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Nonalcoholic fatty liver disease (NAFLD) can be ameliorated by calorie restriction, which leads to the suppressed somatotroph axis. Paradoxically, the suppressed somatotroph axis is associated with patients with NAFLD and is correlated with the severity of fibrosis. How the somatotroph axis becomes dysregulated and whether the repressed somatotroph axis impacts liver damage during the progression of NAFLD are unclear. Here, we identify a regulatory branch of the hepatic integrated stress response (ISR), which represses the somatotroph axis in hepatocytes through ATF3, resulting in enhanced cell survival and reduced cell proliferation. In mouse models of NAFLD, the ISR represses the somatotroph axis, leading to reduced apoptosis and inflammation but decreased hepatocyte proliferation and exacerbated fibrosis in the liver. NAD+ repletion reduces the ISR, rescues the dysregulated somatotroph axis, and alleviates NAFLD. These results establish that the hepatic ISR suppresses the somatotroph axis to control cell fate decisions and liver damage in NAFLD.

The absence of better biomarkers currently limits early diagnosis and treatment of triple-negative breast cancer (TNBC). Our previously published study reported that the cyclic-peptide SD01 exhibited specific binding to EphA2 (Ephrin type-A receptor 2) on TNBC. To develop a novel PET imaging agent, we prepared gallium-68 (68Ga) labeled-DOTA-SD01 and evaluated its specificity and effectiveness through micro PET/CT imaging in a TNBC-bearing mouse model. SD01 and a control linear peptide YSA were conjugated to DOTA and subsequently labeled with 68Ga, obtaining 68Ga-DOTA-SD01 and 68Ga-DOTA-YSA. Both showed high radiochemical purity, stability, good hydrophilicity, and high binding affinity to 4T1 cells. Micro PET/CT imaging showed high radioactivity accumulation in tumors; SUVmean (mean standardized uptake value) of tumors in the group of 68Ga-DOTA-SD01 was 3.34 ± 0.25 and 2.65 ± 0.32 in the group of 68Ga-DOTA-YSA; T/NT ratios (target to non-target, SUVmean ratios of tumor to muscle) were 3.12 ± 0.06 and 2.77 ± 0.11 at 30 min, respectively (p < 0.05). The biodistribution study showed that tumor uptake % ID per g (percentage of injected dose per gram of tissue) in the group of 68Ga-DOTA-SD01 was 2.73 ± 0.34, and 1.77 ± 0.38 in the group of 68Ga-DOTA-YSA; T/NT ratios (radioactivity of tumor to muscle) were 3.55 ± 0.12 and 3.05 ± 0.10 for both groups at 30 min, respectively (p < 0.05). All these suggest that 68Ga-DOTA-SD01 may act as a better novel PET imaging agent for EphA2 positive tumors, such as TNBC. [ABSTRACT FROM AUTHOR]

To enhance the hitting accuracy of tank with moving firing, an uncertain optimization method based on stochastic programming is adopted to reduce the initial disturbance of projectile. Firstly, the firing dynamics model of moving tank is modeled to simulate the initial disturbance of projectile. Secondly, the controllable interior ballistic parameters such as projectile structure parameters and propellant parameters are treated as random design variables. The surrogate model for the firing dynamics model of moving tank is constructed by using the neural network based on deep learning. The uncertain optimization problem is transformed into a deterministic optimization problem by stochastic programming method. Then multi-objective genetic algorithm is adopted to settle optimization model, and reasonable design interval of random design variables is obtained. Finally, a six-degree-of-freedom rigid external ballistics model is used to establish a hitting accuracy evaluation model of moving tank based on interval uncertainty analysis. Through this model, the effectiveness of the optimization method is demonstrated. [ABSTRACT FROM AUTHOR]

Industrial parks cover a variety of production capacities and energy-consuming entities, with large load demand and complex energy-using structure, and common problems such as low energy utilization efficiency and unreasonable energy structure. The construction of an integrated energy system (IES) with a combined cooling, heating and power system as the core unit in the industrial park is of great significance for achieving reliable, efficient and clean energy use in the park. Therefore, this article is based on the integrated energy system of the industrial park, aims at the lowest total cost of park operators, and considers the constraints of grid node balance, equipment output and energy storage equipment, and constructs source-grid-load-storage linkage operation optimization model, and build a chaotic particle swarm algorithm (CPSO) to solve the model. Finally, a typical industrial park in my country is taken as an example to analyze the scientificity of the model.

The economy of the Regional Integrated Energy System (RIES) scheduling scheme is affected by the parameter accuracy of different energy conversion equipment models. The traditional static energy hub (EH) model regards the equipment efficiency as a constant and ignores the variable condition characteristics of the equipment efficiency changing with the load rate. This paper presents an optimal scheduling method of RIES considering the characteristics of equipment under variable operating conditions. Firstly, the architecture of the integrated energy system is analyzed. Secondly, based on the DEH model, an optimal scheduling method of RIES was proposed considering the characteristics of equipment under variable operating conditions. Finally, an optimal scheduling analysis is performed for a typical RIs. The results show that the proposed method can improve the accuracy of the equipment model and reduce the cost prediction error by considering the variable operating conditions of the equipment, thus providing a more economical scheduling scheme for Ries.

This paper studies the design of clean electric energy alternatives based on electric energy alternative equipment. First, sort out the current research status of energy substitution at home and abroad, and determine the research focus of electric energy substitution; list the electric energy substitution equipment that is suitable for construction, industrial, commercial and other fields, analyze its technical characteristics and specific application scenarios; list each device To analyze the performance of the equipment, establish a multi-factor output model for energy storage and production equipment according to the key influencing factors of the equipment performance coefficient; finally, based the above research, consider the substitutability of each equipment With complementary characteristics, the design is suitable for high-clean energy electric energy to replace typical structures.

Traditional demand response technologies optimize the load profile by cutting or shifting the electrical load, but they affect the customer’s energy experience, and thus the customer’s motivation to participate in demand response and load controllability are limited. For community integrated energy operators, the study of integrated customer demand response can explore their dispatch potential. Based on consumer psychology, we study the alternative energy use behavior of residential customers, establish a customer optimization model and a community integrated energy operator optimization model, and set up a Stackelberg master-slave game model with the operator as the leader and the customer as the follower. The simulation case verifies that the utilization of load-side multi-energy complementary characteristics and integrated demand response behavior can effectively improve the utilization of renewable energy and the efficiency of integrated energy operators.

The planning of integrated energy system is a very complex multi-target, multi-constraint, nonlinear, random uncertainty mixed integrated combination optimization problem, its planning and design process should not only consider the interdependence between the system capacity, energy conversion, energy storage, energy use and other links, but also consider the interaction and integration of cold, hot, electricity and other multi-energy flows, which is essentially a non-deterministic polynomial difficult problem. China’s energy continues to develop rapidly, all kinds of sensors and intelligent equipment data is increasing, the data obtained in the equipment and all kinds of sensors collected energy load prediction related factors such as temperature, weather, wind speed and other data volume increased dramatically, the data dimension is also increasing, the scale of data has also increased from GB to TB or even higher, based on the traditional prediction methods and intelligent prediction methods, has been far below the load forecast desired to achieve accuracy and speed requirements, Therefore, the use of big data technology to predict energy demand is an important future direction.