A novel image fusion algorithm based on bandelet transform

A novel image fusion algorithm based on bandelet transform is proposed. Bandelet transform can take advantage of the geometrical regularity of image structure and represent sharp image transitions such as edges efficiently in image fusion. For reconstructing the fused image, the maximum rule is used to select source images’ geometric flow and bandelet coefficients. Experimental results indicate that the bandelet-based fusion algorithm represents the edge and detailed information well and outperforms the wavelet-based and Laplacian pyramid-based fusion algorithms, especially when the abundant texture and edges are contained in the source images.Navigation Science Foundation (No. 05F07001) and the National Natural Science Foundation of China (No. 60472081)

Enriched oxygen improves age-related cognitive impairment through enhancing autophagy

Age-related cognitive impairment represents a significant health concern, with the understanding of its underlying mechanisms and potential interventions being of paramount importance. This study aimed to investigate the effects of hyperbaric oxygen therapy (HBOT) on cognitive function and neuronal integrity in aged (22-month-old) C57BL/6 mice. Male mice were exposed to HBOT for 2 weeks, and spatial learning and memory abilities were assessed using the Morris water maze. We employed transcriptome sequencing and Gene Ontology (GO) term enrichment analysis to examine the effects of HBOT on gene expression profiles, with particular attention given to synapse-related genes. Our data indicated a significant upregulation of postsynapse organization, synapse organization, and axonogenesis GO terms, likely contributing to improved cognitive performance. Moreover, the hyperphosphorylation of tau, a hallmark of many neurodegenerative diseases, was significantly reduced in the HBO-treated group, both in vivo and in vitro. Transmission electron microscopy revealed significant ultrastructural alterations in the hippocampus of the HBOT group, including an increase in the number of synapses and the size of the active zone, a reduction in demyelinated lesions, and a decreased number of “PANTHOS.” Furthermore, Western blot analyses confirmed the upregulation of PSD95, BDNF, and Syn proteins, suggesting enhanced synaptic plasticity and neurotrophic support. Moreover, HBOT increased autophagy, as evidenced by the elevated levels of Beclin-1 and LC3 proteins and the reduced level of p62 protein. Finally, we demonstrated that HBOT activated the AMPK-mTOR signaling pathway, a critical regulator of autophagy. Notably, our findings provide novel insights into the mechanisms by which HBOT ameliorates age-related cognitive impairment, suggesting the potential therapeutic value of this approach

Altered effective connectivity between lateral occipital cortex and superior parietal lobule contributes to manipulability-related modulation of the Ebbinghaus illusion

Action and perception interact reciprocally in our daily life. Previous studies have found that object manipulability can affect visual perceptual processing. Here we probed the neural mechanisms underlying the manipulability-related modulation effect using the well-known Ebbinghaus illusion with the central circle replaced by a high (i.e., a basketball) or a low (i.e., a watermelon) manipulable object. Participants (N 1/4 30) were required to adjust the size of a comparison circle to match that of the central object in the Ebbinghaus configuration. The results showed that the perceived illusion magnitude for the basketball target was significantly reduced than that for the watermelon target, and the manipulability-related modulation effect was manifested in self-connections in the left primary visual cortex and the left superior parietal lobule (SPL), as well as reciprocal connections between the left lateral occipital cortex (LOC) and SPL. Notably, the disparity of the illusion magnitude between the watermelon and the basketball target was positively correlated with the extrinsic connectivity from the left LOC to SPL. The findings suggest that object manipulability can modulate the Ebbinghaus illusion, likely through accentuating the high-manipulability object along the visual processing streams. Moreover, they provide clear evidence that manipulability-related modulation of visual perception relies on the functional interactions between the ventral and dorsal visual pathways. (C)& nbsp;2021 Elsevier Ltd. All rights reserved

Large Area One-Step Facile Processing of Microstructured Elastomeric Dielectric Film for High Sensitivity and Durable Sensing over Wide Pressure Range

Once the requirement of sensitivity has been met, to enable a flexible pressure sensor technology to be widely adopted as an economic and convenient way for sensing diverse human body motions, critical factors need to be considered including low manufacturing cost, a large pressure detection range, and low power consumption. In this work, a facile approach is developed for one-step processing of a large area microstructured elastomer film with high density microfeatures of air voids, which can be seamlessly integrated into the process flow for fabricating flexible capacitive sensors. The fabricated sensors exhibit fast response and high sensitivity in the low pressure range to be able to detect very weak pressure down to 1 Pa and perform reliable wrist pulse monitoring. Compared to previous work, more advantageous features of this sensor are relatively high sensitivity being maintained in a wide pressure range up to 250 kPa and excellent durability under heavy load larger than 1 MPa, attributed to the formed dense air voids inside the film. A smart insole made with the sensor can accurately monitor the real-time walking or running behaviors and even a small weight change less than 1 kg under a heavy load of a 70 kg adult. For both application examples of wrist pulse monitoring and smart insole, the sensors are operated in a 3.3 V electronic system powered by a Li-ion battery, showing the potential for power-constrained wearable applications

Fault Diagnosis for Power Transformers through Semi-Supervised Transfer Learning

The fault diagnosis of power transformers is a challenging problem. The massive multisource fault is heterogeneous, the type of fault is undetermined sometimes, and one device has only met a few kinds of faults in the past. We propose a fault diagnosis method based on deep neural networks and a semi-supervised transfer learning framework called adaptive reinforcement (AR) to solve the above limitations. The innovation of this framework consists of its enhancement of the consistency regularization algorithm. The experiments were conducted on real-world 110 kV power transformers’ three-phase fault grounding currents of the iron cores from various devices with four types of faults: Phases A, B, C and ABC to ground. We trained the model on the source domain and then transferred the model to the target domain, which included the unbalanced and undefined fault datasets. The results show that our proposed model reaches over 95% accuracy in classifying the type of fault and outperforms other popular networks. Our AR framework fits target devices’ fault data with fewer dozen epochs than other novel semi-supervised techniques. Combining the deep neural network and the AR framework helps diagnose the power transformers, which lack diagnosis knowledge, with much less training time and reliable accuracy

Unraveling the dynamics of atmospheric methane: the impact of anthropogenic and natural emissions

The reduction in methane concentration is crucial for achieving the goals of the Paris agreement. However, its annual growth rate is unstable, and understanding the reasons for changes in methane growth is essential for climate policy-making. Currently, there is considerable uncertainty regarding its attribution. Here, we utilize multi-source data and optimal fingerprinting methods to detect the contributions of several key drivers to the methane trend and interannual variability. We find that the methane growth trend is primarily influenced by anthropogenic emissions, while interannual variability is predominantly determined by wetland and biomass burning emissions. This result underscores the central role of anthropogenic emissions in methane dynamics, providing confidence in the effectiveness of human efforts to control methane atmospheric concentrations through emission reductions. It also helps alleviate concerns about the recent surge in atmospheric methane concentration, as it may be a short-term peak caused by increased wetland emissions rather than a long-term change

A New Method for Simultaneous Determination of Phenolic Acids, Alkaloids and Limonoids in Phellodendri Amurensis Cortex

Phellodendri Amurensis Cortex (PAC) is a well-known herbal medicine in China with complex components, but the previous research has mostly focused on its alkaloids analysis. For the first time, a simpler and more efficient method was proposed in this paper to simultaneously determine the content of three different kinds of compounds—phenolic acids, alkaloids and limonoids—in PAC. The phenolic acids included 3-O-feruloylquinic acid, 4-O-feruloylquinic acid and syringin. The alkaloids include magnoflorine, phellodendrine, jatrorrhizine, palmatine and berberine, while the limonoids include obaculactone and obacunone. An approach combining multi-wavelength and HPLC-DAD was used in this study due to the great difference in maximum absorption wavelength of the various components. Four wavelengths at 215, 275, 280 and 310 nm, respectively, were chosen for monitoring. It has been indicated through appropriate tests that this approach is of high accuracy, good repeatability and stability and provides a scientific basis for the quality assessment of PAC and associated derivatives. In addition, the chromatographic fingerprints method combined with multivariate statistical analysis chosen in this study was proved to be effective and reasonable for an accurate classification of 33 batches of samples collected from different locations