CLCF1 signaling restrains thermogenesis and disrupts metabolic homeostasis by inhibiting mitochondrial biogenesis in brown adipocytes.

Great progress has been made in identifying positive regulators that activate adipocyte thermogenesis, but negative regulatory signaling of thermogenesis remains poorly understood. Here, we found that cardiotrophin-like cytokine factor 1 (CLCF1) signaling led to loss of brown fat identity, which impaired thermogenic capacity. CLCF1 levels decreased during thermogenic stimulation but were considerably increased in obesity. Adipocyte-specific CLCF1 transgenic (CLCF1-ATG) mice showed impaired energy expenditure and severe cold intolerance. Elevated CLCF1 triggered whitening of brown adipose tissue by suppressing mitochondrial biogenesis. Mechanistically, CLCF1 bound and activated ciliary neurotrophic factor receptor (CNTFR) and augmented signal transducer and activator of transcription 3 (STAT3) signaling. STAT3 transcriptionally inhibited both peroxisome proliferator-activated receptor-γ coactivator (PGC) 1α and 1ß, which thereafter restrained mitochondrial biogenesis in adipocytes. Inhibition of CNTFR or STAT3 could diminish the inhibitory effects of CLCF1 on mitochondrial biogenesis and thermogenesis. As a result, CLCF1-TG mice were predisposed to develop metabolic dysfunction even without external metabolic stress. Our findings revealed a brake signal on nonshivering thermogenesis and suggested that targeting this pathway could be used to restore brown fat activity and systemic metabolic homeostasis in obesity.

A recyclable Eu3+-functionalized dual-emissive metal-organic framework for portable, rapid detection and efficient removal of malachite green.

A europium-functionalized, dual-emissive, metal-organic framework-based fluorescence sensor (EuUCNDA) was constructed via post-synthetic modification of an UiO-66-type precursor through coordination interactions. EuUCNDA exhibited extremely high selectivity and sensitivity for malachite green (MG) with a low detection limit of 13.01 nM, a wide linear concentration range (0.05-50 µM), excellent anti-interference properties, a rapid response (<1 min), and the possibility of recycling. The good sensing performance of EuUCNDA enables the practical detection of MG in fish pond water and grass carp with good recoveries. Moreover, EuUCNDA can be reused for sensing MG and over 90% of fluorescence intensity can be restored after 7 cycles. Furthermore, EuUCNDA-embedded paper-based sensors combined with smartphone imaging afford portable and visual monitoring of MG in real samples. Notably, besides good sensing performance, EuUCNDA could efficiently remove MG from water. Hence, this work provides a recyclable and sensitive fluorescence sensor for portable, visual, rapid detection and efficient removal of MG.

All Resistive Pressure-Temperature Bimodal Sensing E-Skin for Object Classification.

Electronic skin (E-skin) with multimodal sensing ability demonstrates huge prospects in object classification by intelligent robots. However, realizing the object classification capability of E-skin faces severe challenges in multiple types of output signals. Herein, a hierarchical pressure-temperature bimodal sensing E-skin based on all resistive output signals is developed for accurate object classification, which consists of laser-induced graphene/silicone rubber (LIG/SR) pressure sensing layer and NiO temperature sensing layer. The highly conductive LIG is employed as pressure-sensitive material as well as the interdigital electrode. Benefiting from high conductivity of LIG, pressure perception exhibits an excellent sensitivity of -34.15 kPa-1 . Meanwhile, a high temperature coefficient of resistance of -3.84%°C-1 is obtained in the range of 24-40 °C. More importantly, based on only electrical resistance as the output signal, the bimodal sensing E-skin with negligible crosstalk can simultaneously achieve pressure and temperature perception. Furthermore, a smart glove based on this E-skin enables classifying various objects with different shapes, sizes, and surface temperatures, which achieves over 92% accuracy under assistance of deep learning. Consequently, the hierarchical pressure-temperature bimodal sensing E-skin demonstrates potential application in human-machine interfaces, intelligent robots, and smart prosthetics.

A Digital-Analog Bimodal Memristor Based on CsPbBr3 for Tactile Sensory Neuromorphic Computing.

Memristor with digital and analog bipolar bimodal resistive switching offers a promising opportunity for the information-processing component. However, it still remains a huge challenge that the memristor enables bimodal digital and analog types and fabrication of artificial sensory neural network system. Here, a proposed CsPbBr3 -based memristor demonstrates a high ON/OFF ratio (>103 ), long retention (>104 s), stable endurance (100 cycles), and multilevel resistance memory, which acts as an artificial synapse to realize fundamental biological synaptic functions and neuromorphic computing based on controllable resistance modulation. Moreover, a 5 × 5 spinosum-structured piezoresistive sensor array (sensitivity of 22.4 kPa-1 , durability of 1.5 × 104 cycles, and fast response time of 2.43 ms) is constructed as a tactile sensory receptor to transform mechanical stimuli into electrical signals, which can be further processed by the CsPbBr3 -based memristor with synaptic plasticity. More importantly, this artificial sensory neural network system combined the artificial synapse with 5 × 5 tactile sensing array based on piezoresistive sensors can recognize the handwritten patterns of different letters with high accuracy of 94.44% under assistance of supervised learning. Consequently, the digital-analog bimodal memristor would demonstrate potential application in human-machine interaction, prosthetics, and artificial intelligence.

The Role of Epithelial-Mesenchymal Transition in Chronic Rhinosinusitis.

BACKGROUND: Chronic rhinosinusitis (CRS) is a common condition in otorhinolaryngology. It is characterized by chronic inflammation of the nasal cavity and the sinus mucosa. However, its specific pathogenesis remains unclear. Epithelial dysfunction is closely related to inflammatory airway diseases. Various evidences support that epithelial-mesenchymal transition (EMT) plays a key role in the development of CRS. OBJECTIVE: The study aimed to explore our understanding of how EMT contributes to the pathogenesis of CRS and to examine the role of several signaling pathways in EMT. METHODS: PubMed database was used to review the literature related to EMT in CRS pathogenesis. The following key words were used for the search strategy: CRS, sinusitis, nasal polyps, epithelial cells, EMT, dysfunction, cytokines, signaling pathways, pathogenesis, and therapy. RESULTS: EMT is widely present in the nasal mucosa of CRSwNP patients and contributes to the pathogenesis of the disease. However, there is no sufficient evidence for the existence of EMT in CRSsNP. Multiple signaling pathways and molecules, such as transforming growth factor-ß signaling, Wnt signaling, and hypoxia-inducible factor-1α signaling, have been found to be involved in the EMT process and promote CRSwNP. CONCLUSION: EMT is closely associated with CRS pathogenesis. Our study supports further research on epithelial EMT changes in CRS patients and provides a basis for revealing its pathogenesis and exploring new treatments.

Optimal Diffusion Gradient Encoding Scheme for Diffusion Tensor Imaging Based on Golden Ratio.

BACKGROUND: The accuracy of the estimated diffusion tensor elements can be improved by using a well-chosen magnetic resonance imaging (MRI) diffusion gradient encoding scheme (DGES). Conversely, diffusion tensor imaging (DTI) is typically challenged by the subject's motion during data acquisition and results in corrupted image data. PURPOSE: To identify a reliable DGES based on the golden ratio (GR) that can generate an arbitrary number of uniformly distributed directions to precisely estimate the DTI parameters of partially acquired datasets owing to subject motion. STUDY TYPE: Prospective. POPULATION: Simulations study; three healthy volunteers. FIELD STRENGTH/SEQUENCE: 3 T/DTI data were obtained using a single-shot echo planar imaging sequence. STATISTICAL TESTS: A paired sample t-test and the Wilcoxon test were used, P < 0.05 was considered statistically significant. ASSESSMENT: Two corrupted scenarios A and B were considered and evaluated. For the simulation study, the GR DGES and generated subsets were compared with the Jones and spiral DGESs by electric potential (EP) and condition number (CN). For the human study, the specific subsets A and B selected from scenarios A and B were used for MRI to evaluate fractional anisotropic (FA) map. RESULTS: For the simulation study, the EPs of the GR (14034.25 ± 12957.24) DGES were significantly lower than the Jones (15112.81 ± 13926.08) and spiral (14297.49 ± 13232.94) DGESs. CN variations of GR (1.633 ± 0.024) DGES were significantly lower than Jones (1.688 ± 0.119) and spiral (4.387 ± 2.915) DGESs. For the human study, GR (0.008 ± 0.020) DGES performed similarly with Jones (0.008 ± 0.022) DGES and was superior to spiral (0.022 ± 0.054) DGES in the FA map error. DATA CONCLUSION: The GR DGES ensured that directions of the complete sets and subsets were uniform. The GR DGES had lower error propagation sensitivity, which can help image infants or patients who cannot stay still during scanning. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Merging Gold/Copper Catalysis and Copper/Photoredox Catalysis: An Approach to Alkyl Oxazoles from N-Propargylamides.

Here, we report a mild and highly efficient approach to alkyl oxazoles through merging gold/copper catalysis and copper/photoredox catalysis. Various alkyl oxazoles are synthesized from N-propargylamides with alkyl halides in good to excellent yields with wide functional-group compatibility under blue-light irradiation. Significantly, a copper catalyst plays a dual role in this transformation: as a powerful cocatalyst to accelerate protodeauration of vinyl gold intermediates and improve photoredox catalysis.

Real-Time Facial Affective Computing on Mobile Devices.

Convolutional Neural Networks (CNNs) have become one of the state-of-the-art methods for various computer vision and pattern recognition tasks including facial affective computing. Although impressive results have been obtained in facial affective computing using CNNs, the computational complexity of CNNs has also increased significantly. This means high performance hardware is typically indispensable. Most existing CNNs are thus not generalizable enough for mobile devices, where the storage, memory and computational power are limited. In this paper, we focus on the design and implementation of CNNs on mobile devices for real-time facial affective computing tasks. We propose a light-weight CNN architecture which well balances the performance and computational complexity. The experimental results show that the proposed architecture achieves high performance while retaining the low computational complexity compared with state-of-the-art methods. We demonstrate the feasibility of a CNN architecture in terms of speed, memory and storage consumption for mobile devices by implementing a real-time facial affective computing application on an actual mobile device.

Investigation of Endothelial Surface Glycocalyx Components and Ultrastructure by Single Molecule Localization Microscopy: Stochastic Optical Reconstruction Microscopy (STORM).

On the luminal surface of our blood vessels, there is a thin layer called endothelial surface glycocalyx (ESG) which consists of proteoglycans, glycosaminoglycans (GAGs), and glycoproteins. The GAGs in the ESG are heparan sulfate (HS), hyaluronic acid (HA), chondroitin sulfate (CS), and sialic acid (SA). In order to play important roles in regulating vascular functions, such as being a mechanosensor and transducer for the endothelial cells (ECs) to sense the blood flow, a molecular sieve to maintain normal microvessel permeability and a barrier between the circulating cells and endothelial cells forming the vessel wall, the ESG should have an organized structure at the molecular level. Due to the limitations of conventional optical and electrical microscopy, the ultrastructure of ESG, in the order of 10 to 100 nanometers, has not been revealed until recent development of a super resolution fluorescence optical microscope, Stochastic Optical Reconstruction Microscope (STORM), which is one type of single molecule localization microscopy. This short review describes how the STORM can overcome the diffraction barrier in the conventional fluorescence microscopy to identify the chemical components of the ESG at a high spatial resolution. Examples of the organized ultrastructure of the ESG on the in vitro EC monolayer revealed by the Nikon-STORM system are given as well as how its components get lost during the onset of sepsis, a systemic inflammatory syndrome induced by bacterial infection, which demonstrate that this new technique can be applied to discover the structural and molecular mechanisms at nanometer scales in the native cellular environment for the cellular functions under normal and disease conditions.

A simple procedure to improve the surface passivation for single molecule fluorescence studies.

The single-molecule fluorescence technique is becoming a general and mature tool to probe interactions and dynamics of biomolecules with ultra high precision and accuracy. However, nonspecific adsorption of biomolecules to the flow cells remains a major experimental riddle for the study of many complex biological systems, especially those exhibiting low binding affinity and presenting with weakly populated intermediates. Many novel surface passivation methods have been introduced to reduce nonspecific interactions. Here, we present an effective and inexpensive method to significantly reduce nonspecific binding of biomolecules in conventional poly (ethylene glycol) (PEG)-based surface passivation protocols, without additional exogenous effects. In particular, we propose a simple 10 min Tween-20 treatment for the PEG passivated surface, which could further increase the hydrophilicity of the surface and thus promote passivation efficacy by about 5 to 10 times. We anticipate that this new procedure will find broad practical applications and extend the current reaches of single-molecule fluorescence studies.

Artificial Intelligence-Based Facial Palsy Evaluation: A Survey.

Facial palsy evaluation (FPE) aims to assess facial palsy severity of patients, which plays a vital role in facial functional treatment and rehabilitation. The traditional manners of FPE are based on subjective judgment by clinicians, which may ultimately depend on individual experience. Compared with subjective and manual evaluation, objective and automated evaluation using artificial intelligence (AI) has shown great promise in improving traditional manners and recently received significant attention. The motivation of this survey paper is mainly to provide a systemic review that would guide researchers in conducting their future research work and thus make automatic FPE applicable in real-life situations. In this survey, we comprehensively review the state-of-the-art development of AI-based FPE. First, we summarize the general pipeline of FPE systems with the related background introduction. Following this pipeline, we introduce the existing public databases and give the widely used objective evaluation metrics of FPE. In addition, the preprocessing methods in FPE are described. Then, we provide an overview of selected key publications from 2008 and summarize the state-of-the-art methods of FPE that are designed based on AI techniques. Finally, we extensively discuss the current research challenges faced by FPE and provide insights about potential future directions for advancing state-of-the-art research in this field.

Dielectric spectroscopy technology combined with machine learning methods for nondestructive detection of protein content in fresh milk.

To quickly achieve nondestructive detection of protein content in fresh milk, this study utilized a network analyzer and an open coaxial probe to analyze the dielectric spectra of milk samples at 100 frequency points within the 2-20 GHz range, focusing on the dielectric constant ε' and the dielectric loss factor ε''. Feature variables were extracted from the full dielectric spectra using the successive projections algorithm (SPA), uninformative variables elimination (UVE), and the combined UVE-SPA method. These variables were then used to develop partial least squares regression (PLSR), support vector machine (SVM), decision tree (DT), random forest (RF), and least squares boosting (LSBOOST) models for predicting protein content. The results showed that ε' decreased monotonically with increasing frequency, while ε'' increased monotonically. The UVE-SPA method for feature extraction demonstrated superior performance, with the UVE-SPA-PLSR model being the best for predicting milk protein content, achieving the highest RC 2 = 0.998 and RP 2 = 0.989 and the lowest RMSEC = 0.019% and RMSEP = 0.032%. This study provides a theoretical reference for evaluating milk quality and developing intelligent detection equipment for natural milk.

Development of an interpretable machine learning model associated with genetic indicators to identify Yin-deficiency constitution.

BACKGROUND: Traditional Chinese Medicine (TCM) defines constitutions which are relevant to corresponding diseases among people. As one of the common constitutions, Yin-deficiency constitution influences a number of Chinese population in the disease onset. Therefore, accurate Yin-deficiency constitution identification is significant for disease prevention and treatment. METHODS: In this study, we collected participants with Yin-deficiency constitution and balanced constitution, separately. The least absolute shrinkage and selection operator (LASSO) and logistic regression were used to analyze genetic predictors. Four machine learning models for Yin-deficiency constitution classification with multiple combined genetic indicators were integrated to analyze and identify the optimal model and features. The Shapley Additive exPlanations (SHAP) interpretation was developed for model explanation. RESULTS: The results showed that, NFKBIA, BCL2A1 and CCL4 were the most associated genetic indicators with Yin-deficiency constitution. Random forest with three genetic predictors including NFKBIA, BCL2A1 and CCL4 was the optimal model, area under curve (AUC): 0.937 (95% CI 0.844-1.000), sensitivity: 0.870, specificity: 0.900. The SHAP method provided an intuitive explanation of risk leading to individual predictions. CONCLUSION: We constructed a Yin-deficiency constitution classification model based on machine learning and explained it with the SHAP method, providing an objective Yin-deficiency constitution identification system in TCM and the guidance for clinicians.

Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation.

MRI image segmentation is widely used in clinical practice as a prerequisite and a key for diagnosing brain tumors. The quest for an accurate automated segmentation method for brain tumor images, aiming to ease clinical doctors' workload, has gained significant attention as a research focal point. Despite the success of fully supervised methods in brain tumor segmentation, challenges remain. Due to the high cost involved in annotating medical images, the dataset available for training fully supervised methods is very limited. Additionally, medical images are prone to noise and motion artifacts, negatively impacting quality. In this work, we propose MAPSS, a motion-artifact-augmented pseudo-label network for semi-supervised segmentation. Our method combines motion artifact data augmentation with the pseudo-label semi-supervised training framework. We conduct several experiments under different semi-supervised settings on a publicly available dataset BraTS2020 for brain tumor segmentation. The experimental results show that MAPSS achieves accurate brain tumor segmentation with only a small amount of labeled data and maintains robustness in motion-artifact-influenced images. We also assess the generalization performance of MAPSS using the Left Atrium dataset. Our algorithm is of great significance for assisting doctors in formulating treatment plans and improving treatment quality.

Transparent Self-Healing Anti-Freezing Ionogel for Monolayered Triboelectric Nanogenerator and Electromagnetic Energy-Based Touch Panel.

The advent of Internet of Things and artificial intelligence era necessitates the advancement of self-powered electronics. However, prevalent multifunctional electronics still face great challenges in rigid electrodes, stacked layers, and external power sources to restrict the development in flexible electronics. Here, a transparent, self-healing, anti-freezing (TSA) ionogel composed of fluorine-rich ionic liquid and fluorocarbon elastomer, which is engineered for monolayered triboelectric nanogenerators (M-TENG) and electromagnetic energy-based touch panels is developed. Notably, the TSA-ionogel exhibits remarkable features including outstanding transparency (90%), anti-freezing robustness (253 K), impressive stretchability (600%), and repetitive self-healing capacity. The resultant M-TENG achieves a significant output power density (200 mW m-2 ) and sustains operational stability beyond 1 year. Leveraging this remarkable performance, the M-TENG is adeptly harnessed for biomechanical energy harvesting, self-powered control interface, electroluminescent devices, and enabling wireless control over electrical appliances. Furthermore, harnessing Faraday's induction law and exploiting human body's intrinsic antenna properties, the TSA-ionogel seamlessly transforms into an autonomous multifunctional epidermal touch panel. This touch panel offers impeccable input capabilities through word inscription and participation in the Chinese game of Go. Consequently, the TSA-ionogel's innovation holds the potential to reshape the trajectory of next-generation electronics and profoundly revolutionize the paradigm of human-machine interaction.

Poly(Arylene Alkylene)s with Tetrazole Pendants for Alkaline Ion-Solvating Polymer Electrolytes.

Alkaline ion-solvating membranes derived from a tetrazole functionalized poly(arylene alkylene) are prepared, characterized and evaluated as electrode separators in alkaline water electrolysis. The base polymer, poly[[1,1'-biphenyl]-4,4'-diyl(1,1,1-trifluoropropan-2-yl)], is synthesized by superacid catalyzed polyhydroxyalkylation and subsequently functionalized with tetrazole pendants. After equilibration in aqueous KOH, the relatively acidic tetrazole pendants are deprotonated to form the corresponding potassium tetrazolides. The room temperature ion conductivity is found to peak at 19 mS cm-1 in 5 wt. % KOH, and slightly declines with increasing KOH concentration to 13 mS cm-1 in 30 wt. % KOH. Based on an overall assessment of the mechanical properties, conductivity and electrode activity, 30 wt. % KOH is applied for alkaline electrolysis cell tests. Current densities of up to 1000 mA cm-2 were reached with uncatalyzed Ni-foam electrodes at a cell voltage of less than 2.6 V, with improved gas barrier characteristics compared to that of the several times thicker Zirfon separator.

Multifunctional Eu3+-MOF for simultaneous quantification of malachite green and leuco-malachite green and efficient adsorption of malachite green.

Multi-target detection combined with in-situ removal of contaminants is a challenging issue difficult to overcome. Herein, a dual-emissive Eu3+-metal organic framework (Eu3+-MOF) was constructed by pre-functionalization with a blue-emissive ligand and post-functionalization with red-emissive Eu3+ ions using a UiO-66 precursor. The fluorescence of the synthesized Eu3+-MOF is highly selective and sensitive toward malachite green (MG) and its metabolite leuco-malachite green (LMG), which are environmentally persistent and highly toxic to humans. The limit of detection of MG and LMG are 34.20 and 1.98 nM, respectively. Interestingly, the fluorescence of this Eu3+-MOF showed ratiometric but different responsive modes toward MG and LMG, which enabled the simultaneous quantification of MG and LMG. Furthermore, a paper-based sensor combined with the smartphone was fabricated, which facilitated not only the dual-channel detection of MG, but also its portable, visual, rapid, and intelligent determination. Furthermore, the high surface area of MOFs, together with the coordinate bonding interaction, π-π stacking, and electrostatic interaction sites, endows Eu3+-MOF with the efficient ability toward MG removal. This multifunctional Eu3+-MOF can be successfully used for trace detection, simultaneous determination of MG and LMG, as well as efficient removal of MG. Thus, it exhibits bright prospects for widespread applications in the field of food and environmental analysis.

A facile and intelligent detection method for diclazuril based on a stable dual emissive Eu3+-dopped metal-organic framework.

Diclazuril (DIC) is a broad-spectrum anti-coccidiosis drug of the triazine class, widely used in poultry farming. The overuse of DIC may lead to its accumulation in animal bodies, which may enter the food chain and threaten human health. In this work, we fabricated a stable Eu3+-doped UiO-66 fluorescence sensor (EuUHIPA-30) for the sensitive detection of DIC. Among 20 veterinary drugs, the fluorescence of EuUHIPA-30 selectively responds to DIC, with a low detection limit (0.19 µM) and fast response (10 s). EuUHIPA-30 is recyclable and can detect DIC in chicken and eggs with good recoveries. Moreover, a smartphone-integrated paper-based sensor enables the instrument-free, rapid, visual, and intelligent detection of DIC in chickens and eggs. This work provides a promising candidate for practical fluorescent DIC sensing in animal-derived food to promote food safety.

Dual-emissive Eu(III)-functionalized metal-organic frameworks for visual, rapid, and intelligent sensing of albendazole and albendazole sulfoxide in animal-origin food.

Albendazole (ABZ), a benzimidazole-based anthelmintic, is widely used to treat helminth infections. The extensive and improper use of ABZ may cause drug residues in animal-origin food and anthelmintics resistance, which potentially threaten human health. Meanwhile, albendazole sulfoxide (ABZSO), a metabolite of ABZ, also exhibits toxic effects. Therefore, the detection of ABZ and ABZSO in animal-derived food is significantly necessary. Herein, a dual-emission europium fluorescent sensor (EuUHC-30) was rationally designed and constructed. EuUHC-30 exhibits high selectivity and sensitivity towards ABZ and ABZSO with a detection limit of 0.10 and 0.13 µM, respectively. Furthermore, EuUHC-30 was successfully applied for quantification of ABZ and ABZSO in milk and pig kidney, which were verified by HPLC analysis. Moreover, a smartphone-assisted EuUHC-30 fluorescent paper sensor was fabricated for the practical determination of ABZ and ABZSO in real food. Overall, this work provides a visual, rapid, and intelligent method for the detection of ABZ and ABZSO in animal-origin food.

NR2F6 is essential for brown adipocyte differentiation and systemic metabolic homeostasis.

OBJECTIVE: Brown adipose tissue (BAT) development and function are essential for maintaining energy balance. However, the key factors that specifically regulate brown adipogenesis require further identification. Here, we demonstrated that the nuclear receptor subfamily 2 group F member 6 (NR2F6) played a pivotal role in brown adipogenesis and energy homeostasis. METHODS: We examined the differentiation of immortalized brown adipocytes and primary brown adipocytes when NR2F6 were deleted, and explored the mechanism through which NR2F6 regulated adipogenesis using ChIP-qPCR in vitro. Male wild type (WT) and Pdgfra-Cre-mediated deletion of Nr2f6 in preadipocytes (NR2F6-PKO) mice were fed with high fat diet (HFD) for 12 weeks, and adiposity, glucose intolerance, insulin resistance and inflammation were assessed. RESULTS: NR2F6 exhibited abundant expression in BAT, while its expression was minimal in white adipose tissue (WAT). Within BAT, NR2F6 was highly expressed in preadipocytes, experienced a transient increase in the early stage of brown adipocyte differentiation, and significantly decreased in the mature adipocytes. Depletion of NR2F6 in preadipocytes inhibited brown adipogenesis, caused hypertrophy of brown adipocytes, and impaired thermogenic function of BAT, but without affecting WAT development. NR2F6 transcriptionally regulated PPARγ expression to promote adipogenic process in brown adipocytes. Loss of NR2F6 in preadipocytes led to increased susceptibility to diet-induced metabolic disorders. CONCLUSIONS: Our findings unveiled NR2F6 as a novel key regulator of brown adipogenesis, potentially opening up new avenues for maintaining metabolic homeostasis by targeting NR2F6.