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Global Navigation Satellite System Reflectometry (GNSS-R) is a remote sensing technique that can be regarded as a bistatic radar system. GNSS-R uses GNSS signals as signal sources and obtains the Earth's surface environmental parameters, such as soil moisture (SM), by receiving the L-band microwave signal reflected from the Earth's surface. However, surface vegetation could be one of the main factors influencing the accuracy of GNSS-R land applications since plants, including branches and leaves, attenuate GNSS signals. Additionally, the evaluation of signal attenuation caused by the plant canopy is quite difficult. In this paper, we study the attenuations of received L1- and L2-band GPS signals to the vegetation leaf area index (LAI) for different types of plants. The relationship between the attenuation of the GPS signal-to-noise ratio (SNR) (both above and below the canopy) and the LAI is established through field experiments. The results show that the mean SNR received in the L2 band is lower than that in the L1 band for each satellite but with a larger standard deviation (SD). The sensitivities of L1- and L2-band signals to the LAI are revealed, revealing greater sensitivity and a relatively good Pearson correlation coefficient (R) for lower elevation angles and vegetation biomass. In addition, the sensitivity and R of L2-band signals to the LAI are lower than those of L1-band signals. This study is significantly valuable for improving the quantitative representation of error estimates for GNSS-R SM retrieval. The established model can be employed in GNSS land applications and aid in solving signal surface-scattering problems in which accurate signal estimates are important. [ABSTRACT FROM AUTHOR]

The recently introduced complex-valued convolutional neural network (CV-CNN) has shown considerable advancements for polarimetric synthetic aperture radar (PolSAR) image classification by effectively incorporating both magnitude and phase information. However, a solitary 2D or 3D CNN encounters challenges such as insufficiently extracting scattering channel dimension features or excessive computational parameters. Moreover, these networks' default is that all information is equally important, consuming vast resources for processing useless information. To address these issues, this study presents a new hybrid CV-CNN with the attention mechanism (CV-2D/3D-CNN-AM) to classify PolSAR ground objects, possessing both excellent computational efficiency and feature extraction capability. In the proposed framework, multi-level discriminative features are extracted from preprocessed data through hybrid networks in the complex domain, along with a special attention block to filter the feature importance from both spatial and channel dimensions. Experimental results performed on three PolSAR datasets demonstrate our present approach's superiority over other existing ones. Furthermore, ablation experiments confirm the validity of each module, highlighting our model's robustness and effectiveness. [ABSTRACT FROM AUTHOR]

Mice expressing a germline mutation in the phospholipase C-γ1-binding site of linker for activation of T cells (LAT) show progressive lymphoproliferation and ultimately die at 4-6 mo age. The hyperactivated T cells in these mice show defective TCR-induced calcium flux but enhanced Ras/ERK activation, which is critical for disease progression. Despite the loss of LAT-dependent phospholipase C-γ1 binding and activation, genetic analysis revealed RasGRP1, and not Sos1 or Sos2, to be the major Ras guanine exchange factor responsible for ERK activation and the lymphoproliferative phenotype in these mice. Analysis of isolated CD4(+) T cells from LAT-Y136F mice showed altered proximal TCR-dependent kinase signaling, which activated a Zap70- and LAT-independent pathway. Moreover, LAT-Y136F T cells showed ERK activation that was dependent on Lck and/or Fyn, protein kinase C-θ, and RasGRP1. These data demonstrate a novel route to Ras activation in vivo in a pathological setting.

Objective(s): To investigate the effects of Nonylphenol (NP) in pups from dams exposed during gestational and lactational periods on immediate early genes (c-jun, c-fos) in hippocampus and the learning and memory of F1 rats. Materials and Methods: Twenty eight pregnant dams, stratified by pregnancy date, were randomly assigned into 4 groups, which were gavaged with NP at the doses of 50 mg/kg/day, 100 mg/kg/day, 200 mg/kg/day and groundnut oil, respectively. Step-down avoidance test, and learning and memory effects of NP were evaluated on 8-weeks-old pups. The expressions of c-jun and c-fos and the activities of choline acetyltransferase (ChAT) and acetylcholinesterase (AchE) were evaluated in hippocampus of pups. Results: Compared to the control, reaction time (RT) that pups spent to jump to the platform was longer (P=0.02), the number of errors were higher (P=0.01), and the step-down latency was shorter in the 200 mg/kg/day NP-treated group (P=0.04). Exposure to NP induced a significant reduction in ChAT activity in hippocampus in the 100 (P=0.005) and 200 mg/kg/day NP-treated groups (P=0.002), whereas exposure to 200 mg/kg/day caused a significant increase in AchE activity (P=0.004); a dose–response relationship was revealed between ChAT and AchE activities and NP exposure in the hippocampus of pups (r=-0.821, P=0.01; r=0.757, P=0.04). Exposure to NP in the 100 and 200 mg/kg/day NP-treated groups exhibited an increase in number of c-fos and c-jun positive cells. Conclusion: Exposure to NP might negatively affect learning and memory ability in F1 rats, possibly due to the alterations in the expression of c-jun and c-fos, and ChAT, AchE activities in hippocampus of pups.

Motivated by comparing the distribution of longitudinal quality of life (QoL) data among different treatment groups from a cancer clinical trial, we propose a semiparametric test statistic for the homogeneity of the distributions of multigroup longitudinal measurements, which are bounded in a closed interval with excess observations taking the boundary values. Our procedure is based on a three-component mixed density ratio model and a composite empirical likelihood for the longitudinal data taking values inside the interval. A nonparametric bootstrap method is applied to calculate the p-value of the proposed test. Simulation studies are conducted to evaluate the proposed procedure, which show that the proposed test is effective in controlling type I errors and more powerful than the procedure which ignores the values on the boundaries. It is also robust to the model mispecification than the parametric test. The proposed procedure is also applied to compare the distributions of the scores of Physical Function subscale and Global Heath Status between the patients randomized to two treatment groups in a cancer clinical trial. [ABSTRACT FROM AUTHOR]