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The fault cavern type reservoir in Block 1 of Shunbei area is located in the ultra-deep layer and is classified as a volatile oil reservoir. The reservoir is characterized by very deep burial with an oil/gas ratio between 250 and 500 m3/tone and a saturation pressure range of 28–36 MPa. As of March 2021, the formation pressure has dropped to 30.5 MPa and the pressure retention rate is 35.8%. In order to effectively develop this reservoir, it was decided to use a water injection development method with a planned transition to natural gas injection in March 2022 to further enhance recovery and maintain formation pressure. Through evaluation and research, the pressure distribution, production capacity change and reservoir dynamic response during the water injection development stage were analyzed with respect to the characteristics of fracture-controlled fracture-cave type reservoir in Shunbei-1 block. On this basis, the necessity, feasibility, and expected effects of conversion to natural gas injection are explored, including improvement of displacement efficiency, reduction of fluid output, and enhancement of formation energy replenishment. In addition, this study also carries out in-depth research and prediction on the optimization of natural gas injection parameters, the selection of injection methods and possible problems, aiming to provide scientific basis and technical support for the efficient and sustainable development of this block. In order to smoothly promote the implementation of natural gas injection development, a special reservoir description technique applicable to fault cavern type reservoirs was adopted. Based on this foundation, an in-depth study was conducted on the mechanism of natural gas displacement, so as to optimize the layout of the well network and the setting of injection and extraction parameters. In addition, a set of evaluation system for natural gas injection effect in fault cavern type reservoirs was constructed. By adopting the natural gas injection development method and combining the corresponding supporting technology development and optimization measures, the reservoir development efficiency and oil (gas) production rate have been effectively improved, and the risk of gas flaring has been reduced at the same time. These research results are of great significance for ensuring the comprehensive development and effective utilization of oil and gas resources, and provide strong technical support for future development work.

Abstract In this study, a method for assimilating FY4A advanced geostationary radiance imager (AGRI) cirrus‐effected radiances (CER) is investigated, and the impact of this method on water vapor analysis and rainstorm forecasting is examined through observing system simulation experiments and actual case experiments. The high proportion of inverted humidity profiles in the cirrus‐effected pixels is the main reason for the negative effect of assimilation in the mid‐to‐lower troposphere. To address this, relevant constraint conditions are incorporated into the cost function. The statistical results reveal that the addition of a CER assimilation improves the analysis increment of water vapor, with pattern correlation coefficients of 0.33, 0.35, and 0.20 at 200, 300, and 400 hPa, respectively, which are greater than those of a clear‐sky radiance assimilation (0.28, 0.33, and 0.17, respectively). Moreover, the inclusion of a CER assimilation greatly improves data utilization, and has a neutral to positive effect on precipitation forecasting.

Abstract Background Chronic inflammatory pain (CIP) frequently coincides with depression among patients. The onset and development of pain and depression are associated with altered neural synaptic plasticity. Electroacupuncture (EA) can effectively relieve CIP and depression. However, the underlying mechanisms have not been fully illustrated. Objective To explore whether EA can relieve CIP and depression by regulating hippocampal synaptic plasticity, and the present study offers foundational evidence for the efficacy of EA in treating CIP‐related depression (CIPD). Methods Rats were divided into four groups: 0.9% normal saline group, complete Freund's adjuvant (CFA) group, CFA + duloxetine group, and CFA + EA group. Pain hypersensitivity was detected by mechanical withdrawal threshold and thermal paw withdrawal latency, and the depression level was gauged using the open field test, the sucrose preference test, and the forced swimming test. The morphology of the hippocampal neurons was observed using Nissl staining. The protein expression levels of synuclein (Syn), postsynaptic density protein‐95 (PSD‐95), brain‐derived neurotrophic factors (BDNFs), tyrosine‐protein kinase B (TrKB), p‐TrkB, cAMP response element binding protein (CREB), and p‐CREB were measured by western blotting and immunofluorescence staining. BDNF and TrkB mRNA expression were detected using quantitative real‐time polymerase chain reaction (PCR) (qRT‐PCR). The content of 5‐hydroxytryptamine (5‐HT) and γ‐aminobutyric acid (GABA) was detected using enzyme‐linked immunosorbent assay, and the glutamic acid (Glu) content was determined using the ultraviolet colorimetry method. The hippocampal neuron ultrastructure was observed using transmission electron microscopy. Results EA could alleviate CIP and related depressive behaviors as well as protect the hippocampal neuronal structure from damage and regulate 5‐HT/GABA/Glu levels in the hippocampus. Additionally, EA could significantly increase the expression of synapse‐associated proteins such as PSD‐95 and Syn by activating the BDNF/TrKB/CREB signaling pathway. Conclusion EA improves pain and depressive behaviors in CIPD rats, and the mechanism may be related to synaptic plasticity mediated by the BDNF/TrKB/CREB signaling pathway.

Implicit learning refers to the process of unconsciously learning complex knowledge through feedback. Previous studies investigated the influences of different types of feedback (e.g., social and non-social feedback) on implicit learning. This study focused on the social information presented in the learning situation and tried to explore the effects of different social feedback on implicit rule learning. We assigned participants randomly into an encouraging facial feedback group (happy expression for correct answer, neutral but not negative expression for incorrect answer) and a discouraging facial feedback group (neutral but not happy expression for correct answer, negative expression for incorrect answer). The implicit learning task included four difficulty levels, and social feedback was presented in the learning phase but not the testing phase in two experiments. The only difference between the two experiments was that the sad face used as negative feedback in Experiment 1 was replaced with an angry face in Experiment 2 to enhance the ecological validity of the discouraging facial feedback group. These two experiments yielded consistent results: the performances in the encouraging facial feedback group were more accurate in both the learning and the testing phases at all difficulty levels. These findings indicated that the influence of encouraging social feedback for a better implicit learning achievement was stable and established a new groundwork for future research on incentive-based education, making it critical to investigate the impact of various forms of encouraging-based education on learning.

From 17 to 22 July 2021, the “21·7” extreme rainfall event (“21·7” ERE) hit Henan Province, breaking the record for mainland China with a maximum hourly rainfall of 201.9 mm at the Zhengzhou station. The long-lived (20 h) mesoscale Huang-Huai vortex (HHV) was an important system that directly affected the major rainfall stage, including the extreme hourly rainfall. This study investigates the formation and development mechanism of the HHV, as well as its association with the simulation of extreme hourly rainfall through numerical simulations. The simulated rainfall and radar composite reflectivity were in good agreement with the observations, thus effectively reproducing the generation and developmental process of the HHV. The analysis results showed that the HHV initially formed at 850 hPa on 19 July at 1800 UTC and eventually developed to 550 hPa. The positive feedback formed by the horizontal convergence and vertical vorticity transport was the main mechanism leading to the generation and deepening of the HHV. The stretching effect (STR) term played an absolutely dominant role in the increase in the vorticity tendency, and it primarily originated from the coupling effect of boundary layer jets (BLJs) and synoptic-weather-system-related low-level jets (SLLJs). The accurate simulation of the HHV allowed the early rainfall to reasonably reproduce the surface cold pool near the Zhengzhou station, and then the cooperation of the SLLJs, the BLJs, and the cold pool made the simulated extreme hourly rainfall exactly close to the Zhengzhou station, but with a weaker intensity, due to the fact that the HHV moved northeastward after its formation, resulting in a narrow range of southerly flow in southern Henan, which is not conducive to convective triggering in the southerly flow.

Many ensemble-based data assimilation (DA) methods use observation space localization to mitigate the sampling errors due to the insufficient ensemble members. Observation space localization is simpler and more timesaving than model space localization in implementation, but more difficult to directly assimilate satellite radiance observations, a kind of non-local observations. The vertical locations of radiance observations are undetermined and the transmission of observational information is thereby obstructed. To determine the vertical coordinates of radiance observations, a weighted average hypsometry is proposed. Using this hypsometry, AMSU-A radiance observations are directly assimilated with an ensemble four-dimensional variational (En4DVar) DA system. It consists of a four-dimensional ensemble-variational (4DEnVar) system providing ensemble covariance and a 4DVar system. Observing system simulation experiments show that the hypsometry alleviates the degradations in the late period of medium-range forecast in the Northern Extratropics that occur in the traditional peak-based hypsometry. It obviously improves the analysis qualities and forecast skills of the En4DVar system and its two components, especially in the Southern Extratropics, when incorporating AMSU-A radiance observations. The improvement in the En4DVar-initialized forecast is comparable to that in the 4DVar-initialized forecast in the Southern Extratropics and Tropics. It indicates that a proper hypsometry enables efficient extraction of useful information from AMSU-A radiance observations by 4DEnVar with observation space localization. Therefore, the 4DEnVar provides high-quality ensemble covariances for En4DVar.

Abstract A four‐dimensional ensemble‐variational (4DEnVar) data assimilation (DA) system was developed based on the global forecast system of the Global/Regional Assimilation and Prediction System (GRAPES‐GFS). Instead of using the adjoint technique, this system utilizes a dimension‐reduced projection (DRP) technique to minimize the cost function of the standard four‐dimensional variational (4DVar) DA. It dynamically predicts ensemble background error covariance (BEC) and realizes the explicit flow‐dependence of BEC in the variational configuration. An inflation technique based on a linear combination of analysis increments and balanced random perturbations, is utilized to overcome the problem of underestimation of BEC matrix (B‐matrix) during the assimilation cycle. To mitigate the spurious correlations in the ensemble B‐matrix caused by the insufficient ensemble members, an ensemble‐sample‐based subspace localization method is utilized. In order to evaluate the new system, single‐point observation experiments (SOEs) and observing system simulation experiments (OSSEs) were conducted with sounding and cloud‐derived wind data based on GRAPES‐GFS. The explicit flow‐dependent characteristic of the 4DEnVar system using a localized ensemble covariance was verified in the SOEs. In the OSSEs, the ensemble mean analysis of 4DEnVar outperforms the analysis of 4DVar. The deterministic forecast initialized from the 4DEnVar ensemble mean analysis has better performance in the short‐range forecasts, better (worse) performance in the early (late) period of the medium‐range forecasts in the Northern Extratropics, and opposite performance in the Southern Extratropics, and exhibits slightly worse effects in the Tropics. Moreover, the ensemble mean forecast initialized by the 4DEnVar system has higher forecast skills.

Abstract Marked by large interannual variability, East Asian summer monsoon (EASM) rainfall has profound socio‐economic impacts through its dominant influence on floods and droughts. Improving predictions of the interannual variations of EASM rainfall has important implications for over 20% of the world's population. While coupled modeling systems have demonstrated some prediction skill related to the El Niño Southern Oscillation with remote influence on EASM rainfall, the impact of soil moisture has heretofore not been systematically investigated. Using a weakly coupled data assimilation (WCDA) system to constrain the soil moisture and soil temperature in a coupled climate model with a global land data assimilation product, this study demonstrates significant improvements in simulating the interannual variations of EASM rainfall, capturing the notable shift to a “wetter‐South‐drier‐North” rainfall pattern in China in the early 1990s. Hindcast simulations initialized with the well‐balanced states from a coupled simulation with WCDA also show significant multi‐year rainfall prediction skill over East China and Tibetan Plateau. Improvements in predicting the EASM rainfall are attributed to the strong land‐atmosphere coupling in large areas over China, which allows improved predictions of soil moisture to influence precipitation through soil moisture‐precipitation feedback, and the effects of land anomalies on the EASM circulation. This study highlights the significant contribution of land to the interannual predictability of EASM rainfall, with a great potential to advance skillful interannual predictions of benefit to the large populations influenced by the annual whiplash of the summer monsoon rain.

Abstract A new weakly coupled data assimilation (CDA) system based on the dimension‐reduced projection four‐dimensional variational data assimilation (DRP‐4DVar) with a simplified off‐line localization technique and a fully coupled model, i.e., the Grid‐point Version 2 of Flexible Global Ocean‐Atmosphere‐Land System Model (FGOALS‐g2), was developed for the initialization of decadal predictions. A 1‐month assimilation window was adopted for the CDA system, in which monthly mean temperature and salinity analyses were assimilated along the trajectory of the coupled model during the initialization for the period of 1945–2006. The system is efficient because the 62‐year initialization only takes about 2.375 times of the time cost of the uninitialized run for the same period. Compared with the uninitialized simulation and the initialization without localization, ocean temperature and salinity, sea surface elevation, surface air temperature, and precipitation are in better agreement with the verification data. Furthermore, climate variabilities in the Pacific and Atlantic regions such as El Niño‐Southern Oscillation, Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) are more realistically captured. Starting from the initial conditions (ICs) generated by the initialization, 10‐member ensemble decadal prediction experiments were conducted each year from 1961 to 1996. The results demonstrate that higher decadal prediction skills of surface air temperature anomalies averaged over the globe, ocean, land, and the North Pacific subpolar gyre are achieved than those obtained from persistence, the uninitialized simulation, and the prediction initialized from the ICs without localization. Besides, PDO and AMO indices exhibit significant correlation skills in most lead times.

Current methods used to evaluate in vivo target efficacy of selected compound include western blot to semi-quantitatively analyze protein expression. However, problems arise as it is difficult to compare in vivo target efficacy of anti-tumor agents with the same mode of action. It is therefore desirable to develop a protocol that can quantitatively display in vivo target efficacy while also providing other useful information. In this study EdU labeling was used to mark out the proliferating area. The tumor tissue was accordingly divided into proliferating and non-proliferating areas. Fifteen tumor related proteins were stained by immunofluorescence and were found to express in either the proliferating or non-proliferating areas. This allows the quantitative analysis of protein expressions within the precise area. With simple image analysis, our method gave precise percent changes of protein expression and cell proliferation between the drugs treated group and the control group. Additional information, such as, the status of protein expression can also be obtained. This method exhibits high sensitivity, and provides a quantitative approach for in vivo evaluation of target efficacy.

Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM) is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the performance of LSSVM model. In this paper, a novel hybrid quantum harmony search algorithm-based LSSVM (QHSA-LSSVM) energy forecasting model is proposed. The QHSA which combines the quantum computation theory and harmony search algorithm is applied to searching the optimal values of and C in LSSVM model to enhance the learning and generalization ability. The case study on annual fossil fuel energy consumption for power generation in China shows that the proposed model outperforms other four comparative models, namely regression, grey model (1, 1) (GM (1, 1)), back propagation (BP) and LSSVM, in terms of prediction accuracy and forecasting risk.

Chromenone-derived natural products include chromones (flavone, isoflavone) and coumarins. Chromenone compounds not only exhibit impressive biological activities, but also are an important resource of experimentally used fluorophores, such as, 7-amino-4-methylcoumarin (AMC). Various chromenone compounds have reported to have weak fluorescence, and this has the potential to interfere with the measurements during AMC fluorogenic assays and result in non-robust assay readouts. Several flavones and isoflavones were found as SIRT1 activators, while fluorogenic sirtuin assays utilized AMC labelled peptides as the substrates. In this study we investigated whether the fluorescent properties of chromenone-derived natural products interrupt the measurement of SIRT1/2 modulated activities. We found that the reported SIRT1 activators: flavones were detected with the SIRT1 activation activity, but isoflavones were not detected with SIRT1 activation activity, and instead that they were found to be fluorogenic compounds. Another chromenone compound, osthole, exhibited a moderate SIRT2 inhibitory activity with an IC50 of 10 μM. In conclusion, the fluorescent properties of these chromenone compounds do affect the measurement of the sirtuin activities of both inhibitors and activators. However, if the possible fluorescence properties are mitigated in the assay readout, these fluorogenic assays enable the screening of activity modulators.

Cancer stem cells (CSCs) can invade and metastasize by epithelial-to-mesenchymal transition (EMT). However, how they escape immune surveillance is unclear. B7H1 is crucial negative co-stimulatory molecule but little information about whether it works in CSCs. Therefore, we determined the expression of B7H1 and EMT-associated markers in colorectal cancer stem-like cells to investigate a possible immunoevasion way of CSCs. We enriched CD133+ colorectal cancer cells which manifested the CSCs-like properties such as higher levels of other stem cell markers Oct-4 and Sox-2, tumor sphere forming ability and more tumorigenic in NOD/SCID mice. These CD133+ cells possess EMT gene expression profile including higher level of Snail, Twist, vimentin, fibronectin and lower level of E-cadherin. Moreover, CD133+ cells in both cell line and colorectal cancer tissues expressed high level of negative co-stimulate molecule B7H1. Furthermore, some B7H1+ cancer cells also showed the characteristic of EMT, indicating EMT cells could escape immune attack during metastasis. B7H1 expression and EMT phenotypes on CSCs indicates a possible immunoevasion way.

The extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model has been applied to analyzing the relationship between CO2 emissions from power industry and the influential factors for the period from 1997 to 2020. The two groups found through partial least square (PLS) regularity test show two important areas for CO2 emissions reduction from the power industry: economic activity and low-carbon electric technology. Moreover, considering seven influential factors (economic activity, population, urbanization level, industrial structure, electricity intensity, generation structure, and energy intensity) that affect the power CO2 emissions and the practical situation in the power sector, possible development scenarios for the 13th Five-Year Plan period were designed, and the corresponding CO2 emissions from the power sector for different scenarios were estimated. Through scenario analysis, the potential mitigation of emissions from power industry can be determined. Moreover, the CO2 emissions reduction rates in the different scenarios indicate the possible low-carbon development directions and policies for the power industry during the period of the 13th Five Year Plan.