ROLES OF ABCG5 ABCG8 CHOLESTEROL TRANSPORTER IN LIPID HOMEOSTASIS

The ABCG5 ABCG8 (G5G8) sterol transporter promotes cholesterol secretion into bile and opposes dietary sterol absorption in the small intestine. An emerging body of literature suggests that G5G8 links sterol flux to various risk factors for metabolic syndrome (MetS) and nonalcoholic fatty liver disease (NAFLD). Therapeutic approaches that accelerate G5G8 activity may augment reverse cholesterol transport (RCT) and provide beneficial effects in the prevention and treatment of cardiovascular and liver disease. Mice lacking leptin (ob/ob) or its receptor (db/db) are obese, insulin resistant in part due to the reduced levels of hepatic G5G8 and biliary cholesterol. The underlying mechanisms responsible for the reduced G5G8 protein expression in these mice may provide a clue to the drug development for this target. My studies show that neither acute leptin replacement nor liver-specific deletion of leptin receptor alters G5G8 abundance or biliary cholesterol. Similarly, hepatic vagotomy has no effect on G5G8 expression. Conversely, expression of the ER chaperone, GRP78, rescues G5G8 in db/db mice. Previous studies suggest an interdependent relationship between liver and intestine for cholesterol elimination. A combination therapy that increases G5G8-mediated biliary cholesterol secretion and simultaneously reduces intestinal absorption is likely to act additively in cholesterol elimination. My studies show that treatment with ursodiol (Urso) increases hepatic G5G8 protein and both biliary and fecal sterols in a dose-dependent manner. Ezetimibe (EZ), a potent inhibitor of intestinal cholesterol absorption, produces an additive and dose-dependent increase in fecal sterol excretion in the presence of Urso. However, the stimulatory effects of both Urso and Urso-EZ are not G5G8-dependent. Beyond increasing G5G8 protein expression and biliary cholesterol secretion, my studies also show that Urso stimulates ileal FGF15 expression in mice. Our data of the stimulated ileal FGF15 expression in LIRKO and reduced hepatic G5G8 protein levels in Atsb KO mice both indicate the previous unrecognized role of FGF15/19 in the regulation of G5G8 and its activity. Indeed, this is subsequently confirmed by our results from the direct test of recombinant human FGF19 on G5G8. Thus, FGF15/19 may provide an alternative strategy in drug development to target G5G8 activity and accelerate cholesterol elimination

Channel Adaptive DL based Joint Source-Channel Coding without A Prior Knowledge

Significant progress has been made in wireless Joint Source-Channel Coding (JSCC) using deep learning techniques. The latest DL-based image JSCC methods have demonstrated exceptional performance across various signal-to-noise ratio (SNR) levels during transmission, while also avoiding cliff effects. However, current channel adaptive JSCC methods rely heavily on channel prior knowledge, which can lead to performance degradation in practical applications due to channel mismatch effects. This paper proposes a novel approach for image transmission, called Channel Blind Joint Source-Channel Coding (CBJSCC). CBJSCC utilizes Deep Learning techniques to achieve exceptional performance across various signal-to-noise ratio (SNR) levels during transmission, without relying on channel prior information. We have designed an Inverted Residual Attention Bottleneck (IRAB) module for the model, which can effectively reduce the number of parameters while expanding the receptive field. In addition, we have incorporated a convolution and self-attention mixed encoding module to establish long-range dependency relationships between channel symbols. Our experiments have shown that CBJSCC outperforms existing channel adaptive DL-based JSCC methods that rely on feedback information. Furthermore, we found that channel estimation does not significantly benefit CBJSCC, which provides insights for the future design of DL-based JSCC methods. The reliability of the proposed method is further demonstrated through an analysis of the model bottleneck and its adaptability to different domains, as shown by our experiments

Banks’ maturity mismatch, financial stability, and macroeconomic dynamics

The average maturity of total bank assets has been rising sharply following the 4-trillion-yuan stimulus package proposed by the Chinese government in 2009. This paper investigates the macroeconomic implications of maturity mismatch problem using the Chinese data over the period 2007Q1–2019Q4. We extend the New-Keynesian DSGE framework from several dimensions: (i) financial frictions between banks and households; (ii) multi-period loan contracts; (iii) dynamic differential reserve requirement as a macroprudential regulation tool. After estimating the model with Chinese data, the simulation results indicate that the sluggish adjustment of financing cost caused by maturity mismatch will attenuate the real sector fluctuation, however, the feedback effects will amplify the responses of the banking sector. Meanwhile, a severe maturity mismatch will dampen the effect of the required reserve rate as a tool to keep financial stability when confronted with productivity shock

Preliminary Study on Air Injection in Annuli to Manage Pressure during Cementing

AbstractAlong with the development of low permeability reservoirs, underbalanced drilling technology is applied more and more widely. During the cementing operation of underbalanced drilling wells, cementing liquid can flow into the reservoir more easily for the absence of the mud cake, which certainly weakens the reservoir protection advantage of underbalanced drilling. Based on the methods of underbalanced drilling and managed pressure drilling, a new method of cement technology, Balanced Pressure Cementing Technology by Air Injection in Annuli, was put forward. The calculation models of the maximum depth of injection point and the maximum start-up pressure were built. Considering the power limitation of the pump, valves of gas lift were introduced and the calculation method of valve location was developed. This technology could effectively control the annulus pressure of wellbore, assure the cementing quality and protect the hydrocarbon reservoir, thus reduces the exploration and development cost

The Effect of Batter Characteristics on Protein-Aided Control of Fat Absorption in Deep-Fried Breaded Fish Nuggets

Soy protein (SP), egg white protein (EP), and whey protein (WP) at 6% w/w were individually incorporated into the batter of a wheat starch (WS) and wheat gluten (WG) blend (11:1 w/w ratio). Moisture adsorption isotherms of WS and proteins and the viscosity, rheological behavior, and calorimetric properties of the batters were measured. Batter-breaded fish nuggets (BBFNs) were fried at 170 °C for 40 s followed by 190 °C for 30 s, and pick-up of BBFNs, thermogravimetric properties of crust, and fat absorption were determined. The moisture absorption capacity was the greatest for WS, followed by WG, SP, EP, and WP. The addition of SP significantly increased the viscosity and shear moduli (G″, G′) of batter and pick-up of BBFNs, while EP and WP exerted the opposite effect (p \u3c 0.05). SP, EP, and WP raised WS gelatinization and protein denaturation temperatures and crust thermogravimetry temperature, but decreased enthalpy change (ΔH) and oily characteristics of fried BBFNs. These results indicate that hydrophilicity and hydration activity of the added proteins and their interactions with batter matrix starch and gluten reinforced the batter and the thermal stability of crust, thereby inhibiting fat absorption of the BBFNs during deep-fat frying

Open-vocabulary Semantic Segmentation with Frozen Vision-Language Models

When trained at a sufficient scale, self-supervised learning has exhibited a notable ability to solve a wide range of visual or language understanding tasks. In this paper, we investigate simple, yet effective approaches for adapting the pre-trained foundation models to the downstream task of interest, namely, open-vocabulary semantic segmentation. To this end, we make the following contributions: (i) we introduce Fusioner, with a lightweight, transformer-based fusion module, that pairs the frozen visual representation with language concept through a handful of image segmentation data. As a consequence, the model gains the capability of zero-shot transfer to segment novel categories; (ii) without loss of generality, we experiment on a broad range of self-supervised models that have been pre-trained with different schemes, e.g. visual-only models (MoCo v3, DINO), language-only models (BERT), visual-language model (CLIP), and show that, the proposed fusion approach is effective to any pair of visual and language models, even those pre-trained on a corpus of uni-modal data; (iii) we conduct thorough ablation studies to analyze the critical components in our proposed Fusioner, while evaluating on standard benchmarks, e.g. PASCAL-5i and COCO-20i , it surpasses existing state-of-the-art models by a large margin, despite only being trained on frozen visual and language features; (iv) to measure the model's robustness on learning visual-language correspondence, we further evaluate on synthetic dataset, named Mosaic-4, where images are constructed by mosaicking the samples from FSS-1000. Fusioner demonstrates superior performance over previous models.Comment: BMVC 2022 Ora

ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression

Recently, deep learning methods have shown promising results in point cloud compression. For octree-based point cloud compression, previous works show that the information of ancestor nodes and sibling nodes are equally important for predicting current node. However, those works either adopt insufficient context or bring intolerable decoding complexity (e.g. >600s). To address this problem, we propose a sufficient yet efficient context model and design an efficient deep learning codec for point clouds. Specifically, we first propose a window-constrained multi-group coding strategy to exploit the autoregressive context while maintaining decoding efficiency. Then, we propose a dual transformer architecture to utilize the dependency of current node on its ancestors and siblings. We also propose a random-masking pre-train method to enhance our model. Experimental results show that our approach achieves state-of-the-art performance for both lossy and lossless point cloud compression. Moreover, our multi-group coding strategy saves 98% decoding time compared with previous octree-based compression method

Surface Albedo Variation and Its Influencing Factors over Dongkemadi Glacier, Central Tibetan Plateau

Glacier albedo plays a critical role in surface-atmosphere energy exchange, the variability of which influences glacier mass balance as well as water resources. Dongkemadi glacier in central Tibetan Plateau was selected as study area; this research used field measurements to verify Landsat TM-derived albedo and MOD10A1 albedo product and then analyzed the spatiotemporal variability of albedo over the glacier according to them, as well as its influence factors and the relationship with glacier mass balance. The spatial distribution of glacier albedo in winter did not vary with altitude and was determined by terrain shield, whereas, in summer, albedo increased with altitude and was only influenced by terrain shield at accumulation zone. During 2000–2009, albedo in summer decreased at a rate of 0.0052 per year and was influenced by air temperature and precipitation levels, whereas albedo in winter increased at a rate of 0.0045 per year, influenced by the level and frequency of precipitation. The annual variation of albedo in summer during 2000–2012 has the high relative to that of glacier mass balance measurement, which indicates that glacier albedo in the ablation period can be considered as a proxy for glacier mass balance