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L-Homoserine is a valuable amino acid as a platform chemical in the synthesis of various important compounds. Development of microbial strains for high-level L-homoserine production is an attractive research direction in recent years. Herein, we converted a wild-type Escherichia coli to a non-auxotrophic and plasmid-free hyperproducer of L-homoserine using systematically metabolic engineer strategies. First, an initial strain was obtained through regulating L-homoserine degradation pathway and enhancing synthetic flow. To facilitate L-homoserine production, flux-control genes were tuned by optimizing the copy numbers in chromosome, and transport system was modified to promote L-homoserine efflux. Subsequently, a strategy of cofactors synergistic utilization was proposed and successfully applied to achieve L-homoserine hyperproduction. The final engineered strain could efficiently produce 85.29 g/L L-homoserine, which was the highest production level ever reported from a plasmid-free, antibiotic-free, inducer-free and nonauxotrophic strain. These strategies used here can be considered for developing microbial cell factory of other L-aspartate derivatives.

Batteries can provide valuable operational flexibility to facilitate the system efficiency. However, there lacks market environments to effectively harness and monetize the value of these assets. Most existing works apply a profit-oriented single-signal pricing scheme, which mixes the value of energy and flexibility together and may ultimately raise the electricity bill. Therefore, this paper proposes a profit-neutral double-signal retail pricing scheme that distinguishes elastic market players (i.e., batteries) from inelastic market players (i.e., inflexible loads) and quantifies the value of energy and flexibility separately. Experimental results indicate that under the incentive provided by the proposed retail pricing scheme: 1) The system efficiency benefit aligns with benefits to both batteries and inflexible loads; 2) Value of operational flexibility, contributing to improve the energy efficiency, can be transparently priced and fairly allocated among batteries; and 3) The dominant role in which inflexible loads play on determining the market price is avoided.

At present, there is not enough research about the application of liraglutide nano preparations in perioperative neurocognitive dysfunction. Therefore, the purpose of this study is the mechanism of the effect of liraglutide nano preparations on perioperative neurocognitive dysfunction in aged mice. In this study, 140 male SD rats aged 6-8 weeks were used as the research object, and were divided into 4 groups (n=24) according to the random number table method, which were group C (control group), group S (model group), and treatment. Group (low-dose liraglutide pretreated control group) and DS2 group (high-dose liraglutide pretreated control group) were treated with liraglutide anesthesia to establish a cognitive dysfunction model. Morris water maze experiment was conducted 4 days after anesthesia to compare the escape latency and the number of crossings of the original platform in each group; after 4 days of anesthesia, 18 old mice were randomly selected from each group for fluorescence quantitative polymerase chain reaction (RealTimePCR) and protein Western blotting (Western.Blot) was used to determine the mRNA and protein levels of Caspase-3, Bax and Bcl-2 in the hippocampus; the remaining 6 old mice in each group were taken to observe the pathological changes of the hippocampus neurons by transmission electron microscopy . Compared with saline-treated group, the levels of NF-KB, TNF-a and IL-1β protein in mice treated with liraglutide decreased and IkB increased significantly (p0.05). Liraglutide intervention may alleviate non-alcoholic fatty liver in diabetic mice by reducing the expression of inflammatory genes in liver tissue, thereby improving neurocognitive dysfunction in mice.

Distributed energy resources (DERs) are rocking the utilities’ business landscape. It calls for competitive market environments that incentivize DERs to form maximum operating efficiency. Among proposed pricing schemes, distribution-level locational marginal price (DLMP) is effective in signaling the marginal generation cost differences driven by energy losses and network constraints. It can be derived from a distribution-level optimal power flow (OPF) framework, as it essentially presents the sensitivity of optimized generation cost towards incremental loads. However, due to the high resistance-to-inductance ratio and unbalanced characteristics of distribution networks, computational affordable DLMPs are highly challenged. This article provides a linear-approximated DLMP that can be solved efficiently and generalized to account for reactive power flow, three-phase unbalanced loads and meshed network structure. The successive linear programming technique is introduced to enhance the model accuracy. Case studies on an IEEE 123-Bus system validate its accuracy against a nonlinear benchmark and capability in offering proper incentives.

l-arginine is a semi-essential amino acid that is broadly used as food additives and pharmaceutical intermediates. The synthesis of l-arginine is restricted by complex metabolic mechanisms and suboptimal fermentation conditions. Initially, a mutant strain that accumulated 19.4 g/L l-arginine was generated by random mutagenesis. Subsequently, a mutation of the repressor protein (argR

Land resources are foundational for human survival and development. In contrast, land use/land cover (LULC) dynamics drive considerable changes in ecosystem services. Recently, China witnessed a new stage of rapid urbanization. Therefore, investigating the relationships between ecosystem services value (ESV) and LULC in these areas is highly relevant. Based on the data of land use and socioeconomic development in Shanghai from 2000 to 2020, we adopted a land use/land cover dynamics analysis method and established the ESV per unit area at the city scale, discussed the impact of LULC on ESV spatially and quantitatively, and tested the research process based on the sensitivity analysis of the ESV coefficient. The results show that from 2000 to 2020, the LULC pattern in Shanghai rapidly changed. In particular, the area of cultivated land has shrunk by 123.96 thousand hm2, while the construction land has expanded by 141.26 thousand hm2, which has led to a decline in ESV of the entire city (especially regarding hydrological adjustment and biodiversity). Nevertheless, although the area of trench and lakes only occupies 1.67–3.16% of the total area of land, its ecological value accounts for an astonishing 23.80–50.70% of the total ESV. At the district level, the primary decline in eco-system services value was noted in the Chongming District in the north and Pudong New Area in the east of Shanghai. However, due to the overall planning of the city and the advantages of its resource endowment, Qingpu District and its surrounding areas in western Shanghai have witnessed improvements in terms of the values of hydrological adjustment, water supply, and environmental purification. This study presents a deeper and more comprehensive understanding of issues regarding ESV in rapidly urbanized areas, thereby providing an important reference for decision-makers regarding the rational layout of cities, sustainable use of land, and management of natural ecosystems.

Seed longevity is important for the maintenance of seed nutritional quality, vigor, and germination potential during storage. Sacred lotus is known as one of the longest living seeds in the world and their ability to maintain longevity has been widely investigated. In this study, a suitable controlled deterioration treatment (CDT) method was first established to evaluate the vigor loss of lotus plumule (LP), and then the Tandem Mass Tags (TMT)-based proteomic analysis was performed on LP from the CDT-treated seed to quantitatively and qualitatively analyze the protein profile dynamic. In total, 4002 proteins were successfully quantified, of them, 558 differently accumulated proteins (DAPs) were identified. Protein processing and RNA-related proteins were found more easily to be affected by CDT, which may directly result in seed vigor loss. Meanwhile, CDT resulted in remarkable up-regulation of numerous proteins related to antioxidation, photosynthesis, RNA and DNA stability, starch and sucrose mobilization, and cell membrane and wall stability, which potentially played key roles in maintaining the lotus seed vigor under CDT. Histological and physiological analyses were also performed to verify some proteome results. This study provided both fundamental data and new insights to further uncover the secret of lotus seed longevity. SIGNIFICANCE: Seed aging affects the seed quality and can result in direct economic losses. The exceptional longevity of sacred lotus seed has attracted extensive attention. In this study, an optimized CDT method was used to mimic the natural aging process of sacred lotus seed, and based on TMT-based quantitative proteomic analysis on the LP profile of CDT-treated seeds, a series of differentially accumulation of specific proteins (DEPs) were revealed related to CDT resistance. Correspondingly, the physiological state and histological structure of the LP along with the CDT were detected to verify the proteome data. This study provided comprehensive information for the molecular basis of lotus seed aging analysis and facilitate to screen seed longevity related proteins for other plant species.

Buildings, as major energy consumers, can provide great untapped demand response (DR) resources for grid services. However, their participation remains low in real-life. One major impediment for popularizing DR in buildings is the lack of cost-effective automation systems that can be widely adopted. Existing optimization-based smart building control algorithms suffer from high costs on both building-specific modeling and on-demand computing resources. To tackle these issues, this paper proposes a cost-effective edge-cloud integrated solution using reinforcement learning (RL). Beside RL’s ability to solve sequential optimal decision-making problems, its adaptability to easy-to-obtain building models and the off-line learning feature are likely to reduce the controller’s implementation cost. Using a surrogate building model learned automatically from building operation data, an RL agent learns an optimal control policy on cloud infrastructure, and the policy is then distributed to edge devices for execution. Simulation results demonstrate the control efficacy and the learning efficiency in buildings of different sizes. A preliminary cost analysis on a 4-zone commercial building shows the annual cost for optimal policy training is only 2.25% of the DR incentive received. Results of this study show a possible approach with higher return on investment for buildings to participate in DR programs.

With the popularisation of laparoscopic cholecystectomy, ligation clips have been commonly used for ligating the cystic duct and cystic artery. However, non-degradable clips remain in the body long-term, which significantly increases the risk of the clip becoming detached. Thus, magnesium alloys have attracted tremendous attention owing to their biodegradability and good biocompatibility. However, the poor corrosion resistance hinders the clinical application of magnesium alloys with microarc oxidation/phytic acid (MAO/PA) composite coatings as protective coatings. Here, these alloys were used to hinder the rapid material degradation in aqueous solution. Electrochemical tests were conducted to evaluate the in vivo degradation behaviour in simulated body fluid (SBF) for Mg–Zn–Y–Nd alloys, and scanning electron microscopy (SEM) was used to observe the micromorphology of in vivo clip degradation. Cell toxicity, cell adhesion, and flow cytometry were performed in vitro to detect cytocompatibility. Biochemical detection of serum magnesium, serum creatinine (CREA), blood urea nitrogen (BUN), alanine transaminase (ALT), and alanine aminotransferase (AST), and haematoxylin–eosin (HE) staining of the heart, liver, and kidney tissues in vivo was conducted to determine the biocompatibility properties after surgery. Electrochemical measurements and SEM images revealed that the MAO/PA-coated magnesium alloy delayed corrosion in SBF. The apoptosis rate increased slightly with increased extract concentration. Nevertheless, MAO/PA-coated magnesium alloys still exhibited good cytocompatibility. No obvious abnormality was observed in the blood biochemical test or HE staining. Thus, MAO/PA-coated magnesium alloys exhibit better corrosion than bare magnesium. In addition, Mg–Zn–Y–Nd and MAO/PA-coated magnesium alloys exhibited no cytotoxicity, good adhesion, and biosafety.

Let L be a Lie superalgebra over a field of characteristic different from 2, 3 and write ID∗(L) for the Lie superalgebra consisting of superderivations mapping L to L2 and the central elements to z...

Objective To elucidate the neuroprotective function of metformin in suppressing propofol-induced apoptosis of HT-22 cells. Methods HT-22 cells were treated with 0, 10 or 100 μmol/L propofol, followed by determination of their proliferative ability. Subsequently, changes in proliferation and apoptosis of propofol-treated HT-22 cells induced with metformin were assessed. Apoptosis-associated genes in HT-22 cells were detected by Western blot. At last, regulatory effects of Cav-1 on propofol and metformin-treated HT-22 cells were examined. Results Propofol treatment dose-dependently decreased proliferative ability and increased apoptosis ability in HT-22 cells, which were partially blocked by metformin administration. Upregulated Bcl-2 and downregulated Bax were observed in propofol-treated HT-22 cells following metformin administration. In addition, Cav-1 level in HT-22 cells was regulated by metformin treatment. Notably, metformin reversed propofol-induced apoptosis stimulation and proliferation decline in HT-22 cells via downregulating Cav-1. Conclusion In our study, we found that propofol could induce apoptosis of HT-22 cells and metformin could rescue the apoptosis effect regulated by propofol. Then, we found that metformin protects propofol-induced neuronal apoptosis via downregulating Cav-1.

Differential scanning calorimetry was used to investigate the non-isothermal crystallization kinetics of the Fe76.5Nb3B20Cu0.5 glassy alloys. The nanocrystallization products and soft magnetic properties as a function of annealing temperatures were also studied by using X-ray diffractometer and vibrating sample magnetometer. The activation energies for Eg, Ex, and Ep are equal to 499 ± 3, 558 ± 7, and 693 ± 2 kJ mol−1 by Kissinger method, respectively, and 512 ± 3, 571 ± 9, and 715 ± 2 kJ mol−1 estimated by Ozawa method, respectively. The results indicate that the glass transition process is easier than the nucleation and grain growth process, while the grain growth process is much difficult than the nucleation process. The local Avrami exponent n(x) varies with the crystallized volume fraction x, indicating that the crystallization process has different crystallization mechanisms. The n(x) ranging from 1.0 to 1.2 for 0.05

Urban parks positively affect the life quality and health of urban residents as well as the environment where they live. When it comes to the design of a future urban forest park, it is necessary to consider the protection of ecological environment, landscape sustainability and practicability. This study explored residents’ spatial preference for urban forest parks based on preference survey data. According to the rating scores obtained for four urban forest park routes during physical activities, this study used cognitive maps and multinomial logit models to figure out the potential influencing factors affecting residents’ spatial preference while they engage in physical activities. The results suggest that forest routes are still the primary choice for urban residents. Although familiarity with the spatial image preference for urban forest parks varied from person to person, residents’ choice of route shows certain commonalities, which was reflected in the sequential cognitive maps obtained from them. In addition, residents’ route preference is influenced by their exercise habits, environmental preference and residential location. There is also a certain correlation between residents’ preference and their characteristics. This study provides additional information for planners, developers, engineers, architects and foresters in building a more suitable environment that is aesthetically appealing and ecologically sound for physical exercising.

Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

Load forecasting problems have traditionally been addressed using various statistical methods, among which autoregressive integrated moving average with exogenous inputs (ARIMAX) has gained the most attention as a classical time-series modeling method. Recently, the booming development of deep learning techniques make them promising alternatives to conventional data-driven approaches. While deep learning offers exceptional capability in handling complex non-linear relationships, model complexity and computation efficiency are of concern. A few papers have explored the possibility of applying deep neural networks to forecast time-series load data but only limited to system-level or single-step building-level forecasting. This study, however, aims at filling in the knowledge gap of deep learning-based techniques for day-ahead multi-step load forecasting in commercial buildings. Two classical deep neural network models, namely recurrent neural network (RNN) and convolutional neural network (CNN), have been proposed and formulated under both recursive and direct multi-step manners. Their performances are compared with the Seasonal ARIMAX model with regard to accuracy, computational efficiency, generalizability and robustness. Among all of the investigated deep learning techniques, the gated 24-h CNN model, performed in a direct multi-step manner, proves itself to have the best performance, improving the forecasting accuracy by 22.6% compared to that of the seasonal ARIMAX.

Urban parks provide their residents with a space for activities and interactions, and soundscapes play an important role in influencing the residents’ sense of experience of the park environment, with the result that the preferences of residents regarding multiple sound sources can be used as the basis of improving the soundscape quality and optimizing the function of urban parks. Using Shanghai Century Park as the research area, this study focused on the residents’ preference to multiple sound sources based on registered residence differences, and comprehensively used the semantic differential method, importance-performance analysis model, and multinomial logit model to conduct quantitative research on urban park soundscapes and combined the measurement results of the soundscape with the residents’ perception and preference. The results showed that the artificial sounds in Century Park were noisy during the morning and evening peak, while the human sounds were noisy when residents gathered in the park. There was a close relationship between the favorability and subjective loudness of urban park soundscapes, and the residents’ perceptions of urban park soundscapes substantially differed in terms of loudness, tone, and sense of belonging, whereas the differences in the perception of timbre, coverage, and sound source distance were relatively small. Furthermore, it is necessary to enhance the role of natural sounds as well as control the main noise-producing sound sources so that various sounds in the park will not interfere with each other; the optimization of soundscapes should also focus on the residents’ different soundscape preferences due to their individual characteristics.

Background: The occurrence and development of colorectal cancer is related to the compositional and functional variation of gut microbiota. Compared with healthy people, gut microbiota of patients with colorectal cancer is in disorder. Most traditional Chinese Medicine is effective by oral administration and has both anti-tumor effect and enteric microecological regulative effect. However, whether the dysbiosis of gut microbiota under tumor burden affects the serum metabolome of human body that related to traditional Chinese medicine is unclear. In this study, Fufangchangtai (FFCT) was chosen to be the model prescription to explore the correlation between gut microbiota and the serum metabolism related to FFCT in anti colorectal cancer treatment. Results: The gut microbiota between colorectal tumor-bearing mice and healthy mice were determined by 16S rRNA gene sequencing, showing quite differences between the two groups and suggesting that Firmicutes , Deferribacteres , Bacteroidetes and Proteobacteria were marked differential intestinal bacteria. The alternations in serum metabolome in the FFCT-treating tumor-bearing mice and simple FFCT-treating mice were detected using Liquid chromatogragh-mass spectrometer (LC/MS), showing significant differences between the two groups as well. Metabolites of FFCT like Citric acid, （±）12-HEPE, Cycloartanyl ferulate were much more in simple FFCT-treating mice, indicating that the present of tumor could affect the absorption and metabolism of FFCT. Additionally, these differential metabolites of FFCT involved in multiple pathways including the Alanine, aspartate and glutamate metabolism, Central carbon metabolism in cancer, Biosynthesis of amino acids. Different doses of FFCT were given to the tumor-bearing mice through oral administration, and the results of gut microbiota 16S rRNA gene sequencing showing that FFCT-treating groups has higher abundance of Firmicutes , Turicibacter and Roseburia than tumor-bearing group, moreover, the abundance of these bacteria was positively correlated to the drug concentration. Firmicutes and Bacteroidetes in FFCT-treating groups showed a similar trend with Healthy group, indicating the modulation of FFCT on gut microbiota of colorectal tumor-bearing mice.Conclusions: Collectively, we concluded that the dysbiosis of gut microbiota in tumor-bearing mice could affect the serum metabolome of human body that related to FFCT, and FFCT could correct the gut microbiota of colorectal tumor-bearing mice. It was pointed out that the GM should be concerned during the therapy of FFCT. The more healthier intestinal microenvironment was conductive to the better clinical curative effect.

Sepsis-associated encephalopathy (SAE) is a serious and diffuse cerebral dysregulation with a high morbidity and mortality caused by sepsis. Mitophagy plays an important role in SAE, and microglial phagocytosis of apoptotic cells (efferocytosis) is the core of the brain regenerative response. Voltage dependent anion channel (VDAC1) is an important regulator of mitophagy. However, it remains unknown whether VDAC1 influences SAE progression by regulating mitophagy and efferocytosis. Herein, we explored the mechanism where knockdown of VDAC1 alleviated the cognitive dysfunction caused by sepsis-associated encephalopathy and further elucidated the underlying molecular mechanisms. SAE model in mice was established through caecal ligation and puncture (CLP). The increased mitophagy and decreased efferocytosis were observed by the transmission electron microscope (TEM) in the SAE model. Besides, immunoblot tests showed an interaction between autophagy and efferocytosis. Further behavior tests and TEM results indicated that knockdown of VDAC1 alleviated the cognitive dysfunction by decreasing the autophagy and increasing the efferocytosis in a PINK1/Parkin-dependent manner. Based on these results, we conclude that knockdown of VDAC1 alleviates the cognitive dysfunction in the CLP-induced SAE mouse model.

Electric water heaters (EWHs) are usually equipped with inbuilt thermostats to measure water temperature near the installed positions. Most of the existing EWHs have only two thermostats and often do not have water flow sensor installed. Estimation of state of heat energy (SOHE) inside the hot water tank, which is crucial for efficient control and operation of the EWH, under such imperfect system conditions is very challenging. This paper, therefore, designs a transactive EWH agent (TEWHA) to better estimate SOHE in presence of measurement errors and imperfect system knowledge. The TEWHA, built-in Python, approximates the EWH physics using limited measurements obtained from the ground truth GridLAB-D model. The performance of the TEWHA is validated against ground truth system for three scenarios: a) ideal condition where TEWHA has complete knowledge of the system, b) imperfect condition where TEWHA has incomplete system information, and c) noised condition where TEWHA measurements contain random errors. Finally, an optimization problem is formulated and solved to demonstrate how TEWHA could use the estimated and measured parameters for a transactive control of EWH.