Publications by authors named "Xingjian Zhang"

Understanding the molecular mechanisms of abiotic stress responses in plants is instrumental for the development of climate-resilient crops. Key factors in abiotic stress responses, such as the proton- pumping pyrophosphatase (AVP1), have been identified, but their function and regulation remain elusive. Here, we explored the post-translational regulation of AVP1 by the ubiquitin-conjugating enzyme UBC34 and its relevance in the salt stress and phosphate starvation responses of Arabidopsis (Arabidopsis thaliana).

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: Manually labeling sleep stages is time-consuming and labor-intensive, making automatic sleep staging methods crucial for practical sleep monitoring. While both single- and multi-channel data are commonly used in automatic sleep staging, limited research has adequately investigated the differences in their effectiveness. In this study, four public data sets-Sleep-SC, APPLES, SHHS1, and MrOS1-are utilized, and an advanced hybrid attention neural network composed of a multi-branch convolutional neural network and the multi-head attention mechanism is employed for automatic sleep staging.

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Quantum theory promises computational speed-ups over classical approaches. The celebrated Gottesman-Knill Theorem implies that the full power of quantum computation resides in the specific resource of "magic" states-the secret sauce to establish universal quantum computation. However, it is still questionable whether magic indeed brings the believed quantum advantage, ridding unproven complexity assumptions or black-box oracles.

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Introduction: Renal tubulointerstitial inflammation represents an effective indicator for predicting the progression of diabetic kidney disease (DKD). Mitophagy abnormality is 1 of the most important factors involved in tubule injury. However, the exact molecular mechanism underlying mitophagy abnormality-mediated tubulointerstitial inflammation in DKD remains poorly understood.

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Humans and animals have a remarkable capacity to collectively coordinate their behavior to respond to environmental challenges. However, the underlying neurobiology remains poorly understood. Here, we found that groups of mice self-organize into huddles at cold ambient temperature during the thermal challenge assay.

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The cytoplasm of most plant cells is connected by membrane-lined cell wall channels, the plasmodesmata (PD). Dynamic regulation of sugar, hormone, and protein diffusion through PD is essential for plant development and stress responses. Understanding this regulation requires knowledge of factors and mechanisms that control PD permeability through the modulation of callose levels in the cell wall around PD openings.

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Objective: The evaluate the feasibility of a novel deep learning-reconstructed ultra-fast respiratory-triggered T2WI sequence (DL-RT-T2WI) In liver imaging, compared with respiratory-triggered Arms-T2WI (Arms-RT-T2WI) and respiratory-triggered FSE-T2WI (FSE-RT-T2WI) sequences.

Methods: 71 patients with liver lesions underwent 3-T MRI and were prospectively enrolled. Two readers independently analyzed images acquired with DL-RT-T2WI, Arms-RT-T2WI, and FSE-RT-T2WI.

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Background: Tubulointerstitial fibrosis plays an important role in the progression of diabetic kidney disease (DKD). Sacubitril/valsartan (Sac/Val) exerts a robust beneficial effect in DKD. However, the potential functional effect of Sac/Val on tubulointerstitial fibrosis in DKD is still largely unclear.

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The chemical inertness of CO molecules makes their adsorption and activation on a catalyst surface one of the key challenges in recycling CO into chemical fuels. However, the traditional template synthesis and chemical modification strategies used to tackle this problem face severe structural collapse and modifier deactivation issues during the often-needed post-processing procedure. Herein, a CO self-selective hydrothermal growth strategy is proposed for the synthesis of CeO octahedral nanocrystals that participate in strong physicochemical interactions with CO molecules.

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Zero-knowledge proof (ZKP) is a fundamental cryptographic primitive that allows a prover to convince a verifier of the validity of a statement without leaking any further information. As an efficient variant of ZKP, noninteractive zero-knowledge proof (NIZKP) adopting the Fiat-Shamir heuristic is essential to a wide spectrum of applications, such as federated learning, blockchain, and social networks. However, the heuristic is typically built upon the random oracle model that makes ideal assumptions about hash functions, which does not hold in reality and thus undermines the security of the protocol.

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During development and disease progression, cells are subject to osmotic and mechanical stresses that modulate cell volume, which fundamentally influences cell homeostasis and has been linked to a variety of cellular functions. It is not well understood how the mechanobiological state of cells is programmed by the interplay of intracellular organization and complex extracellular mechanics when stimulated by cell volume modulation. Here, by controlling cell volume via osmotic pressure, we evaluate physical phenotypes (including cell shape, morphodynamics, traction force, and extracellular matrix (ECM) remodeling) and molecular signaling (YAP), and we uncover fundamental transitions in active biophysical states.

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Complementarity is an essential feature of quantum mechanics. The preparation of an eigenstate of one observable implies complete randomness in its complementary observable. In quantum cryptography, complementarity allows us to formulate security analyses in terms of phase-error correction.

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Precise and real-time measurements of economic prosperity are vital to a country's economic system. This study aims to identify news topics that promoted economic prosperity in China from 2011-2021. By extracting economic topics from news text data, we construct a news coincidence index with comprehensive information and strong timeliness and reveal the trend of topic contribution.

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The lipid-inspired biosilicification process enables the creation of a series of concave silica nanoarchitectures in the complex shapes of nanobowls, nanodishes, nanoboats, and nanoloops. The reaction at a pH of 8 initially allows the formation of thin and elastic circular gel nanosheets that can undergo inducible stretching and folding, which subsequently evolves into nanodish and nanobowl through a potential global buckling process. The adjustment of the pH to 9 and 4 enables the production of more complex morphogens of nanoboat and nanoloop, respectively.

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Our previous study found that the intravesical perfusion of metformin has excellent inhibitory effects against bladder cancer (BC). However, this administration route allows the drug to be diluted and excreted in urine. Therefore, increasing the adhesion of metformin to the bladder mucosal layer may prolong the retention time and increase the pharmacological activity.

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Physical forces are prominent during tumor progression. However, it is still unclear how they impact and drive the diverse phenotypes found in cancer. Here, we apply an integrative approach to investigate the impact of compression on melanoma cells.

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Sailing has gained an increasing attention among children and adolescents in China, which raised a strong need for sail courses through physical education (PE). However, challenges in teaching practice arise with rapid development of the sport. In the current study, we proposed a perspective that virtual reality (VR) technology makes high-quality sail education accessible for students.

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Background: Trends in cardiovascular disease (CVD) risk factors and mortality have been reported among US adults with diabetes, but not among those with prediabetes. This study aimed to examine and compare the trends in CVD risk factors and mortality in US adults with diabetes and prediabetes.

Methods: In this serial, cross-sectional study, medical records of prediabetic patients from the National Health and Nutrition Examination Survey (NHANES) among adults aged 18 years or older were retrospectively reviewed.

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The brain-computer interface (BCI), also known as a brain-machine interface (BMI), has attracted extensive attention in biomedical applications. More importantly, BCI technologies have substantially revolutionized early predictions, diagnostic techniques, and rehabilitation strategies addressing acute diseases because of BCI's innovations and clinical translations. Therefore, in this chapter, a comprehensive description of the basic concepts of BCI will be exhibited, and various visualization techniques employed in BCI's medical applications will be discussed.

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Objectives: This study aims to assess the risk of relapse after complete remission (CR) and partial remission (PR), and to develop a prognostic nomogram predicting the probability in lupus nephritis (LN) patients.

Methods: Data from patients with LN who had been in remission were collected as a training cohort. The prognostic factors were analyzed using the univariable and multivariable Cox model for the training group.

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Podocyte injury is a characteristic feature of diabetic nephropathy (DN). The secretion of exosomes in podocytes increases significantly in DN; however, the precise mechanisms remain poorly understood. Here, we demonstrated that Sirtuin1 (Sirt1) was significantly downregulated in podocytes in DN, which correlated negatively with increased exosome secretion.

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Patient-dependent seizure detection based on intracranial electroencephalography (iEEG) has made significant progress. However, due to the difference in the locations and number of iEEG electrodes used for each patient, patient-independent seizure detection based on iEEG has not been carried out. Additionally, current seizure detection algorithms based on deep learning have outperformed traditional machine learning algorithms in many performance metrics.

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Motivation: In this work, we present an analytical method for quantifying both single-cell morphologies and cell network topologies of tumor cell populations and use it to predict 3D cell behavior.

Results: We utilized a supervised deep learning approach to perform instance segmentation on label-free live cell images across a wide range of cell densities. We measured cell shape properties and characterized network topologies for 136 single-cell clones derived from the YUMM1.

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Background: Delayed graft function (DGF) is one of the most common postoperative complications after kidney transplantation. The ability to predict DGF after transplantation can greatly aid clinical decision-making. Several models have been proposed to predict DGF in adult recipients of adult donor kidneys, but there is currently no model to predict DGF in adult recipients of pediatric donor transplants.

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3D in vitro tumor models have recently been investigated as they can recapitulate key features in the tumor microenvironment. Reconstruction of a biomimetic scaffold is critical in these models. However, most current methods focus on modulating local properties, e.

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