Showing papers by "Shanghai Jiao Tong University published in 2019"

Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range

Abstract: Improvements in sensor accuracy, greater convenience and ease of use, and expanding reimbursement have led to growing adoption of continuous glucose monitoring (CGM). However, successful utilization of CGM technology in routine clinical practice remains relatively low. This may be due in part to the lack of clear and agreed-upon glycemic targets that both diabetes teams and people with diabetes can work toward. Although unified recommendations for use of key CGM metrics have been established in three separate peer-reviewed articles, formal adoption by diabetes professional organizations and guidance in the practical application of these metrics in clinical practice have been lacking. In February 2019, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address this issue. This article summarizes the ATTD consensus recommendations for relevant aspects of CGM data utilization and reporting among the various diabetes populations.

Simplifying Graph Convolutional Networks

Abstract: Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations. GCNs derive inspiration primarily from recent deep learning approaches, and as a result, may inherit unnecessary complexity and redundant computation. In this paper, we reduce this excess complexity through successively removing nonlinearities and collapsing weight matrices between consecutive layers. We theoretically analyze the resulting linear model and show that it corresponds to a fixed low-pass filter followed by a linear classifier. Notably, our experimental evaluation demonstrates that these simplifications do not negatively impact accuracy in many downstream applications. Moreover, the resulting model scales to larger datasets, is naturally interpretable, and yields up to two orders of magnitude speedup over FastGCN.

Ferroptosis as a target for protection against cardiomyopathy.

Abstract: Heart disease is the leading cause of death worldwide. A key pathogenic factor in the development of lethal heart failure is loss of terminally differentiated cardiomyocytes. However, mechanisms of cardiomyocyte death remain unclear. Here, we discovered and demonstrated that ferroptosis, a programmed iron-dependent cell death, as a mechanism in murine models of doxorubicin (DOX)- and ischemia/reperfusion (I/R)-induced cardiomyopathy. In canonical apoptosis and/or necroptosis-defective Ripk3−/−, Mlkl−/−, or Fadd−/−Mlkl−/− mice, DOX-treated cardiomyocytes showed features of typical ferroptotic cell death. Consistently, compared with dexrazoxane, the only FDA-approved drug for treating DOX-induced cardiotoxicity, inhibition of ferroptosis by ferrostatin-1 significantly reduced DOX cardiomyopathy. RNA-sequencing results revealed that heme oxygenase-1 (Hmox1) was significantly up-regulated in DOX-treated murine hearts. Administering DOX to mice induced cardiomyopathy with a rapid, systemic accumulation of nonheme iron via heme degradation by Nrf2-mediated up-regulation of Hmox1, which effect was abolished in Nrf2-deficent mice. Conversely, zinc protoporphyrin IX, an Hmox1 antagonist, protected the DOX-treated mice, suggesting free iron released on heme degradation is necessary and sufficient to induce cardiac injury. Given that ferroptosis is driven by damage to lipid membranes, we further investigated and found that excess free iron accumulated in mitochondria and caused lipid peroxidation on its membrane. Mitochondria-targeted antioxidant MitoTEMPO significantly rescued DOX cardiomyopathy, supporting oxidative damage of mitochondria as a major mechanism in ferroptosis-induced heart damage. Importantly, ferrostatin-1 and iron chelation also ameliorated heart failure induced by both acute and chronic I/R in mice. These findings highlight that targeting ferroptosis serves as a cardioprotective strategy for cardiomyopathy prevention.

Ambient Particulate Air Pollution and Daily Mortality in 652 Cities

Abstract: BACKGROUND: The systematic evaluation of the results of time-series studies of air pollution is challenged by differences in model specification and publication bias.METHODS: We evaluated the assoc ...

Thermodynamically stabilized β-CsPbI3-based perovskite solar cells with efficiencies >18.

Abstract: Although β-CsPbI3 has a bandgap favorable for application in tandem solar cells, depositing and stabilizing β-CsPbI3 experimentally has remained a challenge. We obtained highly crystalline β-CsPbI3 films with an extended spectral response and enhanced phase stability. Synchrotron-based x-ray scattering revealed the presence of highly oriented β-CsPbI3 grains, and sensitive elemental analyses-including inductively coupled plasma mass spectrometry and time-of-flight secondary ion mass spectrometry-confirmed their all-inorganic composition. We further mitigated the effects of cracks and pinholes in the perovskite layer by surface treating with choline iodide, which increased the charge-carrier lifetime and improved the energy-level alignment between the β-CsPbI3 absorber layer and carrier-selective contacts. The perovskite solar cells made from the treated material have highly reproducible and stable efficiencies reaching 18.4% under 45 ± 5°C ambient conditions.

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

Abstract: A key technical challenge in performing 6D object pose estimation from RGB-D image is to fully leverage the two complementary data sources. Prior works either extract information from the RGB image and depth separately or use costly post-processing steps, limiting their performances in highly cluttered scenes and real-time applications. In this work, we present DenseFusion, a generic framework for estimating 6D pose of a set of known objects from RGB-D images. DenseFusion is a heterogeneous architecture that processes the two data sources individually and uses a novel dense fusion network to extract pixel-wise dense feature embedding, from which the pose is estimated. Furthermore, we integrate an end-to-end iterative pose refinement procedure that further improves the pose estimation while achieving near real-time inference. Our experiments show that our method outperforms state-of-the-art approaches in two datasets, YCB-Video and LineMOD. We also deploy our proposed method to a real robot to grasp and manipulate objects based on the estimated pose.

XAI—Explainable artificial intelligence

Abstract: Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Abstract: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition

Abstract: Action recognition with skeleton data has recently attracted much attention in computer vision. Previous studies are mostly based on fixed skeleton graphs, only capturing local physical dependencies among joints, which may miss implicit joint correlations. To capture richer dependencies, we introduce an encoder-decoder structure, called A-link inference module, to capture action-specific latent dependencies, i.e. actional links, directly from actions. We also extend the existing skeleton graphs to represent higher-order dependencies, i.e. structural links. Combing the two types of links into a generalized skeleton graph, We further propose the actional-structural graph convolution network (AS-GCN), which stacks actional-structural graph convolution and temporal convolution as a basic building block, to learn both spatial and temporal features for action recognition. A future pose prediction head is added in parallel to the recognition head to help capture more detailed action patterns through self-supervision. We validate AS-GCN in action recognition using two skeleton data sets, NTU-RGB+D and Kinetics. The proposed AS-GCN achieves consistently large improvement compared to the state-of-the-art methods. As a side product, AS-GCN also shows promising results for future pose prediction.

The sixth visual object tracking VOT2018 challenge results

Abstract: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).

The role of m 6 A RNA methylation in human cancer

Abstract: N6-methyladenosine (m6A) is identified as the most common, abundant and conserved internal transcriptional modification, especially within eukaryotic messenger RNAs (mRNAs). M6A modification is installed by the m6A methyltransferases (METTL3/14, WTAP, RBM15/15B and KIAA1429, termed as “writers”), reverted by the demethylases (FTO and ALKBH5, termed as “erasers”) and recognized by m6A binding proteins (YTHDF1/2/3, IGF2BP1 and HNRNPA2B1, termed as “readers”). Acumulating evidence shows that, m6A RNA methylation has an outsize effect on RNA production/metabolism and participates in the pathogenesis of multiple diseases including cancers. Until now, the molecular mechanisms underlying m6A RNA methylation in various tumors have not been comprehensively clarified. In this review, we mainly summarize the recent advances in biological function of m6A modifications in human cancer and discuss the potential therapeutic strategies.

Isolated Diatomic Ni-Fe Metal-Nitrogen Sites for Synergistic Electroreduction of CO2.

Abstract: Polynary single-atom structures can combine the advantages of homogeneous and heterogeneous catalysts while providing synergistic functions based on different molecules and their interfaces. However, the fabrication and identification of such an active-site prototype remain elusive. Here we report isolated diatomic Ni-Fe sites anchored on nitrogenated carbon as an efficient electrocatalyst for CO2 reduction. The catalyst exhibits high selectivity with CO Faradaic efficiency above 90 % over a wide potential range from -0.5 to -0.9 V (98 % at -0.7 V), and robust durability, retaining 99 % of its initial selectivity after 30 hours of electrolysis. Density functional theory studies reveal that the neighboring Ni-Fe centers not only function in synergy to decrease the reaction barrier for the formation of COOH* and desorption of CO, but also undergo distinct structural evolution into a CO-adsorbed moiety upon CO2 uptake.

Sodium oligomannate therapeutically remodels gut microbiota and suppresses gut bacterial amino acids-shaped neuroinflammation to inhibit Alzheimer's disease progression.

Abstract: Recently, increasing evidence has suggested the association between gut dysbiosis and Alzheimer’s disease (AD) progression, yet the role of gut microbiota in AD pathogenesis remains obscure. Herein, we provide a potential mechanistic link between gut microbiota dysbiosis and neuroinflammation in AD progression. Using AD mouse models, we discovered that, during AD progression, the alteration of gut microbiota composition leads to the peripheral accumulation of phenylalanine and isoleucine, which stimulates the differentiation and proliferation of pro-inflammatory T helper 1 (Th1) cells. The brain-infiltrated peripheral Th1 immune cells are associated with the M1 microglia activation, contributing to AD-associated neuroinflammation. Importantly, the elevation of phenylalanine and isoleucine concentrations and the increase of Th1 cell frequency in the blood were also observed in two small independent cohorts of patients with mild cognitive impairment (MCI) due to AD. Furthermore, GV-971, a sodium oligomannate that has demonstrated solid and consistent cognition improvement in a phase 3 clinical trial in China, suppresses gut dysbiosis and the associated phenylalanine/isoleucine accumulation, harnesses neuroinflammation and reverses the cognition impairment. Together, our findings highlight the role of gut dysbiosis-promoted neuroinflammation in AD progression and suggest a novel strategy for AD therapy by remodelling the gut microbiota.

Petawatt and exawatt class lasers worldwide

Abstract: In the 2015 review paper 'Petawatt Class Lasers Worldwide' a comprehensive overview of the current status of highpower facilities of >200 TW was presented. This was largely based on facility specifications, with some description of their uses, for instance in fundamental ultra-high-intensity interactions, secondary source generation, and inertial confinement fusion (ICF). With the 2018 Nobel Prize in Physics being awarded to Professors Donna Strickland and Gerard Mourou for the development of the technique of chirped pulse amplification (CPA), which made these lasers possible, we celebrate by providing a comprehensive update of the current status of ultra-high-power lasers and demonstrate how the technology has developed. We are now in the era of multi-petawatt facilities coming online, with 100 PW lasers being proposed and even under construction. In addition to this there is a pull towards development of industrial and multidisciplinary applications, which demands much higher repetition rates, delivering high-average powers with higher efficiencies and the use of alternative wavelengths: mid-IR facilities. So apart from a comprehensive update of the current global status, we want to look at what technologies are to be deployed to get to these new regimes, and some of the critical issues facing their development.

SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects

Abstract: Object detection has been a building block in computer vision. Though considerable progress has been made, there still exist challenges for objects with small size, arbitrary direction, and dense distribution. Apart from natural images, such issues are especially pronounced for aerial images of great importance. This paper presents a novel multi-category rotation detector for small, cluttered and rotated objects, namely SCRDet. Specifically, a sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects. Meanwhile, the supervised pixel attention network and the channel attention network are jointly explored for small and cluttered object detection by suppressing the noise and highlighting the objects feature. For more accurate rotation estimation, the IoU constant factor is added to the smooth L1 loss to address the boundary problem for the rotating bounding box. Extensive experiments on two remote sensing public datasets DOTA, NWPU VHR-10 as well as natural image datasets COCO, VOC2007 and scene text data ICDAR2015 show the state-of-the-art performance of our detector. The code and models will be available at https://github.com/DetectionTeamUCAS.

Symmetric Cross Entropy for Robust Learning With Noisy Labels

Abstract: Training accurate deep neural networks (DNNs) in the presence of noisy labels is an important and challenging task. Though a number of approaches have been proposed for learning with noisy labels, many open issues remain. In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes (``easy" classes), but more surprisingly, it also suffers from significant under learning on some other classes (``hard" classes). Intuitively, CE requires an extra term to facilitate learning of hard classes, and more importantly, this term should be noise tolerant, so as to avoid overfitting to noisy labels. Inspired by the symmetric KL-divergence, we propose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy (RCE). Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels. We provide a theoretical analysis of SL and also empirically show, on a range of benchmark and real-world datasets, that SL outperforms state-of-the-art methods. We also show that SL can be easily incorporated into existing methods in order to further enhance their performance.

FCC-ee: The Lepton Collider: Future Circular Collider Conceptual Design Report Volume 2

Abstract: In response to the 2013 Update of the European Strategy for Particle Physics, the Future Circular Collider (FCC) study was launched, as an international collaboration hosted by CERN. This study covers a highest-luminosity high-energy lepton collider (FCC-ee) and an energy-frontier hadron collider (FCC-hh), which could, successively, be installed in the same 100 km tunnel. The scientific capabilities of the integrated FCC programme would serve the worldwide community throughout the 21st century. The FCC study also investigates an LHC energy upgrade, using FCC-hh technology. This document constitutes the second volume of the FCC Conceptual Design Report, devoted to the electron-positron collider FCC-ee. After summarizing the physics discovery opportunities, it presents the accelerator design, performance reach, a staged operation scenario, the underlying technologies, civil engineering, technical infrastructure, and an implementation plan. FCC-ee can be built with today’s technology. Most of the FCC-ee infrastructure could be reused for FCC-hh. Combining concepts from past and present lepton colliders and adding a few novel elements, the FCC-ee design promises outstandingly high luminosity. This will make the FCC-ee a unique precision instrument to study the heaviest known particles (Z, W and H bosons and the top quark), offering great direct and indirect sensitivity to new physics.

Cytosine base editor generates substantial off-target single-nucleotide variants in mouse embryos.

Abstract: Genome editing holds promise for correcting pathogenic mutations. However, it is difficult to determine off-target effects of editing due to single-nucleotide polymorphism in individuals. Here we developed a method named GOTI (genome-wide off-target analysis by two-cell embryo injection) to detect off-target mutations by editing one blastomere of two-cell mouse embryos using either CRISPR-Cas9 or base editors. Comparison of the whole-genome sequences of progeny cells of edited and nonedited blastomeres at embryonic day 14.5 showed that off-target single-nucleotide variants (SNVs) were rare in embryos edited by CRISPR-Cas9 or adenine base editor, with a frequency close to the spontaneous mutation rate. By contrast, cytosine base editing induced SNVs at more than 20-fold higher frequencies, requiring a solution to address its fidelity.

Surface strategies for catalytic CO2 reduction: from two-dimensional materials to nanoclusters to single atoms

Abstract: Redox catalysis, including photocatalysis and (photo)electrocatalysis, may alleviate global warming and energy crises by removing excess CO2 from the atmosphere and converting it to value-added resources. Nano-to-atomic two-dimensional (2D) materials, clusters and single atoms are superior catalysts because of their engineerable ultrathin/small dimensions and large surface areas and have attracted worldwide research interest. Given the current gap between research and applications in CO2 reduction, our review systematically and constructively discusses nano-to-atomic surface strategies for catalysts reported to date. This work is expected to drive and benefit future research to rationally design surface strategies with multi-parameter synergistic impacts on the selectivity, activity and stability of next-generation CO2 reduction catalysts, thus opening new avenues for sustainable solutions to climate change, energy and environmental issues, and the potential industrial economy.

Identifying Stable Patterns over Time for Emotion Recognition from EEG

Abstract: In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. We systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset called SEED for this study. Discriminative Graph regularized Extreme Learning Machine with differential entropy features achieves the best average accuracies of 69.67 and 91.07 percent on the DEAP and SEED datasets, respectively. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotions than negative emotions in beta and gamma bands; the neural patterns of neutral emotions have higher alpha responses at parietal and occipital sites; and for negative emotions, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition models shows that the neural patterns are relatively stable within and between sessions.

Evidence of a gate-tunable Mott insulator in a trilayer graphene moiré superlattice

Abstract: The Mott insulator is a central concept in strongly correlated physics and manifests when the repulsive Coulomb interaction between electrons dominates over their kinetic energy1,2. Doping additional carriers into a Mott insulator can give rise to other correlated phenomena such as unusual magnetism and even high-temperature superconductivity2,3. A tunable Mott insulator, where the competition between the Coulomb interaction and the kinetic energy can be varied in situ, can provide an invaluable model system for the study of Mott physics. Here we report the possible realization of such a tunable Mott insulator in a trilayer graphene heterostructure with a moire superlattice. The combination of the cubic energy dispersion in ABC-stacked trilayer graphene4–8 and the narrow electronic minibands induced by the moire potential9–15 leads to the observation of insulating states at the predicted band fillings for the Mott insulator. Moreover, the insulating states in the heterostructure can be tuned: the bandgap can be modulated by a vertical electrical field, and at the same time the electron doping can be modified by a gate to fill the band from one insulating state to another. This opens up exciting opportunities to explore strongly correlated phenomena in two-dimensional moire superlattice heterostructures. Report of the likely observation of a Mott insulator in trilayer graphene with a moire potential. The Mott state can be tuned between different filling fractions via gating, which will enable the careful study of this paradigmatic many-body state.

Knowledge Graph Convolutional Networks for Recommender Systems

Abstract: To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information. In general, the attributes are not isolated but connected with each other, which forms a knowledge graph (KG). In this paper, we propose Knowledge Graph Convolutional Networks (KGCN), an end-to-end framework that captures inter-item relatedness effectively by mining their associated attributes on the KG. To automatically discover both high-order structure information and semantic information of the KG, we sample from the neighbors for each entity in the KG as their receptive field, then combine neighborhood information with bias when calculating the representation of a given entity. The receptive field can be extended to multiple hops away to model high-order proximity information and capture users' potential long-distance interests. Moreover, we implement the proposed KGCN in a minibatch fashion, which enables our model to operate on large datasets and KGs. We apply the proposed model to three datasets about movie, book, and music recommendation, and experiment results demonstrate that our approach outperforms strong recommender baselines.

Bioactive compounds and bioactivities of ginger (zingiber officinale roscoe)

Abstract: Ginger (Zingiber officinale Roscoe) is a common and widely used spice. It is rich in various chemical constituents, including phenolic compounds, terpenes, polysaccharides, lipids, organic acids, and raw fibers. The health benefits of ginger are mainly attributed to its phenolic compounds, such as gingerols and shogaols. Accumulated investigations have demonstrated that ginger possesses multiple biological activities, including antioxidant, anti-inflammatory, antimicrobial, anticancer, neuroprotective, cardiovascular protective, respiratory protective, antiobesity, antidiabetic, antinausea, and antiemetic activities. In this review, we summarize current knowledge about the bioactive compounds and bioactivities of ginger, and the mechanisms of action are also discussed. We hope that this updated review paper will attract more attention to ginger and its further applications, including its potential to be developed into functional foods or nutraceuticals for the prevention and management of chronic diseases.

Skin-integrated wireless haptic interfaces for virtual and augmented reality.

Abstract: Traditional technologies for virtual reality (VR) and augmented reality (AR) create human experiences through visual and auditory stimuli that replicate sensations associated with the physical world. The most widespread VR and AR systems use head-mounted displays, accelerometers and loudspeakers as the basis for three-dimensional, computer-generated environments that can exist in isolation or as overlays on actual scenery. In comparison to the eyes and the ears, the skin is a relatively underexplored sensory interface for VR and AR technology that could, nevertheless, greatly enhance experiences at a qualitative level, with direct relevance in areas such as communications, entertainment and medicine1,2. Here we present a wireless, battery-free platform of electronic systems and haptic (that is, touch-based) interfaces capable of softly laminating onto the curved surfaces of the skin to communicate information via spatio-temporally programmable patterns of localized mechanical vibrations. We describe the materials, device structures, power delivery strategies and communication schemes that serve as the foundations for such platforms. The resulting technology creates many opportunities for use where the skin provides an electronically programmable communication and sensory input channel to the body, as demonstrated through applications in social media and personal engagement, prosthetic control and feedback, and gaming and entertainment. Interfaces for epidermal virtual reality technology are demonstrated that can communicate by programmable patterns of localized mechanical vibrations.

Signatures of tunable superconductivity in a trilayer graphene moiré superlattice.

Abstract: Understanding the mechanism of high-transition-temperature (high-Tc) superconductivity is a central problem in condensed matter physics. It is often speculated that high-Tc superconductivity arises in a doped Mott insulator1 as described by the Hubbard model2-4. An exact solution of the Hubbard model, however, is extremely challenging owing to the strong electron-electron correlation in Mott insulators. Therefore, it is highly desirable to study a tunable Hubbard system, in which systematic investigations of the unconventional superconductivity and its evolution with the Hubbard parameters can deepen our understanding of the Hubbard model. Here we report signatures of tunable superconductivity in an ABC-trilayer graphene (TLG) and hexagonal boron nitride (hBN) moire superlattice. Unlike in 'magic angle' twisted bilayer graphene, theoretical calculations show that under a vertical displacement field, the ABC-TLG/hBN heterostructure features an isolated flat valence miniband associated with a Hubbard model on a triangular superlattice5,6 where the bandwidth can be tuned continuously with the vertical displacement field. Upon applying such a displacement field we find experimentally that the ABC-TLG/hBN superlattice displays Mott insulating states below 20 kelvin at one-quarter and one-half fillings of the states, corresponding to one and two holes per unit cell, respectively. Upon further cooling, signatures of superconductivity ('domes') emerge below 1 kelvin for the electron- and hole-doped sides of the one-quarter-filling Mott state. The electronic behaviour in the ABC-TLG/hBN superlattice is expected to depend sensitively on the interplay between the electron-electron interaction and the miniband bandwidth. By varying the vertical displacement field, we demonstrate transitions from the candidate superconductor to Mott insulator and metallic phases. Our study shows that ABC-TLG/hBN heterostructures offer attractive model systems in which to explore rich correlated behaviour emerging in the tunable triangular Hubbard model.