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result(s) for
"Wei Zheng"
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PIEZOs mediate neuronal sensing of blood pressure and the baroreceptor reflex
PIEZO1 and PIEZO2 are two mechanically activated ion channels that are highly expressed in lungs, bladder, and skin. Zeng et al. found that both ion channels are expressed in sensory neurons of a ganglion complex that contribute to the baroreflex, a homeostatic mechanism that helps to keep blood pressure stable (see the Perspective by Ehmke). Conditional double knockout of PIEZO1 and PIEZO2 in these neurons abolished the baroreflex and disrupted blood pressure regulation and heart rates in mice. These changes were very similar to those seen in patients with baroreflex failure. In mice, selective activation of PIEZO2-expressing ganglion neurons triggered immediate increases in heart rate and blood pressure. Science , this issue p. 464 ; see also p. 398 PIEZO1 and PIEZO2 are baroreceptor mechanosensors critical for acute blood pressure control. Activation of stretch-sensitive baroreceptor neurons exerts acute control over heart rate and blood pressure. Although this homeostatic baroreflex has been described for more than 80 years, the molecular identity of baroreceptor mechanosensitivity remains unknown. We discovered that mechanically activated ion channels PIEZO1 and PIEZO2 are together required for baroreception. Genetic ablation of both Piezo1 and Piezo2 in the nodose and petrosal sensory ganglia of mice abolished drug-induced baroreflex and aortic depressor nerve activity. Awake, behaving animals that lack Piezos had labile hypertension and increased blood pressure variability, consistent with phenotypes in baroreceptor-denervated animals and humans with baroreflex failure. Optogenetic activation of Piezo2 -positive sensory afferents was sufficient to initiate baroreflex in mice. These findings suggest that PIEZO1 and PIEZO2 are the long-sought baroreceptor mechanosensors critical for acute blood pressure control.
Journal Article
Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images
White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans. The algorithm was evaluated and ranked 1st in the WMH Segmentation Challenge at MICCAI 2017. In the evaluation stage, the implementation of the algorithm was submitted to the challenge organizers, who then independently tested it on a hidden set of 110 cases from 5 scanners. Averaged dice score, precision and robust Hausdorff distance obtained on held-out test datasets were 80%, 84% and 6.30 mm respectively. These were the highest achieved in the challenge, suggesting the proposed method is the state-of-the-art. Detailed descriptions and quantitative analysis on key components of the system were provided. Furthermore, a study of cross-scanner evaluation is presented to discuss how the combination of modalities affect the generalization capability of the system. The adaptability of the system to different scanners and protocols is also investigated. A quantitative study is further presented to show the effect of ensemble size and the effectiveness of the ensemble model. Additionally, software and models of our method are made publicly available. The effectiveness and generalization capability of the proposed system show its potential for real-world clinical practice.
•Describe the design, methodology, implementation details of our winning method for WMH Segmentation Challenge at MICCAI 2017.•Present an evaluation on both the public training set and the held-out test sets, and compare to other participating methods.•Present a cross-scanner evaluation on the generalization capability of the system.•Present a quantitative and a statistical study on ensemble models to test the effect of ensemble size and each element.
Journal Article
Mapping ticks and tick-borne pathogens in China
Understanding ecological niches of major tick species and prevalent tick-borne pathogens is crucial for efficient surveillance and control of tick-borne diseases. Here we provide an up-to-date review on the spatial distributions of ticks and tick-borne pathogens in China. We map at the county level 124 tick species, 103 tick-borne agents, and human cases infected with 29 species (subspecies) of tick-borne pathogens that were reported in China during 1950−2018.
Haemaphysalis longicornis
is found to harbor the highest variety of tick-borne agents, followed by
Ixodes persulcatus
,
Dermacentor nutalli
and
Rhipicephalus microplus
. Using a machine learning algorithm, we assess ecoclimatic and socioenvironmental drivers for the distributions of 19 predominant vector ticks and two tick-borne pathogens associated with the highest disease burden. The model-predicted suitable habitats for the 19 tick species are 14‒476% larger in size than the geographic areas where these species were detected, indicating severe under-detection. Tick species harboring pathogens of imminent threats to public health should be prioritized for more active field surveillance.
Ticks are an important vector of disease in China, posing threats to humans, livestock and wild animals. Here, Zhao et al. compile a database of the distributions of the 124 tick species known in China and 103 tick-borne pathogens and predict the additional suitable habitats for the predominant vector species.
Journal Article
PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction
In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, Path-Based MiRNA-Disease Association (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.
Journal Article
RGB-IR Person Re-identification by Cross-Modality Similarity Preservation
Person re-identification (Re-ID) is an important problem in video surveillance for matching pedestrian images across non-overlapping camera views. Currently, most works focus on RGB-based Re-ID. However, RGB images are not well suited to a dark environment; consequently, infrared (IR) imaging becomes necessary for indoor scenes with low lighting and 24-h outdoor scene surveillance systems. In such scenarios, matching needs to be performed between RGB images and IR images, which exhibit different visual characteristics; this cross-modality matching problem is more challenging than RGB-based Re-ID due to the lack of visible colour information in IR images. To address this challenge, we study the RGB-IR cross-modality Re-ID (RGB-IR Re-ID) problem. Rather than applying existing cross-modality matching models that operate under the assumption of identical data distributions between training and testing sets to handle the discrepancy between RGB and IR modalities for Re-ID, we cast learning shared knowledge for cross-modality matching as the problem of cross-modality similarity preservation. We exploit same-modality similarity as the constraint to guide the learning of cross-modality similarity along with the alleviation of modality-specific information, and finally propose a Focal Modality-Aware Similarity-Preserving Loss. To further assist the feature extractor in extracting shared knowledge, we design a modality-gated node as a universal representation of both modality-specific and shared structures for constructing a structure-learnable feature extractor called Modality-Gated Extractor. For validation, we construct a new multi-modality Re-ID dataset, called SYSU-MM01, to enable wider study of this problem. Extensive experiments on this SYSU-MM01 dataset show the effectiveness of our method. Download link of dataset: https://github.com/wuancong/SYSU-MM01.
Journal Article
Optically-controlled bacterial metabolite for cancer therapy
Bacteria preferentially accumulating in tumor microenvironments can be utilized as natural vehicles for tumor targeting. However, neither current chemical nor genetic approaches alone can fully satisfy the requirements on both stability and high efficiency. Here, we propose a strategy of “charging” bacteria with a nano-photocatalyst to strengthen their metabolic activities. Carbon nitride (C
3
N
4
) is combined with
Escherichia coli
(
E. coli
) carrying nitric oxide (NO) generation enzymes for photo-controlled bacterial metabolite therapy (PMT). Under light irradiation, photoelectrons produced by C
3
N
4
can be transferred to
E. coli
to promote the enzymatic reduction of endogenous NO
3
–
to cytotoxic NO with a 37-fold increase. In a mouse model, C
3
N
4
loaded bacteria are perfectly accumulated throughout the tumor and the PMT treatment results in around 80% inhibition of tumor growth. Thus, synthetic materials-remodeled microorganism may be used to regulate focal microenvironments and increase therapeutic efficiency.
Targeting tumors with bacteria as vehicles for metabolite therapy suffers from low efficiency and robustness. Here, the authors combine carbon nitride with nitric oxide generation enzyme-positive
E. coli
for photo-controlled metabolite therapy (PMT) and observe increased effects both in vitro and in tumor-bearing mice.
Journal Article
Spontaneously separated intermetallic Co3Mo from nanoporous copper as versatile electrocatalysts for highly efficient water splitting
Developing robust nonprecious electrocatalysts towards hydrogen/oxygen evolution reactions is crucial for widespread use of electrochemical water splitting in hydrogen production. Here, we report that intermetallic Co
3
Mo spontaneously separated from hierarchical nanoporous copper skeleton shows genuine potential as highly efficient electrocatalysts for alkaline hydrogen/oxygen evolution reactions in virtue of in-situ hydroxylation and electro-oxidation, respectively. The hydroxylated intermetallic Co
3
Mo has an optimal hydrogen-binding energy to facilitate adsorption/desorption of hydrogen intermediates for hydrogen molecules. Associated with high electron/ion transport of bicontinuous nanoporous skeleton, nanoporous copper supported Co
3
Mo electrodes exhibit impressive hydrogen evolution reaction catalysis, with negligible onset overpotential and low Tafel slope (~40 mV dec
−1
) in 1 M KOH, realizing current density of −400 mA cm
−2
at overpotential of as low as 96 mV. When coupled to its electro-oxidized derivative that mediates efficiently oxygen evolution reaction, their alkaline electrolyzer operates with a superior overall water-splitting output, outperforming the one assembled with noble-metal-based catalysts.
Electrochemical water splitting is an attractive energy conversion technology, but it usually suffers from low efficiency. Here, the authors report intermetallic Co
3
Mo integrated on porous Cu as highly efficient electrocatalysts for alkaline HER/OER due to in-situ hydroxylation and electro-oxidation.
Journal Article