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High-fat diet induces lipid metabolism disorders that has become one of the grievous public health problems and imposes a serious economic and social burden worldwide. Safety probiotics isolated from nature are regarded as a novel supplementary strategy for preventing and improving diet-induced lipid metabolism disorders and related chronic diseases. The present review summarized the latest researches of probiotics in high fat diet induced lipid metabolism disorders to provide a critical perspective on the regulatory function of probiotics for future research. Furthermore, the screening criteria and general sources of probiotics with lipid-lowering ability also outlined to enlarge microbial species resource bank instantly, which promoted the development of functional foods with lipid-lowering strains from nature. After critically reviewing the lipid-lowering potential of probiotics both in vitro and in vivo and even in clinical data of humans, we provided a perspective that probiotics activated AMPK signaling pathway to regulate fat synthesis and decomposition, as well as affected positively the gut microbiota structure, intestinal barrier function and systemic inflammatory response, then these beneficial effects are amplified along Gut-liver axis, which regulated intestinal flora metabolites such as SCFAs and BAs by HMGCR/FXR/SHP signaling pathway to improve high fat diet induced lipid metabolism disorders effectively.

With the goal of developing an accurate and fast image reconstruction algorithm for ultrasound computed tomography, we combine elements of model- and data-driven approaches and propose a learned method which addresses the disadvantages of both approaches. We design a deep neural network which accounts for a nonlinear forward operator and primal-dual algorithm by its inherent network architecture. The network is trained end-to-end, with ultrasound pressure field data as input to get directly an optimized reconstruction of speed of sound and attenuation images. The training and test data are based on a set of Optical and Acoustic Breast Phantom Database, where we use the image as ground truth and simulate pressure field data according to our forward model. Extensive experiments show that our method achieves significant improvements over state-of-the-art reconstruction methods in this field. Experiments show that the proposed algorithm improves the measures structural similarity measure (SSIM) from 0.74 to 0.95 and root mean squared error (RMSE) from 0.13 to 0.09 on average concerning the speed of sound reconstruction, while it improves the SSIM from 0.60 to 0.94 and RMSE from 0.24 to 0.10 on average in attenuation reconstruction.

This article investigates a robust guaranteed cost finite-time control for coupled neural networks with parametric uncertainties. The parameter uncertainties are assumed to be time-varying norm bounded, which appears on the system state and input matrices. The robust guaranteed cost control laws presented in this article include both continuous feedback controllers and intermittent feedback controllers, which were rarely found in the literature. The proposed guaranteed cost finite-time control is designed in terms of a set of linear-matrix inequalities (LMIs) to steer the coupled neural networks to achieve finite-time synchronization with an upper bound of a guaranteed cost function. Furthermore, open-loop optimization problems are formulated to minimize the upper bound of the quadratic cost function and convergence time, it can obtain the optimal guaranteed cost periodically intermittent and continuous feedback control parameters. Finally, the proposed guaranteed cost periodically intermittent and continuous feedback control schemes are verified by simulations.

Objective. The image reconstruction of ultrasound computed tomography is computationally expensive with conventional iterative methods. The fully learned direct deep learning reconstruction is promising to speed up image reconstruction significantly. However, for direct reconstruction from measurement data, due to the lack of real labeled data, the neural network is usually trained on a simulation dataset and shows poor performance on real data because of the simulation-to-real gap. Approach. To improve the simulation-to-real generalization of neural networks, a series of strategies are developed including a Fourier-transform-integrated neural network, measurement-domain data augmentation methods, and a self-supervised-learning-based patch-wise preprocessing neural network. Our strategies are evaluated on both the simulation dataset and real measurement datasets from two different prototype machines. Main results. The experimental results show that our deep learning methods help to improve the neural networks’ robustness against noise and the generalizability to real measurement data. Significance. Our methods prove that it is possible for neural networks to achieve superior performance to traditional iterative reconstruction algorithms in imaging quality and allow for real-time 2D-image reconstruction. This study helps pave the path for the application of deep learning methods to practical ultrasound tomography image reconstruction based on simulation datasets.

To improve the energy efficiency of a direct expansion air conditioning (DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The control strategy consists of an open loop optimization controller and a closed-loop guaranteed cost periodically intermittent-switch controller (GCPISC). The error dynamics system of the closed-loop control is modelled based on the GCPISC principle. The difference, compared to the previous DX A/C system control methods, is that the controller designed in this paper performs control at discrete times. For the ease of designing the controller, a series of matrix inequalities are derived to be the sufficient conditions of the lower-layer closed-loop GCPISC controller. In this way, the DX A/C system output is derived to follow the optimal references obtained through the upper-layer open loop controller in exponential time, and the energy efficiency of the system is improved. Moreover, a static optimization problem is addressed for obtaining an optimal GCPISC law to ensure a minimum upper bound on the DX A/C system performance considering energy efficiency and output tracking error. The advantages of the designed hierarchical controller for a DXA/C system with uncertain parameters are demonstrated through some simulation results.

Multi-Label Learning (MLL) is a classification task in which each instance may be associated with two or more class labels simultaneously. With the arrival of information explosion era, MLL is confronted with the curse of high-dimensional data. Feature selection is an effective technique that has been used to reduce dimensionality. Previous Multi-Label Feature Selection (MLFS) approaches only lay emphasis on the fitting relations between the feature space and the corresponding label space while neglecting the instance correlations. To remedy the deficiency, we present a novel MLFS method by Exploring Instance Correlations with Local Discriminant model (EICLD). More specifically, we first construct a local set for each instance, and use a local discriminant model for each local set to explore instance correlations. Then, we further integrate the local models of all instances to guarantee the global instance correlations. Finally, l2,1-norm is introduced into loss function and the regularization of feature weight matrix respectively to facilitate feature selection process. An optimization algorithm is designed for handling the proposed objective function. The paper is compared with seven representative approaches on twelve data sets. The results validate the superiority of the proposed approach.

In multi-label learning, feature selection is an essential preprocessing module, which can be exploited a more compact and precise representation of instances. Most of existing multi-label feature selection methods are either converted into multiple single-label feature selection methods or directly utilize half-baked label information, thus it is difficult for them to obtain a discriminative feature subset across multiple labels. To tackle this problem, we propose multi-label feature selection with local discriminant model and label correlations. First, for each instance, a local clique comprising this instance and its neighboring instances is constructed, and a local discriminant model for each local clique is integrated globally to evaluate the clustering performance of all instances. Second, in terms of clustering results, we explore high-order label correlations to reduce the impact of half-baked label information. Finally, we combine l2,1-norm regularization to design the objective function to achieve multi-label feature selection. Comprehensive experiments are conducted on twelve real-world multi-label data sets, and results demonstrate the effectiveness of the proposed method in comparison with several representative methods.

Cyclophosphamide (CTX) is a widely used anticancer drug that can cause liver injury, but there is no effective treatment available at present. The antioxidant properties of Lactobacillus plantarum Lp2 in vitro and its effect on CTX-induced liver injury in mice were investigated thoroughly. The order of antioxidant capacity of the fermentate of Lp2 was as followed: fermented supernatant cell-free extract intact cell. BALB/c mice were intraperitoneally injected with 80 mg/kg BW/d CTX for 3 days to build a liver injury model, then treated with Lp2 fermented supernatant (Lp2-s) and Lp2 culture broth (Lp2). After 10 days, the indicators of oxidative stress and liver injury were measured. Both Lp2-s and Lp2 restored the levels of T-SOD, CAT, GSH-Px, MDA, GSH, ALT, and AST. The western blotting results showed that Lp2-s and Lp2 ameliorated CTX-induced oxidative damage and hepatocyte apoptosis via inhibiting MAPKs pathway and strengthening Nrf2/HO-1/NQO1 antioxidant defense system, thus inhibiting the mitochondrial-mediated apoptosis pathway. Therefore, both Lp2-s and Lp2 had similar protective effects on CTX-induced liver injury.

The Laplacian eigenmaps (LE) is one of the most commonly used nonlinear dimensionality reduction methods and aims to find a low-dimensional representation to preserve the topological relationship between sample points in the original data. However, the l2-norm based loss function makes LE unable to preserve the relationship in many cases. Additionally, the topological relationship does not represent the real intrinsic structure of data. For example, the overemphasis of the topological relationship by LE easily breaks the manifold structure into multiple local areas in the embedding space, which makes the spectral clustering analysis of multi-manifold data more difficult to carry out. To solve this problem, we propose the soft adaptive loss based LE (SALE). With the soft adaptive loss, SALE can adaptively emphasize the topological relationship between sample points and the clustering structure of data. The model is tested and validated on UCI, face and gene expression data sets, and compared with some state-of-the-art models. The experimental results show that the method is robust to noise.

In this paper, we study the influence of uncertainty in economic policy on the business operations distribution using data from China. In doing so, we rely on the China Economic Policy Uncertainty ...

Review question / Objective: To study on the efficacy and safety of Kunxian Capsule in treating patients with lupus nephritis( LN). Condition being studied: The study showed that Kunxian capsule significantly improved immune status and renal function and suppressed IL-10 and TNF in serum of lupus mice- α Of secretion, works better in combination with and reduces hormone dosage. At present, there are few studies in which Kunxian capsules are used in the clinic, and the conclusions of the studies are inconsistent, so this study was conducted in order to comprehensively evaluate the efficacy and safety of Kunxian capsules for the treatment of lupus nephritis.

Poverty, food insecurity and climate change are global issues facing humanity, threatening social, economic and environmental sustainability. Greenhouse cultivation provides a potential solution to these challenges. However, some greenhouses operate inefficiently and need to be optimized for more economical and cleaner crop production. In this paper, an economic model predictive control (EMPC) method for a greenhouse is proposed. The goal is to manage the energy-water‑carbon-food nexus for cleaner production and sustainable development. First, an optimization model that minimizes the greenhouse's operating costs, including costs associated with greenhouse heating/cooling, ventilation, irrigation, carbon dioxide (CO

In the literature, a lot of work focused on studying intermittent control problems via feedback control strategy. No study on the intermittent control problems via sliding mode control method has been reported so far. This paper studies the problem of fast synchronization between two complex dynamical networks via aperiodically intermittent control and sliding mode control. In order to achieve fast synchronization of complex dynamical networks by using aperiodically intermittent sliding mode controller, new differential inequalities are derived firstly. After that, some sufficient finite-time synchronization criteria and finite-time achieving slide mode surface are obtained based on finite-time stability theory, aperiodically intermittent sliding mode control technique and constructing Lyapunov function. Finally, an example is provided to verify the effectiveness of the proposed theoretical methods.

In this paper, the finite-time synchronization problem of complex dynamic networks with time delay is studied via aperiodically intermittent control. By compared with the existed results concerning aperiodically intermittent control, some new results are obtained to guarantee the synchronization of networks in a finite time. Especially, a new lemma is proposed to reduce the convergence time. In addition, based on aperiodically intermittent control scheme, the essential condition ensuring finite-time synchronization of dynamic networks is also obtained, and the convergence time is closely related to the topological structure of networks and the maximum ratio of the rest width to the aperiodic time span. Finally, a numerical example is provided to verify the validness of the proposed theoretical results.

Deep neural networks have proven to excel classical medical image reconstruction techniques. Some networks are based on fully connected (FC) layers to achieve domain transformation such as from the data acquisition domain to the image domain. However, FC layers result in huge numbers of parameters which take a lot of GPU memory. Hence, they do not scale well, and the overall performance is limited. For ultrasound computed tomography (USCT) application, we propose a memory-efficient convolutional network that reconstructs images from the frequency domain to image domain with much less parameters compared with multilayer perceptron, by using data-driven learning. Extensive experiments demonstrate that our method achieves high reconstruction quality. It improves the structural similarity measure (SSIM) from 0.73 to 0.99 when compared with state-of-the-art reconstruction methods in this field while reduces 2/3 parameters when compared with deep neural network with FC layers to reconstruct images from frequency domain to image domain.

Ultrasound transmission tomography promises a high potential and novel imaging method for early breast cancer diagnosis; it can quantitatively characterize tissues or materials by the attenuation and speed of sound (SoS). Reconstruction of ultrasound transmission tomography is an inverse problem that can be solved iteratively based on a paraxial approximation of the Helmholtz equation as forward model, which is highly non-linear and time-consuming. In order to address these problems and reconstruct desired images, we design a dual domain network architecture for ultrasound transmission tomography reconstruction. It can enhance the information of measurement domain and directly reconstruct from pressure field measurements without using any initialization of reconstruction and fully connected layer. We train the network on simulated ImageNet data and transfer it for ultrasound transmission tomography images to avoid overfitting when the amount of ultrasound transmission tomography images is limited. Our experimental results demonstrate that a dual domain network produces significant improvements over state-of-the-art methods. It improves the measured structural similarity measure (SSIM) from 0.54 to 0.90 and normalized root mean squared error (nRMSE) from 0.49 to 0.01 on average concerning the SoS reconstruction, and from 0.46 to 0.98 for SSIM, from 353 to 0.03 for nRMSE on average concerning the attenuation reconstruction.

Network security has become an important issue in our work and life. Hackers' attack mode has been upgraded from normal attack to APT( Advanced Persistent Threat, APT) attack. The key of APT attack chain is the penetration and intrusion of active directory, which can not be completely detected via the traditional IDS and antivirus software. Further more, lack of security protection of existing solutions for domain control aggravates this problem. Although researchers have proposed methods for domain attack detection, many of them have not yet been converted into effective market-oriented products. In this paper, we analyzes the common domain intrusion methods, various domain related attack behavior characteristics were extracted from ATT&CK matrix (Advanced tactics, techniques, and common knowledge) for analysis and simulation test. Based on analyzing the log file generated by the attack, the domain attack detection rules are established and input into the analysis engine. Finally, the available domain intrusion detection system is designed and implemented. Experimental results show that the network attack detection method based on the analysis of domain attack behavior can analyze the log file in real time and effectively detect the malicious intrusion behavior of hackers , which could facilitate managers find and eliminate network security threats immediately.

Ultrasound Computed Tomography (USCT) is a new high resolution imaging method with high potential for breast cancer detection. USCT image reconstruction requires the iterative solution of a wave equation and its adjoint, being very time-consuming. To address this time consumption problem, a convolutional neural network with frequency pressure field as input is presented, outperforming classical iterative optimization strategies to solve the inverse problem. The Optical and Acoustic Breast Phantom Database (OA-Breast) is used to generate simulated USCT data using paraxial approximation forward model both for training and testing. Extensive experiments demonstrate that our neural network outperforms the classical strategies both in reconstruction time and quality.

Determining a systematic whole-building retrofit plan for envelope components and indoor appliances to achieve targets such as cost savings and policy compliance is a challenging task. To be specific, the systematic whole-building retrofit problem, when solved by an optimization approach, is highly complicated. It is sometimes even impossible to find a solution with given computation resources and algorithms. In addition, a costly comprehensive bottom-up audit is required to establish the parameters of the problem. This study presents two models to reduce the complexity of the systematic whole-building retrofit optimization problem. Firstly, the proposed models use the grouping concept to optimize the retrofit of subsystems in a building instead of individual components/appliances, which reduces the dimension of the problem effectively. Secondly, the models use so-called ‘notch test’ data, which are sampled and verified savings of an intervention, to eliminate the need for bottom-up energy audits. This further simplifies the retrofit optimization problem and reduces the retrofit cost. The models are based on our previous work and aim at energy savings maximization and payback period minimization, considering the green building policy and tax incentive initiatives. A case study shows that about 2530 MWh energy savings and an A rating from the energy performance certificate standard can be obtained with a payback period of 59 months, which verifies the feasibility and effectiveness of the models proposed.