Butylparaben induces glycolipid metabolic disorders in mice via disruption of gut microbiota and FXR signaling.

Butylparaben, a common preservative, is widely used in food, pharmaceuticals and personal care products. Epidemiological studies have revealed the close relationship between butylparaben and diabetes; however the mechanisms of action remain unclear. In this study, we administered butylparaben orally to mice and observed that exposure to butylparaben induced glucose intolerance and hyperlipidemia. RNA sequencing results demonstrated that the enrichment of differentially expressed genes was associated with lipid metabolism, bile acid metabolism, and inflammatory response. Western blot results further validated that butylparaben promoted hepatic lipogenesis, inflammation, gluconeogenesis, and insulin resistance through the inhibition of the farnesoid X receptor (FXR) pathway. The FXR agonists alleviated the butylparaben-induced metabolic disorders. Moreover, 16 S rRNA sequencing showed that butylparaben reduced the abundance of Bacteroidetes, S24-7, Lactobacillus, and Streptococcus, and elevated the Firmicutes/Bacteroidetes ratio. The gut microbiota dysbiosis caused by butylparaben led to decreased bile acids (BAs) production and increased inflammatory response, which further induced hepatic glycolipid metabolic disorders. Our results also demonstrated that probiotics attenuated butylparaben-induced disturbances of the gut microbiota and hepatic metabolism. Taken collectively, the findings reveal that butylparaben induced gut microbiota dysbiosis and decreased BAs production, which further inhibited FXR signaling, ultimately contributing to glycolipid metabolic disorders in the liver.

Polystyrene Nanoplastics Induce Lipid Metabolism Disorder by Activating the PERK-ATF4 Signaling Pathway in Mice.

In recent years, the study of microplastics (MPs) and nanoplastics (NPs) and their effects on human health has gained significant attention. The impacts of NPs on lipid metabolism and the specific mechanisms involved remain poorly understood. To address this, we utilized high-throughput sequencing and molecular biology techniques to investigate how endoplasmic reticulum (ER) stress might affect hepatic lipid metabolism in the presence of polystyrene nanoplastics (PS-NPs). Our findings suggest that PS-NPs activate the PERK-ATF4 signaling pathway, which in turn upregulates the expression of genes related to lipid synthesis via the ATF4-PPARγ/SREBP-1 pathway. This activation leads to an abnormal accumulation of lipid droplets in the liver. 4-PBA, a known ER stress inhibitor, was found to mitigate the PS-NPs-induced lipid metabolism disorder. These results demonstrate the hepatotoxic effects of PS-NPs and clarify the mechanisms of abnormal lipid metabolism induced by PS-NPs.

Foodborne Carbon Dots Aggravate High-Fat-Diet-Induced Glucose Homeostasis Imbalance by Disrupting the Gut-Liver Axis.

Foodborne carbon dots (CDs) are generally produced during cooking and exist in food items. Generally, CDs are regarded as nontoxic materials, but several studies have gradually confirmed the cytotoxicity of CDs, such as oxidative stress, reduced cellular activity, apoptosis, etc. However, studies focusing on the health effects of long-term intake of food-borne CDs are scarce, especially in populations susceptible to metabolic disease. In this study, we reported that CDs in self-brewing beer had no effect on glucose metabolism in CHOW-fed mice but exacerbated high-fat-diet (HFD)-induced glucose metabolism disorders via the gut-liver axis. Chronic exposure to foodborne CDs increased fasting glucose levels and exacerbated liver and intestinal barrier damage in HFD-fed mice. The 16s rRNA sequencing analysis revealed that CDs significantly altered the gut microbiota composition and promoted lipopolysaccharide (LPS) synthesis-related KEGG pathways (superpathway of (Kdo)2-lipid A, Kdo transfer to lipid IVA Ill (Chlamydia), lipid IVA biosynthesis, and so on) in HFD-fed mice. Mechanically, CD exposure increased the abundance of Gram-negative bacteria (Proteobacteria and Desulfovibrionaceae), thus producing excessive endotoxin-LPS, and then LPS was transferred by the blood circulation to the liver due to the damaged intestinal barrier. In the liver, LPS promoted TLR4/NF-κB/P38 MAPK signaling, thus enhancing systemic inflammation and exacerbating HFD-induced insulin resistance. However, pretreating mice with antibiotics eliminated these effects, indicating a key role for gut microbiota in CDs exacerbating glucose metabolism disorders in HFD-fed mice. The finding herein provides new insight into the potential health risk of foodborne nanoparticles in susceptible populations by disturbing the gut-liver axis.

Learned digital lens enabled single optics achromatic imaging.

High-quality imaging with reduced optical complexity has been extensively investigated owing to its promising future in academic and industrial research. However, the practical performance of most imaging systems has encountered a bottleneck posed by optics rather than electronics. Here, we propose a digital lens (DL) to compensate for the chromatic aberration induced by physical optical elements, while the residual wavelength-independent degradation is tackled through a self-designed neural network. By transforming physical aberration correction to an algorithm-based computational imaging task, the proposed DL enables our framework to reduce optical complexity and achieve achromatic imaging in the analog domain. Real experiments have been conducted with an off-the-shelf single lens and recovered images show up to 14.62 dB higher peak signal-to-noise ratio (PSNR) than the original chromatic input. Furthermore, we run a comprehensive ablation study to highlight the contribution of embedding the proposed DL, which shows a 4.83 dB PSNR improvement compared with the methods without DL. Technically, the proposed method can be an alternative for future applications that require both simple optics and high-fidelity visualization.

Large depth-of-field computational imaging with multi-spectral and dual-aperture optics.

Large DOF (depth-of-field) with high SNR (signal-noise-ratio) imaging is a crucial technique for applications from security monitoring to medical diagnostics. However, traditional optical design for large DOF requires a reduction in aperture size, and hence with a decrease in light throughput and SNR. In this paper, we report a computational imaging system integrating dual-aperture optics with a physics-informed dual-encoder neural network to realize prominent DOF extension. Boosted by human vision mechanism and optical imaging law, the dual-aperture imaging system is consisted of a small-aperture NIR camera to provide sharp edge and a large-aperture VIS camera to provide faithful color. To solve the imaging inverse problem in NIR-VIS fusion with different apertures, a specific network with parallel double encoders and the multi-scale fusion module is proposed to adaptively extract and learn the useful features, which contributes to preventing color deviation while preserving delicate scene textures. The proposed imaging framework is flexible and can be designed in different protos with varied optical elements for different applications. We provide theory for system design, demonstrate a prototype device, establish a real-scene dataset containing 3000 images, perform elaborate ablation studies and conduct peer comparative experiments. The experimental results demonstrate that our method effectively produces high-fidelity with larger DOF range than input raw images about 3 times. Without complex optical design and strict practical limitations, this novel, intelligent and integratable system is promising for variable vision applications such as smartphone photography, computational measurement, and medical imaging.

Single-shot hyperspectral imaging based on dual attention neural network with multi-modal learning.

Hyperspectral imaging is being extensively investigated owing to its promising future in critical applications such as medical diagnostics, sensing, and surveillance. However, current techniques are complex with multiple alignment-sensitive components and spatiospectral parameters predetermined by manufacturers. In this paper, we demonstrate an end-to-end snapshot hyperspectral imaging technique and build a physics-informed dual attention neural network with multimodal learning. By modeling the 3D spectral cube reconstruction procedure and solving that compressive-imaging inverse problem, the hyperspectral volume can be directly recovered from only one scene RGB image. Spectra features and camera spectral sensitivity are jointly leveraged to retrieve the multiplexed spatiospectral correlations and realize hyperspectral imaging. With the help of integrated attention mechanism, useful information supplied by disparate modal components is adaptively learned and aggregated to make our network flexible for variable imaging systems. Results show that the proposed method is ultra-faster than the traditional scanning method, and 3.4 times more precise than the existing hyperspectral imaging convolutional neural network. We provide theory for network design, demonstrate training process, and present experimental results with high accuracy. Without bulky benchtop setups and strict experimental limitations, this simple and effective method offers great potential for future spectral imaging applications such as pathological digital stain, computational imaging and virtual/augmented reality display, etc.

Scan-free end-to-end new approach for snapshot camera spectral sensitivity estimation.

Spectral sensitivity is largely related to sensor imaging, which has drawn widespread attention in computer vision. Accurate estimation becomes increasingly urgent because manufacturers rarely disclose it. In this Letter, we present a novel, compact, inexpensive, and real-time computational system for snapshot spectral sensitivity estimation. A multi-scale camera based on the multi-scale convolutional neural network is first proposed, to the best of our knowledge, to automatically extract multiplexing features of an input image by multiscale deep learning, which is vital to solving the inverse problem in sensitivity estimation. Our network is flexible and can be designed with different convolutional kernel sizes for a given application. We build a dataset with 10,500 raw images and generate an excellent pre-trained model. Commercial cameras are adopted to test model validity; the results show that our system can achieve estimation accuracy as high as 91.35%. We provide a method for system design, propose a deep learning network, build a dataset, demonstrate training process, and present experimental results with high precision. This simple and effective method provides an accurate approach for precise estimation of spectral sensitivity and is suitable for computational applications such as pathological digital stain, virtual/augmented reality display, and high-quality image acquisition.

CVNet: confidence voting convolutional neural network for camera spectral sensitivity estimation.

Spectral sensitivity, as one of the most important parameters of a digital camera, is playing a key role in many computer vision applications. In this paper, a confidence voting convolutional neural network (CVNet) is proposed to rebuild the spectral sensitivity function, modeled as the sum of weighted basis functions. By evaluating useful information supplied by different image segments, disparate confidence is calculated to automatically learn basis functions' weights, only using one image captured by the object camera. Three types of basis functions are made up and employed in the network, including Fourier basis function (FBF), singular value decomposition basis function (SVDBF), and radial basis function (RBF). Results show that the accuracy of the proposed method with FBF, SVDBF, and RBF is 97.92%, 98.69%, and 99.01%, respectively. We provide theory for network design, build a dataset, demonstrate training process, and present experimental results with high precision. Without bulky benchtop setups and strict experimental limitations, this proposed simple and effective method could be an alternative in the future for spectral sensitivity function estimation.