Dendrobium officinale phenolic extract maintains proteostasis by regulating autophagy in a Caenorhabditis elegans model of Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disease, and accumulating evidence suggested that proteostatic imbalance is a key feature of the disease. Traditional Chinese medicine exhibits a multi-target therapeutic effect, making it highly suitable for addressing protein homeostasis imbalance in AD. Dendrobium officinale is a traditional Chinese herbs commonly used as tonic agent in China. In this study, we investigated protection effects of D. officinale phenolic extract (SH-F) and examined its underlying mechanisms by using transgenic Caenorhabditis elegans models. We found that treatment with SH-F (50 µg/mL) alleviated Aß and tau protein toxicity in worms, and also reduced aggregation of polyglutamine proteins to help maintain proteostasis. RNA sequencing results showed that SH-F treatment significantly affected the proteolytic process and autophagy-lysosomal pathway. Furthermore, we confirmed that SH-F showing maintainance of proteostasis was dependent on bec-1 by qRT-PCR analysis and RNAi methods. Finally, we identified active components of SH-F by LC-MS method, and found the five major compounds including koaburaside, tyramine dihydroferulate, N-p-trans-coumaroyltyramine, naringenin and isolariciresinol are the main bioactive components responsible for the anti-AD activity of SH-F. Our findings provide new insights to develop a treatment strategy for AD by targeting proteostasis, and SH-F could be an alternative drug for the treatment of AD.

Spatiotemporal Excitation Module-based CNN for Diagnosis of Hepatic Malignancy in Four-phase CT Images.

Liver cancer is a part of the common causes of cancer death worldwide, and the accurate diagnosis of hepatic malignancy is important for effective next treatment. In this paper, we propose a convolutional neural network (CNN) based on a spatiotemporal excitation (STE) module for identification of hepatic malignancy in four-phase computed tomography (CT) images. To enhance the display detail of lesion, we expand single-channel CT images into three channels by using the channel expansion method. Our proposed STE module consists of a spatial excitation (SE) module and a temporal interaction (TI) module. The SE module calculates the temporal differences of CT slices at the feature level, which is used to excite shape-sensitive channels of the lesion features. The TI module shifts a portion of the channels in the temporal dimension to exchange information among the current CT slice and adjacent CT slices. Four-phase CT images of 398 patients diagnosed with hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are used for experiments and five cross-validations are performed. Our model achieved average accuracy of 85.00% and average AUC of 88.91% for classifying HCC and ICC.Clinical Relevance- The proposed deep learning-based model can perform HCC and ICC recognition tasks based on four-phase CT images, assisting doctors to obtain better diagnostic performance.

Potential window alignment regulating ion transfer in faradaic junctions for efficient photoelectrocatalysis.

In the past decades, a band alignment theory has become a basis for designing different high-performance semiconductor devices, such as photocatalysis, photoelectrocatalysis, photoelectrostorage and third-generation photovoltaics. Recently, a faradaic junction model (coupled electron and ion transfer) has been proposed to explain charge transfer phenomena in these semiconductor heterojunctions. However, the classic band alignment theory cannot explain coupled electron and ion transfer processes because it only regulates electron transfer. Therefore, it is very significant to explore a suitable design concept for regulating coupled electron and ion transfer in order to improve the performance of semiconductor heterojunctions. Herein, we propose a potential window alignment theory for regulating ion transfer and remarkably improving the photoelectrocatalytic performance of a MoS2/Cd-Cu2ZnSnS4 heterojunction photocathode. Moreover, we find that a faradaic potential window, rather than the band position of the intermediate layer, is a criterion for identifying interface charge transfer direction. This finding can offer different perspectives for designing high-performance semiconductor heterojunctions with suitable potential windows for solar energy conversion and storage.

Liensinine and neferine exert neuroprotective effects via the autophagy pathway in transgenic Caenorhabditis elegans.

BACKGROUND: Liensinine and neferine are the main bisbenzylisoquinoline alkaloids obtained from the seeds of Nelumbo nucifera, which commonly used as edible food and traditional medicine in Asia. It was reported that liensinine and neferine could inhibit the activities of acetylcholinesterase and cross the blood-brain barriers, suggesting their therapeutic potential for the management of Alzheimer's disease. METHODS: Here, we employed SH-SY5Y human neuroblastoma cells stably transfected with the human Swedish amyloid precursor protein (APP) mutation APP695 (APP695swe SH-SY5Y) as an in vitro model and transgenic Caenorhabditis elegans as an in vivo model to investigate the neuroprotective effects and underlying mechanism of liensinine and neferine. RESULTS: We found that liensinine and neferine could significantly improve the viability and reduce ROS levels in APP695swe SH-SY5Y cells, inhibit ß-amyloid and tau-induced toxicity, and enhance stress resistance in nematodes. Moreover, liensinine and neferine had obviously neuroprotective effects by assaying chemotaxis, 5-hydroxytryptamine sensitivity and the integrity of injured neurons in nematodes. Preliminary mechanism studies revealed that liensinine and neferine could upregulate the expression of autophagy related genes (lgg-1, unc-51, pha-4, atg-9 and ced-9) and reduce the accumulation of ß-amyloid induced autophagosomes, which suggested autophagy pathway played a key role in neuroprotective effects of these two alkaloids. CONCLUSIONS: Altogether, our findings provided a certain working foundation for the use of liensinine and neferine to treat Alzheimer's disease based on neuroprotective effects.

Bipolarized intrinsic faradaic layer on a semiconductor surface under illumination.

Interface charge transfer plays a key role in the performance of semiconductors for different kinds of solar energy utilization, such as photocatalysis, photoelectrocatalysis, photochromism and photo-induced superhydrophilicity. In previous studies, different mechanisms have been used to understand interface charge transfer processes. However, the charge transfer mechanism at the solid/liquid interface remains a controversial topic. Here, taking TiO2 as a model, we find and prove, via experiments, the new characteristic of photo-induced bipolarity of the surface layer (reduction faradaic layer and oxidation faradaic layer) on a semiconductor for the first time. Different from energy level positions in the classic surface states transfer mechanism, the potential window of a surface faradaic layer is located out of the forbidden band. Moreover, we find that the reduction faradaic layer and oxidation faradaic layer serve as electron and hole transfer mediators in photocatalysis, while the bipolarity or mono-polarity of the surface layer on a semiconductor depends on the applied potential in photoelectrocatalysis. The new characteristic of bipolarity can also offer new insights into the charge transfer process at the semiconductor/liquid interface for solar energy utilization.

CT-based COPD identification using multiple instance learning with two-stage attention.

BACKGROUND AND OBJECTIVE: Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality worldwide. However, COPD remains underdiagnosed globally. Spirometry is currently the primary tool for diagnosing COPD, but it has unneglected difficulties in detecting mild COPD. Chest computed tomography (CT) has been validated for COPD diagnosis and quantification. Whereas many CT-based deep learning approaches have been developed to identify COPD, it remains challenging to characterize CT-based pathological alternations of COPD which are multidimensional and highly spatially heterogeneous, and the diagnosis performance still needs to be improved. METHODS: A multiple instance learning (MIL) with two-stage attention (TSA-MIL) is proposed to identify COPD using CT images. Based on transfer learning, a Resnet-50 model pre-trained on natural images is used to extract multicomponent and multidimensional features of COPD abnormalities, in which a pseudo-color method is designed to transfer single-channel CT slices to RGB-like three channels and meanwhile increase the richness of feature representations. To generate more robust attention score for each instance, a two-stage attention module is utilized with the first stage aiming at discovering the key instance while the second stage correcting the attention score for each instance by calculating its average relative distance to the key instances; besides, an instance-level clustering over feature domain is exploited to further improve feature separability and therefore facilitate the subsequent attention module. CT scans, spirometry and demographic data of a total of 800 participants were collected from a large public hospital, with 720 and 80 participants used for model development and evaluation, respectively. In addition, data of 260 participants from another large hospital were also collected for external validation. RESULTS AND CONCLUSIONS: The proposed TSA-MIL approach outperforms not only most of the advanced MIL models, but also other up-to-date COPD identification methods, with an accuracy of 0.9200 and an area under curve (AUC) of 0.9544 on the test set, and with an accuracy of 0.8115 and an AUC of 0.8737 on the external validation set without multicenter effect reduction, which is clinically acceptable. Therefore, this approach is promising to be a powerful tool for COPD diagnosis in clinical practice.

Dual-Stream Subspace Clustering Network for revealing gene targets in Alzheimer's disease.

The rapid development of scRNA-seq technology in recent years has enabled us to capture high-throughput gene expression profiles at single-cell resolution, reveal the heterogeneity of complex cell populations, and greatly advance our understanding of the underlying mechanisms in human diseases. Traditional methods for gene co-expression clustering are limited to discovering effective gene groups in scRNA-seq data. In this paper, we propose a novel gene clustering method based on convolutional neural networks called Dual-Stream Subspace Clustering Network (DS-SCNet). DS-SCNet can accurately identify important gene clusters from large scales of single-cell RNA-seq data and provide useful information for downstream analysis. Based on the simulated datasets, DS-SCNet successfully clusters genes into different groups and outperforms mainstream gene clustering methods, such as DBSCAN and DESC, across different evaluation metrics. To explore the biological insights of our proposed method, we applied it to real scRNA-seq data of patients with Alzheimer's disease (AD). DS-SCNet analyzed the single-cell RNA-seq data with 10,850 genes, and accurately identified 8 optimal clusters from 6673 cells. Enrichment analysis of these gene clusters revealed functional signaling pathways including the ILS signaling, the Rho GTPase signaling, and hemostasis pathways. Further analysis of gene regulatory networks identified new hub genes such as ELF4 as important regulators of AD, which indicates that DS-SCNet contributes to the discovery and understanding of the pathogenesis in Alzheimer's disease.

A Fissure-Aided Registration Approach for Automatic Pulmonary Lobe Segmentation Using Deep Learning.

The segmentation of pulmonary lobes is important in clinical assessment, lesion location, and surgical planning. Automatic lobe segmentation is challenging, mainly due to the incomplete fissures or the morphological variation resulting from lung disease. In this work, we propose a learning-based approach that incorporates information from the local fissures, the whole lung, and priori pulmonary anatomy knowledge to separate the lobes robustly and accurately. The prior pulmonary atlas is registered to the test CT images with the aid of the detected fissures. The result of the lobe segmentation is obtained by mapping the deformation function on the lobes-annotated atlas. The proposed method is evaluated in a custom dataset with COPD. Twenty-four CT scans randomly selected from the custom dataset were segmented manually and are available to the public. The experiments showed that the average dice coefficients were 0.95, 0.90, 0.97, 0.97, and 0.97, respectively, for the right upper, right middle, right lower, left upper, and left lower lobes. Moreover, the comparison of the performance with a former learning-based segmentation approach suggests that the presented method could achieve comparable segmentation accuracy and behave more robustly in cases with morphological specificity.

Photovoltage memory effect in a portable Faradaic junction solar rechargeable device.

Two-electrode solar rechargeable device is one of the promising technologies to address the problem of solar energy storage in large scale. However, the mechanism of dark output voltage remains unclear and the low volumetric energy density also limits its practical applications. Herein, we report that a Si/CoOx/KBi(aq)/MnOx Faradaic junction device exhibits a photovoltage memory effect, that is, the dark output voltage can precisely record the value of the photovoltage in the device. To investigate the mechanism of the effect, we develop an open circuit potential method to real-time monitor the photo charge and dark discharge processes in the Faradaic junction device. This effect leads to minimized interface energy loss in the Faradaic junction device, which achieves much higher performances than the devices without the effect. Moreover, we realize a portable device with a record value of the dark volumetric energy density (∼1.89 mJ cm-3) among all reported two-electrode solar rechargeable devices. These results offer guidance to improve the performance of a solar rechargeable device and design other photoelectric devices for new applications.

One Spatio-Temporal Sharpening Attention Mechanism for Light-Weight YOLO Models Based on Sharpening Spatial Attention.

Attention mechanisms have demonstrated great potential in improving the performance of deep convolutional neural networks (CNNs). However, many existing methods dedicate to developing channel or spatial attention modules for CNNs with lots of parameters, and complex attention modules inevitably affect the performance of CNNs. During our experiments of embedding Convolutional Block Attention Module (CBAM) in light-weight model YOLOv5s, CBAM does influence the speed and increase model complexity while reduce the average precision, but Squeeze-and-Excitation (SE) has a positive impact in the model as part of CBAM. To replace the spatial attention module in CBAM and offer a suitable scheme of channel and spatial attention modules, this paper proposes one Spatio-temporal Sharpening Attention Mechanism (SSAM), which sequentially infers intermediate maps along channel attention module and Sharpening Spatial Attention (SSA) module. By introducing sharpening filter in spatial attention module, we propose SSA module with low complexity. We try to find a scheme to combine our SSA module with SE module or Efficient Channel Attention (ECA) module and show best improvement in models such as YOLOv5s and YOLOv3-tiny. Therefore, we perform various replacement experiments and offer one best scheme that is to embed channel attention modules in backbone and neck of the model and integrate SSAM into YOLO head. We verify the positive effect of our SSAM on two general object detection datasets VOC2012 and MS COCO2017. One for obtaining a suitable scheme and the other for proving the versatility of our method in complex scenes. Experimental results on the two datasets show obvious promotion in terms of average precision and detection performance, which demonstrates the usefulness of our SSAM in light-weight YOLO models. Furthermore, visualization results also show the advantage of enhancing positioning ability with our SSAM.

Faradaic junction and isoenergetic charge transfer mechanism on semiconductor/semiconductor interfaces.

Energy band alignment theory has been widely used to understand interface charge transfer in semiconductor/semiconductor heterojunctions for solar conversion or storage, such as quantum-dot sensitized solar cells, perovskite solar cells and photo(electro)catalysis. However, abnormally high open-circuit voltage and charge separation efficiency in these applications cannot be explained by the classic theory. Here, we demonstrate a Faradaic junction theory with isoenergetic charge transfer at semiconductor/semiconductor interface. Such Faradaic junction involves coupled electron and ion transfer, which is substantively different from the classic band alignment theory only involving electron transfer. The Faradaic junction theory can be used to explain these abnormal results in previous studies. Moreover, the characteristic of zero energy loss of charge transfer in a Faradaic junction also can provide a possibility to design a solar conversion device with a large open-circuit voltage beyond the Shockley-Queisser limit by the band alignment theory.

X-ray Pulsar-Based Navigation Considering Spacecraft Orbital Motion and Systematic Biases.

The accuracy of X-ray pulsar-based navigation is greatly affected by the Doppler effect caused by the spacecraft orbital motion and the systematic biases introduced by the pulsar directional error, spacecraft-borne clock error, etc. In this paper, an innovative navigation method simultaneously employing the pulse phase (PP), the difference of two neighbor PPs (DPP) and the Doppler frequency (DF) of X-ray pulsars as measurements is proposed to solve this problem. With the aid of the spacecraft orbital dynamics, a single pair of PP and DF relative to the spacecraft's state estimation error can be estimated by using the joint probability density function of the arrival photon timestamps as the likelihood function. The systematic biases involved to the PP is proved to be nearly invariant over two adjacent navigation periods and the major part of it is eliminated in the DPP; therefore, the DPP is also exploited as additional navigation measurement to weaken the impact of systematic biases on navigation accuracy. Results of photon-level simulations show that the navigation accuracy of the proposed method is remarkably better than that of the method only using PP, the method using both PP and DF and the method using both PP and DPP for Earth orbit.

Improved Multitarget Tracking in Clutter Using Bearings-Only Measurements.

Multitarget tracking in clutter using bearings-only measurements is a challenging problem. In this paper, a performance improved nonlinear filter is proposed on the basis of the Random Finite Set (RFS) theory and is named as Gaussian mixture measurements-based cardinality probability hypothesis density (GMMbCPHD) filter. The GMMbCPHD filter enables to address two main issues: measurement-origin-uncertainty and measurement nonlinearity, which constitutes the key problems in bearings-only multitarget tracking in clutter. For the measurement-origin-uncertainty issue, the proposed filter estimates the intensity of RFS of multiple targets as well as propagates the posterior cardinality distribution. For the measurement-origin-nonlinearity issue, the GMMbCPHD approximates the measurement likelihood function using a Gaussian mixture rather than a single Gaussian distribution as used in extended Kalman filter (EKF). The superiority of the proposed GMMbCPHD are validated by comparing with several state-of-the-art algorithms via intensive simulation studies.