Transcription Factor Forkhead Box O1 Mediates Transforming Growth Factor-ß1-Induced Apoptosis in Hepatocytes.

Dysregulation of hepatocyte apoptosis is associated with several types of chronic liver diseases. Transforming growth factor-ß1 (TGF-ß1) is a well-known pro-apoptotic factor in the liver, which constitutes a receptor complex composed of TGF-ß receptor I and II, along with transcription factor Smad proteins. As a member of the forkhead box O (Foxo) class of transcription factors, Foxo1 is a predominant regulator of hepatic glucose production and apoptosis. This study investigated the potential relationship between TGF-ß1 signaling and Foxo1 in control of apoptosis in hepatocytes. TGF-ß1 induced hepatocyte apoptosis in a Foxo1-dependent manner in hepatocytes isolated from both wild-type and liver-specific Foxo1 knockout mice. TGF-ß1 activated protein kinase A through TGF-ß receptor I-Smad3, followed by phosphorylation of Foxo1 at Ser273 in promotion of apoptosis in hepatocytes. Moreover, Smad3 overexpression in the liver of mice promoted the levels of phosphorylated Foxo1-S273, total Foxo1, and a Foxo1-target pro-apoptotic gene Bim, which eventually resulted in hepatocyte apoptosis. The study further demonstrated a crucial role of Foxo1-S273 phosphorylation in the pro-apoptotic effect of TGF-ß1 by using hepatocytes isolated from Foxo1-S273A/A knock-in mice, in which the phosphorylation of Foxo1-S273 was disrupted. Taken together, this study established a novel role of TGF-ß1→protein kinase A→Foxo1 signaling cascades in control of hepatocyte survival.

TGF-ß1 Signaling Impairs Metformin Action on Glycemic Control.

Hyperglycemia is a hallmark of type 2 diabetes (T2D). Metformin, the first-line drug used to treat T2D, maintains blood glucose within a normal range by suppressing hepatic glucose production (HGP). However, resistance to metformin treatment is developed in most T2D patients over time. Transforming growth factor beta 1 (TGF-ß1) levels are elevated both in the liver and serum of T2D humans and mice. Here, we found that TGF-ß1 treatment impairs metformin action on suppressing HGP via inhibiting AMPK phosphorylation at Threonine 172 (T172). Hepatic TGF-ß1 deficiency improves metformin action on glycemic control in high fat diet (HFD)-induced obese mice. In our hepatic insulin resistant mouse model (hepatic insulin receptor substrate 1 (IRS1) and IRS2 double knockout (DKO)), metformin action on glycemic control was impaired, which is largely improved by further deletion of hepatic TGF-ß1 (TKObeta1) or hepatic Foxo1 (TKOfoxo1). Moreover, blockade of TGF-ß1 signaling by chemical inhibitor of TGF-ß1 type I receptor LY2157299 improves to metformin sensitivity in mice. Taken together, our current study suggests that hepatic TGF-ß1 signaling impairs metformin action on glycemic control, and suppression of TGF-ß1 signaling could serve as part of combination therapy with metformin for T2D treatment.

Hepatic p38α MAPK controls gluconeogenesis via FOXO1 phosphorylation at S273 during glucagon signalling in mice.

AIMS/HYPOTHESIS: Hyperglucagonaemia-stimulated hepatic glucose production (HGP) contributes to hyperglycaemia during type 2 diabetes. A better understanding of glucagon action is important to enable efficient therapies to be developed for the treatment of diabetes. Here, we aimed to investigate the role of p38 MAPK family members in glucagon-induced HGP and determine the underlying mechanisms by which p38 MAPK regulates glucagon action. METHODS: p38α, ß, γ and δ MAPK siRNAs were transfected into primary hepatocytes, followed by measurement of glucagon-induced HGP. Adeno-associated virus serotype 8 carrying p38α MAPK short hairpin RNA (shRNA) was injected into liver-specific Foxo1 knockout, liver-specific Irs1/Irs2 double knockout and Foxo1S273D knockin mice. Foxo1S273A knockin mice were fed a high-fat diet for 10 weeks. Pyruvate tolerance tests, glucose tolerance tests, glucagon tolerance tests and insulin tolerance tests were carried out in mice, liver gene expression profiles were analysed and serum triglyceride, insulin and cholesterol levels were measured. Phosphorylation of forkhead box protein O1 (FOXO1) by p38α MAPK in vitro was analysed by LC-MS. RESULTS: We found that p38α MAPK, but not the other p38 isoforms, stimulates FOXO1-S273 phosphorylation and increases FOXO1 protein stability, promoting HGP in response to glucagon stimulation. In hepatocytes and mouse models, inhibition of p38α MAPK blocked FOXO1-S273 phosphorylation, decreased FOXO1 levels and significantly impaired glucagon- and fasting-induced HGP. However, the effect of p38α MAPK inhibition on HGP was abolished by FOXO1 deficiency or a Foxo1 point mutation at position 273 from serine to aspartic acid (Foxo1S273D) in both hepatocytes and mice. Moreover, an alanine mutation at position 273 (Foxo1S273A) decreased glucose production, improved glucose tolerance and increased insulin sensitivity in diet-induced obese mice. Finally, we found that glucagon activates p38α through exchange protein activated by cAMP 2 (EPAC2) signalling in hepatocytes. CONCLUSIONS/INTERPRETATION: This study found that p38α MAPK stimulates FOXO1-S273 phosphorylation to mediate the action of glucagon on glucose homeostasis in both health and disease. The glucagon-induced EPAC2-p38α MAPK-pFOXO1-S273 signalling pathway is a potential therapeutic target for the treatment of type 2 diabetes.

Variational Bayesian Algorithms for Maneuvering Target Tracking with Nonlinear Measurements in Sensor Networks.

The variational Bayesian method solves nonlinear estimation problems by iteratively computing the integral of the marginal density. Many researchers have demonstrated the fact its performance depends on the linear approximation in the computation of the variational density in the iteration and the degree of nonlinearity of the underlying scenario. In this paper, two methods for computing the variational density, namely, the natural gradient method and the simultaneous perturbation stochastic method, are used to implement a variational Bayesian Kalman filter for maneuvering target tracking using Doppler measurements. The latter are collected from a set of sensors subject to single-hop network constraints. We propose a distributed fusion variational Bayesian Kalman filter for a networked maneuvering target tracking scenario and both of the evidence lower bound and the posterior Cramér-Rao lower bound of the proposed methods are presented. The simulation results are compared with centralized fusion in terms of posterior Cramér-Rao lower bounds, root-mean-squared errors and the 3σ bound.

A Reversible Chemogenetic Switch for Chimeric Antigen Receptor T†Cells.

As a revolutionary cancer treatment, the chimeric antigen receptor (CAR) T cell therapy suffers from complications such as cytokine release syndromes and T cell exhaustion. Their mitigation desires controllable activation of CAR-T cells that is achievable through regulatory display of CARs. By embedding the hepatitis C virus NS3 protease (HCV-NS3) between the single-chain variable fragment (scFv) and the hinge domain, we showed that the display of anti-CD19 scFv on CAR-T cells was positively correlated to the presence of a clinical HCV-NS3 inhibitor asunaprevir (ASV). This novel CAR design that allows the display of anti-CD19 scFv in the presence of ASV and its removal in the absence of ASV creates a practically reversible chemical switch. We demonstrated that the intact CAR on T cells can be repeatedly turned on and off by controlling the presence of ASV in a dose dependent manner both in vitro and in vivo, which enables delicate modulation of CAR-T activation during cancer treatment.

Polarization-guided road detection network for LWIR division-of-focal-plane camera.

A long-wave infrared polarization imaging technique recently has been applied in full-time road detection. However, the existing heuristic method has the limitation of fully using the polarization information of the road. In this Letter, we propose a polarization-guided road detection network collaborating with the distinguishable polarization characteristics of the road. A two-branch network is proposed to perform accurate road detection with infrared polarization images as inputs. A coarse road map obtained by thresholding the polarization images of the road guides the network to focus on the road regions through a polarization-guided branch. We also design a road-region-aware feature fusion module to fuse the features from two branches. This customized design of the network gives full play to the advantages of deep learning networks and polarization information. Experiments on a public infrared polarization dataset of road scenes demonstrate that the proposed road detection network outperforms state-of-the-art real-time segmentation networks with fewer parameters and faster speed.

A Colorimetric Assay for the Detection of Glucose and H2O2 Based on Cu-Ag/g-C3N4/ZIF Hybrids with Superior Peroxidase Mimetic Activity.

In this work, we report the synthesis of Cu-Ag bimetallic nanopartiles and g-C3N4 nanosheets decorated on zeolitic imidazolate framework-8 (ZIF-8) to form a Cu-Ag/g-C3N4/ZIF hybrid. The hybrid was synthesized and characterized by Transmission electron microscopy (TEM), Fourier transformed infrared (FTIR), the X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The Cu-Ag/g-C3N4/ZIF hybrid has intrinsic peroxidaselike catalytic activity towards the oxidation of TMB in the presence of H2O2. The situ synthesis of Cu-Ag bimetallic nanopartiles on 2D support such as g-C3N4 nanosheets would significantly enhance the peroxidaselike catalytic properties of individual Cu-Ag bimetallic nanopartiles and the g-C3N4 nanosheets. After loading of Cu-Ag bimetallic nanopartiles and g-C3N4 nanosheets on the ZIF-8, the hybrids exhibited superior peroxidaselike catalytic activity and good recyclability. Then, this method was applied for detecting glucose in human serum, owing the significant potential for detection of metabolites with H2O2-generation reactions.

Demosaicking DoFP images using Newton's polynomial interpolation and polarization difference model.

A demand for division of focal plane (DoFP) polarization imaging technology grows rapidly as nanofabrication technologies become mature. For real-time polarization imaging, a DoFP polarimeter often trades off its spatial resolution, which may cause instantaneous field of view (IFoV) errors. To deal with such problems, interpolation methods are often used to fill the missing polarization information. This paper presents an interpolation technique using Newton's polynomial for DoFP polarimeter demosaicking. The interpolation is performed in the polarization difference domain with the interpolation error taken into consideration. The proposed method uses an edge classifier based on polarization difference and a fusion scheme to recover more accurate boundary features. Experiments using both synthetic and real DoFP images in visible and long-wave infrared spectrum demonstrate that the proposed interpolation method outperforms the state-of-the-art techniques quantitatively as well as visually to reduce nonconformities caused by high-frequency energy.

Compact Belief Rule Base Learning for Classification with Evidential Clustering.

The belief rule-based classification system (BRBCS) is a promising technique for addressing different types of uncertainty in complex classification problems, by introducing the belief function theory into the classical fuzzy rule-based classification system. However, in the BRBCS, high numbers of instances and features generally induce a belief rule base (BRB) with large size, which degrades the interpretability of the classification model for big data sets. In this paper, a BRB learning method based on the evidential C-means clustering (ECM) algorithm is proposed to efficiently design a compact belief rule-based classification system (CBRBCS). First, a supervised version of the ECM algorithm is designed by means of weighted product-space clustering to partition the training set with the goals of obtaining both good inter-cluster separability and inner-cluster pureness. Then, a systematic method is developed to construct belief rules based on the obtained credal partitions. Finally, an evidential partition entropy-based optimization procedure is designed to get a compact BRB with a better trade-off between accuracy and interpretability. The key benefit of the proposed CBRBCS is that it can provide a more interpretable classification model on the premise of comparative accuracy. Experiments based on synthetic and real data sets have been conducted to evaluate the classification accuracy and interpretability of the proposal.

Removal of reflections in LWIR image with polarization characteristics.

Long-wave infrared (LWIR) imaging has been successfully used in surveillance applications in low illumination conditions. However, infrared energy reflected from smooth surfaces such as floors and metallic objects may reduce object detection and tracking accuracies. In this paper, we present a novel reflection removal method using polarization properties of the reflection in LWIR imagery. Reflection can be distinguished from the scene by two unique characteristics of polarization: the difference of two orthogonal polarized components (OPC) and the uniformity of angle of polarization (AoP). The OPC difference helps locate the regions of reflection. The uniformity of AoP in the reflection region pose a strong constraint for reflection detection. The proposed joint reflection detection method combines the OPC difference and the uniformity of AoP can detect actual reflection region. Then the closed-form matting method improves the robustness of the method and removes the reflection from the scene. Experiment results demonstrate that the proposed scheme effectively removes the reflection in challenging situations where many existing techniques may fail.

A Novel Energy-Efficient Multi-Sensor Fusion Wake-Up Control Strategy Based on a Biomimetic Infectious-Immune Mechanism for Target Tracking.

A biomimetic distributed infection-immunity model (BDIIM), inspired by the immune mechanism of an infected organism, is proposed in order to achieve a high-efficiency wake-up control strategy based on multi-sensor fusion for target tracking. The resultant BDIIM consists of six sub-processes reflecting the infection-immunity mechanism: occurrence probabilities of direct-infection (DI) and cross-infection (CI), immunity/immune-deficiency of DI and CI, pathogen amount of DI and CI, immune cell production, immune memory, and pathogen accumulation under immunity state. Furthermore, a corresponding relationship between the BDIIM and sensor wake-up control is established to form the collaborative wake-up method. Finally, joint surveillance and target tracking are formulated in the simulation, in which we show that the energy cost and position tracking error are reduced to 50.8% and 78.9%, respectively. Effectiveness of the proposed BDIIM algorithm is shown, and this model is expected to have a significant role in guiding the performance improvement of multi-sensor networks.

Simultaneous Mean and Covariance Correction Filter for Orbit Estimation.

This paper proposes a novel filtering design, from a viewpoint of identification instead of the conventional nonlinear estimation schemes (NESs), to improve the performance of orbit state estimation for a space target. First, a nonlinear perturbation is viewed or modeled as an unknown input (UI) coupled with the orbit state, to avoid the intractable nonlinear perturbation integral (INPI) required by NESs. Then, a simultaneous mean and covariance correction filter (SMCCF), based on a two-stage expectation maximization (EM) framework, is proposed to simply and analytically fit or identify the first two moments (FTM) of the perturbation (viewed as UI), instead of directly computing such the INPI in NESs. Orbit estimation performance is greatly improved by utilizing the fit UI-FTM to simultaneously correct the state estimation and its covariance. Third, depending on whether enough information is mined, SMCCF should outperform existing NESs or the standard identification algorithms (which view the UI as a constant independent of the state and only utilize the identified UI-mean to correct the state estimation, regardless of its covariance), since it further incorporates the useful covariance information in addition to the mean of the UI. Finally, our simulations demonstrate the superior performance of SMCCF via an orbit estimation example.

An Iterative Nonlinear Filter Using Variational Bayesian Optimization.

We propose an iterative nonlinear estimator based on the technique of variational Bayesian optimization. The posterior distribution of the underlying system state is approximated by a solvable variational distribution approached iteratively using evidence lower bound optimization subject to a minimal weighted Kullback-Leibler divergence, where a penalty factor is considered to adjust the step size of the iteration. Based on linearization, the iterative nonlinear filter is derived in a closed-form. The performance of the proposed algorithm is compared with several nonlinear filters in the literature using simulated target tracking examples.

Multi-Sensor Consensus Estimation of State, Sensor Biases and Unknown Input.

This paper addresses the problem of the joint estimation of system state and generalized sensor bias (GSB) under a common unknown input (UI) in the case of bias evolution in a heterogeneous sensor network. First, the equivalent UI-free GSB dynamic model is derived and the local optimal estimates of system state and sensor bias are obtained in each sensor node; Second, based on the state and bias estimates obtained by each node from its neighbors, the UI is estimated via the least-squares method, and then the state estimates are fused via consensus processing; Finally, the multi-sensor bias estimates are further refined based on the consensus estimate of the UI. A numerical example of distributed multi-sensor target tracking is presented to illustrate the proposed filter.

Hybrid particle swarm optimization for hybrid flowshop scheduling problem with maintenance activities.

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm.

A dynamic attitude measurement system based on LINS.

A dynamic attitude measurement system (DAMS) is developed based on a laser inertial navigation system (LINS). Three factors of the dynamic attitude measurement error using LINS are analyzed: dynamic error, time synchronization and phase lag. An optimal coning errors compensation algorithm is used to reduce coning errors, and two-axis wobbling verification experiments are presented in the paper. The tests indicate that the attitude accuracy is improved 2-fold by the algorithm. In order to decrease coning errors further, the attitude updating frequency is improved from 200 Hz to 2000 Hz. At the same time, a novel finite impulse response (FIR) filter with three notches is designed to filter the dither frequency of the ring laser gyro (RLG). The comparison tests suggest that the new filter is five times more effective than the old one. The paper indicates that phase-frequency characteristics of FIR filter and first-order holder of navigation computer constitute the main sources of phase lag in LINS. A formula to calculate the LINS attitude phase lag is introduced in the paper. The expressions of dynamic attitude errors induced by phase lag are derived. The paper proposes a novel synchronization mechanism that is able to simultaneously solve the problems of dynamic test synchronization and phase compensation. A single-axis turntable and a laser interferometer are applied to verify the synchronization mechanism. The experiments results show that the theoretically calculated values of phase lag and attitude error induced by phase lag can both match perfectly with testing data. The block diagram of DAMS and physical photos are presented in the paper. The final experiments demonstrate that the real-time attitude measurement accuracy of DAMS can reach up to 20″ (1σ) and the synchronization error is less than 0.2 ms on the condition of three axes wobbling for 10 min.

Cascaded Aggregation Convolution Network for Salient Grain Pests Detection.

Pest infestation poses significant threats to grain storage due to pests' behaviors of feeding, respiration, excretion, and reproduction. Efficient pest detection and control are essential to mitigate these risks. However, accurate detection of small grain pests remains challenging due to their small size, high variability, low contrast, and cluttered background. Salient pest detection focuses on the visual features that stand out, improving the accuracy of pest identification in complex environments. Drawing inspiration from the rapid pest recognition abilities of humans and birds, we propose a novel Cascaded Aggregation Convolution Network (CACNet) for pest detection and control in stored grain. Our approach aims to improve detection accuracy by employing a reverse cascade feature aggregation network that imitates the visual attention mechanism in humans when observing and focusing on objects of interest. The CACNet uses VGG16 as the backbone network and incorporates two key operations, namely feature enhancement and feature aggregation. These operations merge the high-level semantic information and low-level positional information of salient objects, enabling accurate segmentation of small-scale grain pests. We have curated the GrainPest dataset, comprising 500 images showcasing zero to five or more pests in grains. Leveraging this dataset and the MSRA-B dataset, we validated our method's efficacy, achieving a structure S-measure of 91.9%, and 90.9%, and a weighted F-measure of 76.4%, and 91.0%, respectively. Our approach significantly surpasses the traditional saliency detection methods and other state-of-the-art salient object detection models based on deep learning. This technology shows great potential for pest detection and assessing the severity of pest infestation based on pest density in grain storage facilities. It also holds promise for the prevention and control of pests in agriculture and forestry.

Hepatocyte FoxO1 Deficiency Protects From Liver Fibrosis via Reducing Inflammation and TGF-ß1-mediated HSC Activation.

BACKGROUND & AIMS: The O-class of the forkhead transcription factor FoxO1 is a crucial factor mediating insulin→PI3K→Akt signaling and governs diverse cellular processes. However, the role of hepatocyte FoxO1 in liver fibrosis has not been well-established. In his study, we investigated the role of hepatocyte FoxO1 in liver fibrosis and uncovered the underlying mechanisms. METHODS: Liver fibrosis was established by carbon tetrachloride (CCL4) administration and compared between liver-specific deletion of FoxO1 deletion (F1KO) and control (CNTR) mice. Using genetic and bioinformatic strategies in vitro and in vivo, the role of hepatic FoxO1 in liver fibrosis and associated mechanisms was established. RESULTS: Increased FoxO1 expression and FoxO1 signaling activation were observed in CCL4-induced fibrosis. Hepatic FoxO1 deletion largely attenuated CCL4-induced liver injury and fibrosis compared with CNTR mice. F1KO mice showed ameliorated CCL4-induced hepatic inflammation and decreased TGF-ß1 mRNA and protein levels compared with those of CNTR mice. In primary hepatocytes, FoxO1 deficiency reduced TGF-ß1 expression and secretion. Conditioned medium (CM) collected from wild-type hepatocytes treated with CCL4 activated human HSC cell line (LX-2); such effect was attenuated by FoxO1 deletion in primary hepatocytes or neutralization of TGF-ß1 in the CM using TGF-ß1 antibody. Hepatic FoxO1 overexpression in CNTR mice promoted CCL4-induced HSC activation; such effect was blocked in L-TGF-ß1KO mice. CONCLUSIONS: Hepatic FoxO1 mediates CCL4-inducled liver fibrosis via upregulating hepatocyte TGF-ß1 expression, stimulating hepatic inflammation and TGF-ß1-mediated HSC activation. Hepatic FoxO1 may be a therapeutic target for prevention and treatment of liver fibrosis.

An estrogen receptor α-derived peptide improves glucose homeostasis during obesity.

Estrogen receptor α (ERα) plays a crucial role in regulating glucose and energy homeostasis during type 2 diabetes mellitus (T2DM). However, the underlying mechanisms remain incompletely understood. Here we find a ligand-independent effect of ERα on the regulation of glucose homeostasis. Deficiency of ERα in the liver impairs glucose homeostasis in male, female, and ovariectomized (OVX) female mice. Mechanistic studies reveal that ERα promotes hepatic insulin sensitivity by suppressing ubiquitination-induced IRS1 degradation. The ERα 1-280 domain mediates the ligand-independent effect of ERα on insulin sensitivity. Furthermore, we identify a peptide based on ERα 1-280 domain and find that ERα-derived peptide increases IRS1 stability and enhances insulin sensitivity. Importantly, administration of ERα-derived peptide into obese mice significantly improves glucose homeostasis and serum lipid profiles. These findings pave the way for the therapeutic intervention of T2DM by targeting the ligand-independent effect of ERα and indicate that ERα-derived peptide is a potential insulin sensitizer for the treatment of T2DM.

Long-term exposure to dietary emulsifier Tween 80 promotes liver lipid accumulation and induces different-grade inflammation in young and aged mice.

With the advent of industrialization, there has been a substantial increase in the production and consumption of ultra-processed foods (UPFs). These processed foods often contain artificially synthesized additives, such as emulsifiers. Emulsifiers constitute approximately half of the total amount of food additives, with Tween 80 being a commonly used emulsifier in the food industry. Concurrently, China is undergoing significant demographic changes, transitioning into an aging society. Despite this demographic shift, there is insufficient research on the health implications of food emulsifiers, particularly on the elderly population. In this study, we present novel findings indicating that even at low concentrations, Tween 80 suppressed the viability of multiple cell types. Prolonged in vivo exposure to 1 % Tween 80 in drinking water induced liver lipid accumulation and insulin resistance in young adult mice under a regular chow diet. Intriguingly, in mice with high-fat diet (HFD) induced metabolic dysfunction-associated steatotic liver disease (MASLD), this inductive effect was masked. In aged mice, liver lipid accumulation was replicated under prolonged Tween 80 exposure. We further revealed that Tween 80 induced inflammation in both adult and aged mice, with a more pronounced inflammation in aged mice. In conclusion, our study provides compelling evidence that Tween 80 could contribute to a low-grade inflammation and liver lipid accumulation. These findings underscore the need for increasing attention regarding the consumption of UPFs with Tween 80 as the emulsifier, particularly in the elderly consumers.