Showing papers by "Fan Yang published in 2022"

Cancer statistics in China and United States, 2022: profiles, trends, and determinants

Abstract: Abstract Background: The cancer burden in the United States of America (USA) has decreased gradually. However, China is experiencing a transition in its cancer profiles, with greater incidence of cancers that were previously more common in the USA. This study compared the latest cancer profiles, trends, and determinants between China and USA. Methods: This was a comparative study using open-source data. Cancer cases and deaths in 2022 were calculated using cancer estimates from GLOBOCAN 2020 and population estimates from the United Nations. Trends in cancer incidence and mortality rates in the USA used data from the Surveillance, Epidemiology, and End Results program and National Center for Health Statistics. Chinese data were obtained from cancer registry reports. Data from the Global Burden of Disease 2019 and a decomposition method were used to express cancer deaths as the product of four determinant factors. Results: In 2022, there will be approximately 4,820,000 and 2,370,000 new cancer cases, and 3,210,000 and 640,000 cancer deaths in China and the USA, respectively. The most common cancers are lung cancer in China and breast cancer in the USA, and lung cancer is the leading cause of cancer death in both. Age-standardized incidence and mortality rates for lung cancer and colorectal cancer in the USA have decreased significantly recently, but rates of liver cancer have increased slightly. Rates of stomach, liver, and esophageal cancer decreased gradually in China, but rates have increased for colorectal cancer in the whole population, prostate cancer in men, and other seven cancer types in women. Increases in adult population size and population aging were major determinants for incremental cancer deaths, and case-fatality rates contributed to reduced cancer deaths in both countries. Conclusions: The decreasing cancer burden in liver, stomach, and esophagus, and increasing burden in lung, colorectum, breast, and prostate, mean that cancer profiles in China and the USA are converging. Population aging is a growing determinant of incremental cancer burden. Progress in cancer prevention and care in the USA, and measures to actively respond to population aging, may help China to reduce the cancer burden.

Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis

Abstract: Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism–related pathway. Untargeted lipidomics was carried out in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum–based feature selection, we identified nine lipids (lysophosphatidylcholines 16:0, 18:0, and 20:4; phosphatidylcholines 16:0–18:1, 16:0–18:2, 18:0–18:1, 18:0–18:2, and 16:0–22:6; and triglycerides 16:0–18:1–18:1) as the features most important for early-stage cancer detection. Using these nine features, we developed a liquid chromatography–mass spectrometry (MS)–based targeted assay using multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low-dose computed tomography and a prospective clinical cohort containing 109 participants, the assay reached more than 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization MS imaging confirmed that the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. This method, designated as Lung Cancer Artificial Intelligence Detector, may be useful for early detection of lung cancer or large-scale screening of high-risk populations for cancer prevention. Description Nine lipids detect stage I lung cancer across multiple independent cohorts. Lipidomics for early lung cancer detection Improving early detection of lung cancer is key to improving outcomes. On the basis of their exploratory single-cell RNA sequencing suggesting dysregulated lipid metabolism in early lung cancer tissue, Wang et al. performed lung plasma lipidomic profiling and found that nine lipids were commonly dysregulated in patients with early-stage lung tumors. The authors validated the ability of these nine lipids to detect early-stage cancer across multiple independent cohorts largely composed of nonsmokers with stage I adenocarcinomas, as compared with healthy individuals or individuals with benign tumors. This study shows promise for the clinical detection of early-stage lung cancer in patients.

3D-Printed PCL/Zn scaffolds for bone regeneration with a dose-dependent effect on osteogenesis and osteoclastogenesis

Abstract: Polycaprolactone (PCL) is a polymer material suitable for being prepared into porous scaffolds used in bone tissue engineering, however, insufficient osteogenic ability and mechanical strength limit its application. Zinc (Zn) alloy with proper mechanical strength and osteogenesis is a promising biodegradable metal that have attracted much attention. Herein, we combined the advantages of PCL and Zn by fabricating PCL/Zn composite scaffolds with different Zn powder contents (1 ​wt%, 2 ​wt%, 3 ​wt%) through fused deposition modelling. The ​mechanical property, cytocompatibility and Zn ​ions release ​behavior of PCL/Zn scaffolds were analyzed ​in vitro. The osteogenesis and osteoclastogenesis properties of the scaffolds were evaluated by being implanted into Sprague-Dawley rats calvaria defect. Results showed that the PCL/Zn scaffolds exhibited improved mechanical properties and cytocompatibility compared with the pure PCL scaffolds. At 8 weeks after in vivo implantaion, the addition of Zn powder promoted new bone formation, in a dose-dependent manner. The scaffolds with 2 ​wt% Zn displayed the best osteogenic effect, while the osteogenic effect was slightly reduced in the scaffolds with 3 ​wt% Zn. In the studied Zn contents, the PCL/Zn scaffolds gradually promoted osteoclastogenesis with increasd Zn content. In the 3 ​wt% Zn group, TRAP-positive cells were observed on the newly formed bone edges around the scaffolds. These dose-dependent effects were verified in vitro using MC3T3-E1 and RAW264.7 ​cells. Finally, we revealed that Zn2+ regulated osteogenesis and osteoclastogenesis by activation of the Wnt/β-catenin and NF-κB signalling pathways, respectively.

CoP Nanoparticle Confined in P, N Co-Doped Porous Carbon Anchored on P-Doped Carbonized Wood Fibers with Tailored Electronic Structure for Efficient Urea Electro-Oxidation.

Abstract: Electronic structure optimization and architecture modulation are widely regarded as rational strategies to enhance the electrocatalysts catalytic performance. Herein, a hybridization of ZIF-67-derived CoP nanoparticles embedded in P, N co-doped carbon matrix (PNC) and anchored on P-doped carbonized wood fibers (PCWF) is constructed using a simple simultaneous phosphorization and carbonization strategy. Benefiting from the optimized surface/interface electronic structures, abundant exposed active sites, and outstanding conductivity, the CoP@PNC/PCWF can drive the urea oxidation reaction (UOR) with greater activity and better stability than most recently reported electrocatalysts, in which a potential as low as 1.32 V (vs reversible hydrogen electrode, RHE) is needed to reach 50 mA cm-2 and shows excellent durability. Furthermore, for overall urea splitting, using the CoP@PNC/PCWF electrocatalyst as the anode and commercial Pt/C supported on nickel foam as the cathode, an ultralow cell voltage of 1.50 V (vs RHE) is expected to achieve the 50 mA cm-2 and operate continuously for more than 50 h at 20 mA cm-2 . The reported strategy may shed light on the use of renewable resources to design and synthesize high-performance non-Ni-based phosphides UOR electrocatalysts for energy-saving H2 production.

Learning Optical Flow with Adaptive Graph Reasoning

Abstract: Estimating per-pixel motion between video frames, known as optical flow, is a long-standing problem in video understanding and analysis. Most contemporary optical flow techniques largely focus on addressing the cross-image matching with feature similarity, with few methods considering how to explicitly reason over the given scene for achieving a holistic motion understanding. In this work, taking a fresh perspective, we introduce a novel graph-based approach, called adaptive graph reasoning for optical flow (AGFlow), to emphasize the value of scene/context information in optical flow. Our key idea is to decouple the context reasoning from the matching procedure, and exploit scene information to effectively assist motion estimation by learning to reason over the adaptive graph. The proposed AGFlow can effectively exploit the context information and incorporate it within the matching procedure, producing more robust and accurate results. On both Sintel clean and final passes, our AGFlow achieves the best accuracy with EPE of 1.43 and 2.47 pixels, outperforming state-of-the-art approaches by 11.2% and 13.6%, respectively. Code is publicly available at https://github.com/megvii-research/AGFlow.

Unshared Pair Electrons of Zincophilic Lewis Base Enable Long-life Zn Anodes under "Three High" Conditions.

Abstract: The hydrogen evolution and dendrite issues are the notorious culprits of the limited lifespan and Coulombic efficiency (CE) of Zn anodes, particularly at harsh test conditions. Herein, considering the Lewis acidic feature of Zn 2+ , abundant unshared pair electrons of zincophilic Lewis bases are proposed as decent electrolyte additives to stabilize Zn anodes at "Three High" conditions (high depth of discharge, high areal capacity and high current). The unshared pair electrons can remove H 2 O from Zn 2+ solvated sheaths and confine the activity of H 2 O by breaking its hydrogen bonding network. The combination of these factors effectively restricts H 2 O-involved side reactions and enables dendrite-free Zn deposition, even at harsh conditions. The effectiveness of this strategy is fully proved by a series of Lewis base molecules. Noticeably, the Zn||Zn cell with an area of 20 cm 2 , using DMF-containing electrolyte can be stably cycled over 1000 h at 60% DOD, with a 100% CE.

Strong Interaction over Ru/Defects‐Rich Aluminium Oxide Boosts Photothermal CO2 Methanation via Microchannel Flow‐Type System

Abstract: The CO2 methanation is an important component of the “power to gas” strategy, and the Ru‐Al2O3 catalyst is considered to be a state‐of‐the‐art catalyst for this reaction. Conventional Ru‐Al2O3 is prepared by wet impregnation. Due to weak interactions between Ru and the Al2O3, construction of a controllable interface between the metal and the substrate is still challenging. In this work, a UV pulse laser is used to controllably construct ultra‐small Ru nanoparticles on defects‐rich Al2O3‐x‐L in situ grown on Al foil (Ru‐Al2O3‐x‐L) for effective photothermal CO2 methanation. The catkin‐like fluff Al2O3‐x‐L efficiently traps light to ensure the light adsorption of Ru‐Al2O3‐x‐L. The defects in Al2O3‐x‐L efficiently anchors Ru. A Strong‐Metal‐Support‐Interaction (SMSI) effect is constructed between the ultra‐small Ru nanoparticles and the Al2O3‐x‐L. The Ru‐Al2O3‐x‐L exhibits remarkable photothermal catalytic performance (CH4 yield of 12.35 mol gRu−1 h−1) in the closed batch system. Then an innovative flow reactor is established based on the one‐piece Ru‐Al2O3‐x‐L microchannel catalyst. Thanks to local pressure on the edge of the microchannels, the CH4 yield is further enhanced to 14.04 mol gRu−1 h−1. Finally, an outdoor setup demonstrates the feasibility of photothermal CO2 methanation (CH4 yield of 18.00 mmol min−1). This work provides novel perspectives for the construction of multi‐level micro/nanostructures integrated catalysts for photothermal CO2 methanation.

Kindlin-2 haploinsufficiency protects against fatty liver by targeting Foxo1 in mice

Abstract: Nonalcoholic fatty liver disease (NAFLD) affects a large population with incompletely defined mechanism(s). Here we report that Kindlin-2 is dramatically up-regulated in livers in obese mice and patients with NAFLD. Kindlin-2 haploinsufficiency in hepatocytes ameliorates high-fat diet (HFD)-induced NAFLD and glucose intolerance without affecting energy metabolism in mice. In contrast, Kindlin-2 overexpression in liver exacerbates NAFLD and promotes lipid metabolism disorder and inflammation in hepatocytes. A C-terminal region (aa 570-680) of Kindlin-2 binds to and stabilizes Foxo1 by inhibiting its ubiquitination and degradation through the Skp2 E3 ligase. Kindlin-2 deficiency increases Foxo1 phosphorylation at Ser256, which favors its ubiquitination by Skp2. Thus, Kindllin-2 loss down-regulates Foxo1 protein in hepatocytes. Foxo1 overexpression in liver abrogates the ameliorating effect of Kindlin-2 haploinsufficiency on NAFLD in mice. Finally, AAV8-mediated shRNA knockdown of Kindlin-2 in liver alleviates NAFLD in obese mice. Collectively, we demonstrate that Kindlin-2 insufficiency protects against fatty liver by promoting Foxo1 degradation.

Does top management team responsible leadership help employees go green? The role of green human resource management and environmental felt‐responsibility

Abstract: Drawing on social information processing theory, the current study investigates the relationship between top management team (TMT) responsible leadership and employee organizational citizenship behavior for the environment (OCBE) from a vertical perspective, and whether green human resource management (GHRM) and employee environmental felt-responsibility can play a sequential mediating role between them. Totally, 102 middle-level managers and 527 employees in 102 Chinese teams voluntarily participated in our study. Drawing on above data, our study verifies that TMT responsible leadership was positively associated with both GHRM and employee environmental felt-responsibility. In addition, GHRM mediated the positive effects of TMT responsible leadership and employee environmental felt-responsibility. Also, GHRM can further promote employee OCBE through employee environmental felt-responsibility. Overall, the positive relationship between TMT responsible leadership and employee OCBE was sequentially mediated by GHRM and employee environmental felt-responsibility. Therefore, the current study shows the way to achieve corporate environmental sustainability strategy.

Quantitative Chemoproteomic Profiling with Data-Independent Acquisition-Based Mass Spectrometry.

Abstract: Activity-based protein profiling (ABPP) has emerged as a powerful and versatile tool to enable annotation of protein functions and discovery of targets of bioactive ligands in complex biological systems. It utilizes chemical probes to covalently label functional sites in proteins so that they can be enriched for mass spectrometry (MS)-based quantitative proteomics analysis. However, the semistochastic nature of data-dependent acquisition and high cost associated with isotopically encoded quantification reagents compromise the power of ABPP in multidimensional analysis and high-throughput screening, when a large number of samples need to be quantified in parallel. Here, we combine the data-independent acquisition (DIA) MS with ABPP to develop an efficient label-free quantitative chemical proteomic method, DIA-ABPP, with good reproducibility and high accuracy for high-throughput quantification. We demonstrated the power of DIA-ABPP for comprehensive profiling of functional cysteineome in three distinct applications, including dose-dependent quantification of cysteines' sensitivity toward a reactive metabolite, screening of ligandable cysteines with a covalent fragment library, and profiling of cysteinome fluctuation in circadian clock cycles. DIA-ABPP will open new opportunities for in-depth and multidimensional profiling of functional proteomes and interactions with bioactive small molecules in complex biological systems.

Intelligent Reflecting Surface-Aided Visible Light Communications: Potentials and Challenges

Abstract: To satisfy the explosive growing traffic demands in wireless communications, visible light communication (VLC) performs as a promising technology due to its broad and license-free bandwidth. However, practical VLC systems usually confront challenges, such as blockage and high path loss. Different from traditional VLC techniques, intelligent reflecting surface (IRS)-aided VLC can achieve a remarkable performance gain.

Liouvillian skin effect in an exactly solvable model

Abstract: The interplay between dissipation, topology and sensitivity to boundary conditions has recently attracted tremendous amounts of attention at the level of effective non-Hermitian descriptions. Here we exactly solve a quantum mechanical Lindblad master equation describing a dissipative topological Su-Schrieffer-Heeger (SSH) chain of fermions for both open boundary condition (OBC) and periodic boundary condition (PBC). We find that the extreme sensitivity on the boundary conditions associated with the non-Hermitian skin effect is directly reflected in the rapidities governing the time evolution of the density matrix giving rise to a Liouvillian skin effect . This leads to several intriguing phenomena including boundary sensitive damping behavior, steady state currents in finite periodic systems, and diverging relaxation times in the limit of large systems. We illuminate how the role of topology in these systems differs in the effective non-Hermitian Hamiltonian limit and the full master equation framework.

UniVIP: A Unified Framework for Self-Supervised Visual Pre-training

Abstract: Self-supervised learning (SSL) holds promise in leveraging large amounts of unlabeled data. However, the success of popular SSL methods has limited on single-centric-object images like those in ImageNet and ignores the correlation among the scene and instances, as well as the semantic difference of instances in the scene. To address the above problems, we propose a Unified Self-supervised Visual Pre-training (UniVIP), a novel self-supervised framework to learn versatile visual representations on either single-centric-object or non-iconic dataset. The framework takes into account the representation learning at three levels: 1) the similarity of scene-scene, 2) the correlation of scene-instance, 3) the discrimination of instance-instance. During the learning, we adopt the optimal transport algorithm to automatically measure the discrimination of instances. Massive experiments show that Uni-VIP pre-trained on non-iconic COCO achieves state-of-the-art transfer performance on a variety of downstream tasks, such as image classification, semi-supervised learning, object detection and segmentation. Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2.5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential.

Mechanically Induced Highly Efficient Hydrogen Evolution from Water over Piezoelectric SnSe nanosheets.

Abstract: Piezoelectric nanomaterials open new avenues in driving green catalysis processes (e.g., H2 evolution from water) through harvesting mechanical energy, but their catalytic efficiency is still limited. The predicted enormous piezoelectricity for 2D SnSe, together with its high charge mobility and excellent flexibility, renders it an ideal candidate for stimulating piezocatalysis redox reactions. In this work, few-layer piezoelectric SnSe nanosheets (NSs) are utilized for mechanically induced H2 evolution from water. The finite elemental method simulation demonstrates an unprecedent maximal piezoelectric potential of 44.1 V for a single SnSe NS under a pressure of 100 MPa. A record-breaking piezocurrent density of 0.3 mA cm-2 is obtained for SnSe NSs-based electrode under ultrasonic excitation (100 W, 45 kHz), which is about three orders of magnitude greater than that of reported piezocatalysts. Moreover, an exceptional H2 production rate of 948.4 µmol g-1 h-1 is achieved over the SnSe NSs without any cocatalyst, far exceeding most of the reported piezocatalysts and competitive with the current photocatalysis technology. The findings not only enrich the potential piezocatalysis materials, but also provide useful guidance toward high-efficiency mechanically driven chemical reactions such as H2 evolution from water.

Kindlin-2 haploinsufficiency protects against fatty liver by targeting Foxo1 in mice

Abstract: Nonalcoholic fatty liver disease (NAFLD) affects a large population with incompletely defined mechanism(s). Here we report that Kindlin-2 is dramatically up-regulated in livers in obese mice and patients with NAFLD. Kindlin-2 haploinsufficiency in hepatocytes ameliorates high-fat diet (HFD)-induced NAFLD and glucose intolerance without affecting energy metabolism in mice. In contrast, Kindlin-2 overexpression in liver exacerbates NAFLD and promotes lipid metabolism disorder and inflammation in hepatocytes. A C-terminal region (aa 570-680) of Kindlin-2 binds to and stabilizes Foxo1 by inhibiting its ubiquitination and degradation through the Skp2 E3 ligase. Kindlin-2 deficiency increases Foxo1 phosphorylation at Ser256, which favors its ubiquitination by Skp2. Thus, Kindllin-2 loss down-regulates Foxo1 protein in hepatocytes. Foxo1 overexpression in liver abrogates the ameliorating effect of Kindlin-2 haploinsufficiency on NAFLD in mice. Finally, AAV8-mediated shRNA knockdown of Kindlin-2 in liver alleviates NAFLD in obese mice. Collectively, we demonstrate that Kindlin-2 insufficiency protects against fatty liver by promoting Foxo1 degradation.

Crystal Structure, Solubility, and Pharmacokinetic Study on a Hesperetin Cocrystal with Piperine as Coformer

Abstract: Hesperetin (HES) is a key biological active ingredient in citrus peels, and is one of the natural flavonoids that attract the attention of researchers due to its numerous therapeutic bioactivities that have been identified in vitro. As a bioenhancer, piperine (PIP) can effectively improve the absorption of insoluble drugs in vivo. In the present study, a cocrystal of HES and PIP was successfully obtained through solution crystallization. The single-crystal structure was illustrated and comprehensive characterization of the cocrystal was conducted. The cocrystal was formed by two drug molecules at a molar ratio of 1:1, which contained O–H–O hydrogen bonds between the carbonyl and ether oxygen of PIP and the phenolic hydroxyl group of HES. In addition, a solubility experiment was performed on powder cocrystal in simulated gastrointestinal fluid, and the result revealed that the cocrystal improves the dissolution behavior of HES compared with that of the pure substance. Furthermore, HES’s bioavailability in the cocrystal was six times higher than that of pristine drugs. These results may provide an efficient oral formulation for HES.

SC2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration

Abstract: In this paper, we present a second order spatial compat-ibility (SC2) measure based method for efficient and robust point cloud registration (PCR), called SC2-PCR 1. Firstly, we propose a second order spatial compatibility (SC2) mea-sure to compute the similarity between correspondences. It considers the global compatibility instead of local consis-tency, allowing for more distinctive clustering between in-liers and outliers at early stage. Based on this measure, our registration pipeline employs a global spectral technique to find some reliable seeds from the initial correspondences. Then we design a two-stage strategy to expand each seed to a consensus set based on the SC2 measure matrix. Finally, we feed each consensus set to a weighted SVD algorithm to generate a candidate rigid transformation and select the best model as the final result. Our method can guarantee to find a certain number of outlier-free consensus sets using fewer samplings, making the model estimation more ef-ficient and robust. In addition, the proposed SC2 measure is general and can be easily plugged into deep learning based frameworks. Extensive experiments are carried out to in-vestigate the performance of our method.