic polymeric vectors attracted plenty of attentions in gene therapy due to nonimmunogenicity, easy to synthesis and flexible properties. However, biocompatibility challenge such as nonspecific interactions with blood cells and serum proteins, may affect the delivery efficiency of cationic vectors; besides, inefficient endosomal escape causes low transfection efficiency. Herein, we synthesized an anionic coating polymer dextran-g-aconic anhydride (Dex-Aco, DA) through a simple esterification reaction, which can protect cationic polymer poly(cystamine-bis-acrylamide)-agmatine-histamine (PCAH, PC) constructed nanomedicine against interactions with blood cells and serum proteins, improving biocompatibility. Interestingly, DA coating significantly increased the transfection efficiency of cationic PC，not due to the increase of cellular uptake, nor functioning as a receptor ligand, but was associated to the change of endocytosis pathway. Finally, using programmed cell death protein 4 (PDCD4) as a functional gene, DA coating PC NPs showed improved therapeutic effect and biocompatibility on tumor bearing mice. We believe that this DA coating PC NPs provides a facile method to improve the performance of cationic polymer vectors in gene therapy and has great potential for clinical applications.

A new strategy for hydrophobic drug delivery using a hydrophilic polymer equipped with stacking units.

A highly hydrophilic polymer equipped with guanidinium groups was used to load aromatic ring-containing hydrophobic agent doxorubicin (DOX) via π-π interaction. The results have shown that the delivery system exhibited enhanced cellular uptake and antitumor efficiency compared with free drugs. This study opens new avenues for the application of hydrophilic polymers in drug delivery.

Regulating the Golgi apparatus by co-delivery of a COX-2 inhibitor and Brefeldin A for suppression of tumor metastasis.

Finding a cure for breast cancer currently remains a medical challenge in due to the failure of common treatment methods to inhibit invasion and metastasis of cancer cells, which eventually leads to recurrence of breast cancer. Many secreted proteins are overexpressed and play crucial roles in tumorigenesis and development. The Golgi apparatus is a key protein processing and secretion factory in which metastasis-associated proteins are modified, transported and secreted; thus, regulating the Golgi apparatus of tumor cells is a viable strategy to inhibit tumor metastasis. Herein, celecoxib (CLX) and Brefeldin A (BFA) were encapsulated into the biocompatible polymer PLGA-PEG to form nanoparticles that act on the Golgi apparatus to treat metastatic breast cancer; CLX is a specific COX-2 inhibitor which accumulates in the Golgi apparatus, and BFA is a protein transport inhibitor fusing the Golgi apparatus into endoplasmic reticulum. The optimized CLX and BFA co-loaded nanoparticles (CBNPs) possessed good physicochemical properties. CBNPs efficiently damaged the Golgi apparatus within 30 min and showed enhanced cytotoxicity of CLX and BFA toward murine metastatic breast cancer 4T1 cells. The migration and invasion abilities of the cells were dramatically suppressed by the CBNPs. Further, the expression and secretion of metastasis-associated proteins such as matrix metalloproteinase-9 (MMP-9) and vascular endothelial growth factor (VEGF) were remarkably decreased. Our findings showed that co-delivering CLX and BFA to regulate the Golgi apparatus may be an efficient strategy to inhibit breast cancer growth and suppress tumor cell metastasis.