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Miniaturized and lightweight Unmanned Aerial Vehicles (UAV) provide a flexible platform for Synthetic Aperture Radar (SAR). The application of UAV Interferometric SAR (InSAR) is gradually increasing in interferometric measurement fields. UAVs are small and light, which are easily affected by airflow disturbances. Their trajectories are nonlinear and unparallel when adopting the multipass mode for interferometry. The nonlinear and unparallel trajectories result in geometric distortion between the interferometric image pairs. Under complex topography conditions, the interferometric image pairs of UAVs have large offsets that are obviously space-dependent, thereby resulting in substantial technical challenges during image registration. Conventional image registration methods based on polynomial fitting are no longer applicable. In this study, we proposed an image registration method based on image partition with topography assistance. First, an elevation threshold is generated based on the UAV trajectories, and the measurement area is partitioned using the assisted topography. Then, a polynomial fitting model is constructed for offsets within each partition with constraints applied at the partition boundaries for joint optimization. Finally, continuous global offset fitting surfaces are obtained, and precise image registration is achieved by resampling the slave image. The effectiveness of the proposed method is preliminarily validated using real measurement data obtained from UAV InSAR in the P-band.

Myocardial substrate metabolism in cardiac hypertrophy or heart failure shifts from fatty acid oxidation to a greater reliance on glycolysis. Here, the authors show that KLF7 can simultaneously regulate key enzymes in glycolysis and fatty acid oxidation to mitigate metabolic imbalance during cardiac hypertrophy.

Abstract Background Neural stem cells (NSCs) are considered as candidates for cell replacement therapy in many neurological disorders. However, the propensity for their differentiation to proceed more glial rather than neuronal phenotypes in pathological conditions limits positive outcomes of reparative transplantation. Exogenous physical stimulation to favor the neuronal differentiation of NSCs without extra chemical side effect could alleviate the problem, providing a safe and highly efficient cell therapy to accelerate neurological recovery following neuronal injuries. Results With 7-day physiological electric field (EF) stimulation at 100 mV/mm, we recorded the boosted neuronal differentiation of NSCs, comparing to the non-EF treated cells with 2.3-fold higher MAP2 positive cell ratio, 1.6-fold longer neuronal process and 2.4-fold higher cells ratio with neuronal spontaneous action potential. While with the classical medium induction, the neuronal spontaneous potential may only achieve after 21-day induction. Deficiency of either PI3Kγ or β-catenin abolished the above improvement, demonstrating the requirement of the PI3K/Akt/GSK-3β/β-catenin cascade activation in the physiological EF stimulation boosted neuronal differentiation of NSCs. When transplanted into the spinal cord injury (SCI) modelled mice, these EF pre-stimulated NSCs were recorded to develop twofold higher proportion of neurons, comparing to the non-EF treated NSCs. Along with the boosted neuronal differentiation following transplantation, we also recorded the improved neurogenesis in the impacted spinal cord and the significantly benefitted hind limp motor function repair of the SCI mice. Conclusions In conclusion, we demonstrated physiological EF stimulation as an efficient method to boost the neuronal differentiation of NSCs via the PI3K/Akt/GSK-3β/β-catenin activation. Pre-treatment with the EF stimulation induction before NSCs transplantation would notably improve the therapeutic outcome for neurogenesis and neurofunction recovery of SCI.

Abstract Background Krüppel-like factor 7 (klf7), a transcription factor in the nervous system to regulate cell proliferation and differentiation, has been recently identified as a causal gene for autism spectrum disorder (ASD), but the mechanism behind remains unknown. Result To uncover this mechanism, in this study we characterized the involvement of klf7 in circadian rhythm by knocking down klf7 in N2A cells and examining the rhythmic expression of circadian genes, especially Clock gene. We constructed klf7−/− mice and then investigated into klf7 regulation on the expression of rhythm genes in vivo as well as the use of melatonin to rescue the autism behavior. Our results illustrated that circadian rhythm was disrupted in klf7 knockdown cells and that klf7−/− mice showed autism-like behavior. Also, we found that Clock gene was downregulated in the brain of these klf7−/− mice and that the downstream rhythm genes of Clock were disturbed. Melatonin, as a circadian regulation drug, could regulate the expression level and amplitude of rhythm genes in klf7 knockout cells and further rescue the autistic behavior of klf7−/− mice. Conclusion Klf7 deficiency causes ASD by disrupting circadian rhythm related genes to trigger rhythm oscillations. To treat ASD, maintaining circadian homeostasis is promising with the use of melatonin.

Abstract Prevention and early intervention are the current focus of treatment for Alzheimer's disease (AD). An increase in reactive oxygen species (ROS) is a feature of the early stages of AD, thus suggesting that the removal of excess ROS can be a viable method of improving AD. Natural polyphenols are able to scavenge ROS and thus promising for treating AD. However, some issues need to be addressed. Among them, important are that most polyphenols are hydrophobic, have low bioavailability in the body, are easily degraded, and that single polyphenols have insufficient antioxidant capacity. In this study, we employed two polyphenols, resveratrol (RES) and oligomeric proanthocyanidin (OPC), and creatively grafted them with hyaluronic acid (HA) to form nanoparticles to address the aforementioned issues. Meanwhile, we strategically grafted the nanoparticles with the B6 peptide, enabling the nanoparticles to cross the blood–brain barrier (BBB) and enter the brain for AD treatment. Our results illustrate that B6‐RES‐OPC‐HA nanoparticles can significantly scavenge ROS, reduce brain inflammation, and improve learning and memory ability in AD mice. B6‐RES‐OPC‐HA nanoparticles have the potential to prevent and alleviate early AD.

The miniaturized and lightweight unmanned aerial vehicle synthetic aperture radar (UAV SAR) is gradually becoming a research hotspot. The motion errors of UAVs lead to a deviation from a straight flight path couple with an unknown elevation of the target area, which leads to a degradation in SAR image quality. To achieve high-precision SAR imaging results, external terrain elevation information should be utilized. However, such data are challenging to obtain and limited in accuracy. In response to this problem, a modified high-precision imaging algorithm based on imaging plane optimization with minimum entropy is proposed. The proposed algorithm makes good use of the nonlinear trajectory of the UAV, which is unfriendly to imaging. Then, the image entropy is taken into account as the evaluation metric to acquire an approximated optimization imaging plane. Finally, the BP imaging is performed on the optimization imaging plane. The proposed method does not rely on external terrain information. Instead, it makes full use of the non-linear trajectory of the UAV and autonomously generates the optimal imaging plane for different terrain areas. By doing so, it achieves high-precision imaging results. Simulations and actual measurements have validated the effectiveness and enhancement of the proposed method.

Subcortical vascular mild cognitive impairment (svMCI) is associated with structural and functional changes in the cerebral cortex affecting major brain networks. While recent studies have shown that the intrinsic cerebral connectivity networks can be mapped onto the cerebellum, and the cortex and cerebellum are interconnected via the cortico-basal ganglia-cerebellar circuit, structural and functional disruptions in cerebellum in svMCI are rarely studied. In this study, we conducted voxel-based morphometry analysis to investigate gray matter atrophy pattern across cerebellar regions in 40 svMCI patients, and explored alterations in functional connectivity between the basal ganglia and cerebellum. The results showed that the amount of cerebellar atrophy within the default mode, salience, and frontoparietal networks correlated with their counterpart in the cerebral cortex. Moreover, key regions of the cerebellum, including the lobule VI, VIIb, VIII, and Crus I, which are reported to have a role in cognitive function, showed both anatomical atrophy and decreased functional connectivity with the striatum. These atrophy and connectivity patterns in the cerebellum also correlated with memory performances. These findings demonstrate that there are coupled changes in cerebral and cerebellar circuits, reflecting that degeneration patterns in svMCI are not limited to the cerebral cortex but similarly extend to the cerebellum as well, and suggest the cortico-basal ganglia-cerebellar circuit may play an important role in the pathology of svMCI.

The stability of large-scale landslide in the reservoir area of hydropower station has an important impact on the safety of dam structure and surrounding people's life and property. Large-scale real-time accurate observation of the stability and deformation trend of this type of landslide can provide reliable early warning and control information for the landslide, which is of great significance. The traditional monitoring methods of landslide in the reservoir area of hydropower station are mainly GNSS monitoring and total station monitoring. In this study, the advanced GB-InSAR system LKR-05-KU-S100 is applied to the monitoring of Cangjiangqiao-Yingpan landslide and Dahua landslide of Dahuaqiao hydropower station on Lancang River. Field monitoring tests show that the system has high precision, and it can carry out long-distance, all-day, all-weather, and large-scale monitoring. It has unique advantages and broad application prospects for the monitoring of large and super-large landslides.

The solid-state transmitters are widely adopted for weather radars, where pulse compression is operated to provide the required sensitivity and range resolution. Therefore, effective sidelobe suppression strategies must be employed, especially for weather observation. Currently, many methods can suppress the sidelobe to a very low level in the case of point targets or uniformly distributed targets. However, in strong convection weather process, the weather echo amplitude lies in a wide dynamic range and the main lobe of weak target is prone to being contaminated by the sidelobe of strong target, causing the degradation of weather fundamental data estimation, even generating artifacts and affecting the quantitative precipitation evaluation. In this paper, we propose a novel strategy which is the further processing of a general pulse compression radar to mitigate the effects of sidelobes. The proposed method is called the predominant component extraction (PCE), in which the re-weighting processing is operated after pulse compression, and then the echo of each bin is optimized and its energy will approach the real targets in each bin. It can improve the estimation of weak signals or even eliminate the artifact at the edge of the scene. Numerical simulation experiments and real-data verifications are implemented to validate the feasibility and superiority. It is noted that the proposed method has no requirement on the transmitted waveform and can be realized only by adding a step after pulse compression in the actual system.

Scar formation can lead to glaucoma filtration surgery (GFS) failure, wherein transforming growth factor (TGF)-β is the core regulator. To reducing scar formation, this paper presents our study on the design of hydrogels to deactivate TGF-β1. We hypothesized that excess TGF-β1 can be removed from aqueous humor through the addition of oxidized hyaluronic acid (O-HA) hydrogels that are seeded with decorin (O-HA ​+ ​D). Immunohistochemistry and enzyme-linked immunosorbent assay (ELISA) were performed to demonstrate the adsorption properties of O-HA ​+ ​D hydrogel, thus reducing the TGF-β1 concentration in aqueous humor. In the light that collagen contraction in human Tenon's capsule fibroblasts (HTFs) and the angiogenesis of human umbilical vein endothelial cells (HUVECs) can be activated by TGF-β1 and β2, we performed the quantitative analysis of polymerase chain reaction to determine the effect of O-HA ​+ ​D on the type I collagen, fibronectin, and angiogenesis. Our results illustrate that O-HA ​+ ​D can inhibit the increase of α-SMA expression in HTF induced by TGF-β1 and that O-HA ​+ ​D can inhibit the production of collagen I and fibronectin in HTF treated with TGF-β1. Furthermore, we performed in vivo studies by employing a rabbit model, where rabbits were treated with hydrogels post GFS. Our results demonstrate that, as compared with other groups, the rabbits treated with O-HA ​+ ​D had the greatest reduction in inflammatory cells with reduced level of collagen in wounds. Taken together, the present study paves the way toward the treatment of post-glaucoma fibrosis following surgery.

A landslide susceptibility map (LSM) is the basis of hazard and risk assessment, guiding land planning and utilization, early warning of disaster, etc. Researchers are often overly keen on hybridizing state-of-the-art models or exploring new mathematical susceptibility models to improve the accuracy of the susceptibility map in terms of a receiver operator characteristic curve. Correlation analysis of the causal factors is a necessary routine process before susceptibility modeling to ensure that the overall correlation among all factors is low. However, this overall correlation analysis is insufficient to detect a high local correlation among the causal factor classes. The objective of this study is to answer three questions: 1) Is there a high correlation between causal factors in some parts locally? 2) Does it affect the accuracy of landslide susceptibility assessment? and 3) How can this influence be eliminated? To this aim, Wanzhou County was taken as the test site, where landslide susceptibility assessment based on 12 causal factors has been previously performed using the frequency ratio (FR) model and random forest (RF) model. In this work, we conducted a local spatial correlation analysis of the “altitude” and “rivers” factors and found a sizeable spatial overlap between altitude-class-1 and rivers-class-1. The “altitude” and “rivers” factors were reclassified, and then the FR model and RF model were used to reevaluate the susceptibility and analyze the accuracy loss caused by the local spatial correlation of the two factors. The results demonstrated that the accuracy of LSMs was markedly enhanced after reclassification of “altitude” and “rivers,” especially for the RF model–based LSM. This research shed new light on the local correlation of causal factors arising from a particular geomorphology and their impact on susceptibility.

The effective detection of unmanned aerial vehicle (UAV) targets is of great significance to guarantee national military security and social stability. In recent years, with the development of communication and control technology, the movement of UAVs has become increasingly flexible and complex, presenting diverse trajectory forms and different motion models in different phases. The Gaussian mixture probability hypothesis density filter incorporating the linear Gaussian jump Markov system approach (LGJMS-GMPHD) provides an efficient method for tracking multiple maneuvering targets, as applied to the switching of motions between a set of models in a Markovian chain. However, in practice, the motion model parameters of targets are generally unknown and the model switching is uncertain. When the preset filtering model parameters are mismatched, the tracking performance is dramatically degraded. In this paper, within the framework of the LGJMS-GMPHD filter, a deep-learning-based multiple model tracking method is proposed. First, an adaptive turn rate estimation network is designed to solve the filtering model mismatch caused by unknown turn rate parameters in coordinate turn models. Second, a filter state modification network is designed to solve the large tracking errors in the maneuvering phase caused by uncertain motion model switching. Finally, based on simulations of multiple maneuvering targets in cluttered environments and experimental field data verification, it can be concluded that the proposed method has strong adaptability to multiple maneuvering forms and can effectively improve the tracking performance of targets with complex maneuvering motion.

The monitoring and early warning of a landslide in the reservoir area of a hydropower station are of great significance in the dam structure of the hydropower station and in the safety of people’s life and property on the reservoir bank. In this study, a new ground-based interferometric synthetic aperture radar system LKR-05-KU-S100 was used to carry out field monitoring tests on Lagu landslide and Xiaozhaju landslide of Dahuaqiao hydropower station and No. 1 landslide on the left bank of Xiaowan hydropower station on the Lancang river. The results show that, during the monitoring period, Lagu landslide of Dahuaqiao hydropower station and No. 1 landslide on the left bank of Xiaowan hydropower station are basically stable, and the deformation trend of Xiaozhaju landslide is obvious so it should undergo continuous monitoring. At the same time, the field monitoring test also shows that the new ground-based interferometric synthetic aperture radar system LKR-05-KU-S100 has the advantages of high precision and long-distance, all-day, all-weather, and large-scale monitoring and has unique advantages and broad application prospects for the overall deformation monitoring of large landslides in the reservoir area.

Entomological radar is an effective means of monitoring insect migration, and can realize long-distance and large-scale rapid monitoring. The stable tracking of individual insect targets is the basic premise underlying the identification of insect species and the study of insect migration mechanisms. However, the complex motion trajectory and large number of false measurements decrease the performance of insect target tracking. In this paper, an insect target tracking algorithm in clutter was designed based on the multidimensional feature fusion strategy (ITT-MFF). Firstly, multiple feature parameters of measurements were fused to calculate the membership of measurements and target, thereby improving the data association accuracy in the presence of clutter. Secondly, a distance-correction factor was introduced to the probabilistic data association (PDA) algorithm to accomplish multi-target data association with a low computational cost. Finally, simulation scenarios with different target numbers and clutter densities were constructed to verify the effectiveness of the proposed method. The tracking result comparisons of the experimental data acquired from a Ku-band entomological radar also indicate that the proposed method can effectively reduce computational cost while maintaining high tracking precision, and is suitable for engineering implementation.

Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets.

Cerebral small vessel diseases play a crucial role in both vascular and non-vascular dementias. The location of white matter hyperintensities (WMHs), a neuroimaging marker of cerebral small vessel disease, has been found to vary between different types of dementias, and those in the basal ganglia (BG) have been particularly associated with vascular cognitive impairment (VCI). However, anatomical variation of WMHs across BG nuclei and its effect on brain network dysconnectivity has not been clearly elucidated. The study sample consisted of 40 patients with amnestic mild cognitive impairment (aMCI), 40 with subcortical vascular MCI (SVMCI), and 40 healthy control subjects. We examined the volume of WMH using T2-weighted magnetic resonance imaging. We also assessed the disturbances in BG-cortical communication by measuring resting-state functional connectivity (rsFC) from the functional magnetic resonance imaging signal. WMHs were more pronounced in the SVMCI group particularly in the caudate regions. In SVMCI patients, while higher WMHs in the dorsal caudate correlated with weaker FC with executive control regions and worse immediate recall performance, WMHs in the ventral caudate were associated with weaker FC with anterior default mode regions and worse delayed recall performance. In contrast, in aMCI patients, BG WMHs were not correlated with their changes in functional connectivity changes, which showed weaker connectivity with almost all BG structures, rather than restricting to specific BG subdivisions as observed in the SVMCI group. Our findings demonstrate that heterogeneously distributed BG WMHs are associated with changes in functional network interactions and verbal episodic memory performance only in SVMCI patients, which establishes a link between cerebrovascular-related structural abnormality, functional integrity of BG circuits, and episodic memory impairments in SVMCI, and may reflect a differential role of the cerebrovascular pathology in disrupting network-level communications and cognition between Alzheimer’s and subcortical vascular dementia.

Large-scale insect migration may cause severe plant diseases and pests which can exert serious impact on agriculture. Predicting the trajectory and destination of insect migration accurately is an effective way to prevent pests. Here, an insect migration trajectory simulation method was proposed with two stages of insect migration, taking off and cruising, considered. First, four criteria based on actual weather condition were proposed to determine the take-off area of the insect migration. Then, a method based on Lagrange diffusion model was proposed to simulate the cruising trajectory. This step needs to combine with the insect orientation strategy and insect trajectory speed which can be, respectively, provided by entomological radar and weather radar. Finally, by comparing the determined take-off area and the simulated insect cruising trajectory with the monitoring results of the weather radar, the effectiveness of the method was verified.