A Comparison of Sentinel-1 Biased and Unbiased Coherence for Crop Monitoring and Classification

Synthetic Aperture Radar (SAR) holds significant potential for applications in crop monitoring and classification. Interferometric SAR (InSAR) coherence proves effective in monitoring crop growth. Currently, the coherence based on the maximum likelihood estimator is biased towards low coherence values. Therefore, the main aim of this work is to access the performance of Sentinel-1 time-series biased coherence and unbiased coherence in crop monitoring and classification. This study was conducted during the 2018 growing season (April-October) in Komoka, an agricultural region in southwestern Ontario, Canada, primarily cultivating three crops: soybean, corn, and winter wheat. To verify the ability of coherence to monitor crops, a linear correlation coefficient between temporal coherence and dual polarimetric radar vegetation index (DpRVI) was fitted. The results revealed a stable correlation between temporal coherence and DpRVI time-series, with the highest correlation observed for soybean (0.7 < R < 0.8), followed by wheat and corn. Notably, unbiased coherence of the VV channel exhibited the highest correlation (R > 0.75). In addition, we applied unbiased coherence to crop classification. The results show that unbiased coherence exhibits very promising classification performance, with the overall accuracy (84.83%) and kappa coefficient (0.76) of VV improved by 8.35% and 0.12, respectively, over biased coherence, and the overall accuracy (73.25%) and kappa coefficient (0.57) of VH improved by 7.56% and 0.14, respectively, over biased coherence, and all crop classification accuracies were also effectively improved. This study demonstrates the feasibility of coherence monitoring of crops and provides new insights in enhancing the higher separability of crops

A Decision-Making Model Using Machine Learning for Improving Dispatching Efficiency in Chengdu Shuangliu Airport

Due to the increasing number of people traveling by air, the passenger flow at the airport is increasing, and the problem of passenger drop-off and pickup has a huge impact on urban traffic. The difficulty of taking a taxi at the airport is still a hot issue in the society. Aiming at the problem of optimizing the allocation of taxi resource, this paper is based on the cost-benefit analysis method to determine the factors that affect the taxi driver’s decision-making. The mathematical methods such as function equation, BP neural network algorithm, and queuing theory were used to establish a complete decision-making model for taxi drivers and an optimization model of dispatching efficiency at the airport. A conclusion has been drawn that the allocation of airport taxi resource should be arranged closely related to drivers’ revenue and the layout of airport line

A multi-cohort study of the hippocampal radiomics model and its associated biological changes in Alzheimer’s Disease

Abstract There have been no previous reports of hippocampal radiomics features associated with biological functions in Alzheimer’s Disease (AD). This study aims to develop and validate a hippocampal radiomics model from structural magnetic resonance imaging (MRI) data for identifying patients with AD, and to explore the mechanism underlying the developed radiomics model using peripheral blood gene expression. In this retrospective multi-study, a radiomics model was developed based on the radiomics discovery group (n = 420) and validated in other cohorts. The biological functions underlying the model were identified in the radiogenomic analysis group using paired MRI and peripheral blood transcriptome analyses (n = 266). Mediation analysis and external validation were applied to further validate the key module and hub genes. A 12 radiomics features-based prediction model was constructed and this model showed highly robust predictive power for identifying AD patients in the validation and other three cohorts. Using radiogenomics mapping, myeloid leukocyte and neutrophil activation were enriched, and six hub genes were identified from the key module, which showed the highest correlation with the radiomics model. The correlation between hub genes and cognitive ability was confirmed using the external validation set of the AddneuroMed dataset. Mediation analysis revealed that the hippocampal radiomics model mediated the association between blood gene expression and cognitive ability. The hippocampal radiomics model can accurately identify patients with AD, while the predictive radiomics model may be driven by neutrophil-related biological pathways

Effects of Exogenous 5-Aminolevulinic Acid and 24-Epibrassinolide on Cd Accumulation in Rice from Cd-Contaminated Soil

High grain-Cd-accumulating rice variety Yongyou 9 was planted in Cd-contaminated farmland in Taizhou City, Zhejiang Province, China to study the effects of 5-aminolevulinic acid (ALA) and 24-epibrassinolide (EBR) on Cd accumulation in brown rice. Results showed that the exogenous ALA and EBR had no significant effects on agronomic traits, soil pH and total Cd content in soil, but had some effects on the available Cd content in soil, and significantly influenced the Cd accumulation in the different parts of rice. Results also showed that 100 mg/L exogenous ALA significantly reduced the Cd accumulation in brown rice to blow the food safety standard (0.2 mg/kg), and also significantly reduced the Cd contents in the roots and culm of rice. However, 200 mg/L exogenous ALA treatment increased the Cd content in brown rice remarkably. In addition, 0.15 mg/L EBR treatment increased Cd accumulation in roots, culm, leaves and brown rice notably, whereas 0.30 mg/L exogenous EBR treatment reduced the Cd accumulation in brown rice properly, but it was not significant. Therefore, proper concentration of ALA can effectively reduce the Cd accumulation in brown rice, which can be used as an effective technical method for the safe production of rice in Cd polluted farmland. Keywords: rice, Cd-contaminated soil, 5-aminolevulinic acid, 24-epibrassinolide, Cd accumulatio

The moderated-mediation role of risk perception and intolerance of uncertainty in the association between residual symptoms and psychological distress: a cross-sectional study after COVID-19 policy lifted in China

Abstract Background A considerable number of individuals infected with COVID-19 experience residual symptoms after the acute phase. However, the correlation between residual symptoms and psychological distress and underlying mechanisms are scarcely studied. We aim to explore the association between residual symptoms of COVID-19 and psychological distress, specifically depression, anxiety, and fear of COVID-19, and examine the role of risk perception and intolerance of uncertainty in the association. Methods A cross-sectional survey was conducted by online questionnaire-based approach in mid-January 2023. Self-reported demographic characteristics, COVID-19-related information, and residual symptoms were collected. Depression, anxiety, fear, risk perception and intolerance of uncertainty were evaluated using the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Fear of COVID-19 Scale (FCV-19S), COVID-19 Risk Perception Scale and Intolerance of Uncertainty Scale-12 (IUS-12), respectively. Linear regression analyses were conducted to explore the associations. A moderated mediation model was then constructed to examine the role of risk perception of COVID-19 and intolerance of uncertainty in the association between residual symptoms and psychological distress. Results 1735 participants effectively completed the survey. 34.9% of the patients experienced residual symptoms after acute phase of COVID-19. Psychological distress was markedly increased by COVID-19 infection, while residual symptoms had a significant impact on psychological distress (Ps < 0.001), including depression (β = 0.23), anxiety (β = 0.21), and fear of COVID-19 (β = 0.14). Risk perception served as a mediator between residual symptoms and all forms of psychological distress, while intolerance of uncertainty moderated the effect of risk perception on depression and anxiety. Conclusion A considerable proportion of patients experience residual symptoms after acute phase of COVID-19, which have a significant impact on psychological distress. Risk perception and intolerance of uncertainty play a moderated-mediation role in the association between residual symptoms and depression/anxiety. It highly suggests that effective treatment for residual symptoms, maintaining appropriate risk perception and improving intolerance of uncertainty are critical strategies to alleviate COVID-19 infection-associated psychological distress

Insm1a Regulates Motor Neuron Development in Zebrafish

Insulinoma-associated1a (insm1a) is a zinc-finger transcription factor playing a series of functions in cell formation and differentiation of vertebrate central and peripheral nervous systems and neuroendocrine system. However, its roles on the development of motor neuron have still remained uncovered. Here, we provided evidences that insm1a was a vital regulator of motor neuron development, and provided a mechanistic understanding of how it contributes to this process. Firstly, we showed the localization of insm1a in spinal cord, and primary motor neurons (PMNs) of zebrafish embryos by in situ hybridization, and imaging analysis of transgenic reporter line Tg(insm1a: mCherry)ntu805. Then we demonstrated that the deficiency of insm1a in zebrafish larvae lead to the defects of PMNs development, including the reduction of caudal primary motor neurons (CaP), and middle primary motor neurons (MiP), the excessive branching of motor axons, and the disorganized distance between adjacent CaPs. Additionally, knockout of insm1 impaired motor neuron differentiation in the spinal cord. Locomotion analysis showed that swimming activity was significantly reduced in the insm1a-null zebrafish. Furthermore, we showed that the insm1a loss of function significantly decreased the transcript levels of both olig2 and nkx6.1. Microinjection of olig2 and nkx6.1 mRNA rescued the motor neuron defects in insm1a deficient embryos. Taken together, these data indicated that insm1a regulated the motor neuron development, at least in part, through modulation of the expressions of olig2 and nkx6.1

Intrinsic surface p-wave superconductivity in layered AuSn4

Abstract The search for topological superconductivity (TSC) is currently an exciting pursuit, since non-trivial topological superconducting phases could host exotic Majorana modes. However, the difficulty in fabricating proximity-induced TSC heterostructures, the sensitivity to disorder and stringent topological restrictions of intrinsic TSC place serious limitations and formidable challenges on the materials and related applications. Here, we report a new type of intrinsic TSC, namely intrinsic surface topological superconductivity (IS-TSC) and demonstrate it in layered AuSn4 with T c of 2.4 K. Different in-plane and out-of-plane upper critical fields reflect a two-dimensional (2D) character of superconductivity. The two-fold symmetric angular dependences of both magneto-transport and the zero-bias conductance peak (ZBCP) in point-contact spectroscopy (PCS) in the superconducting regime indicate an unconventional pairing symmetry of AuSn4. The superconducting gap and surface multi-bands with Rashba splitting at the Fermi level (E F ), in conjunction with first-principle calculations, strongly suggest that 2D unconventional SC in AuSn4 originates from the mixture of p-wave surface and s-wave bulk contributions, which leads to a two-fold symmetric superconductivity. Our results provide an exciting paradigm to realize TSC via Rashba effect on surface superconducting bands in layered materials