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On 7 December 2022, the State Council of China released ‘Measures to Further Optimize the Implementation of the Prevention and Control of the New Coronavirus Epidemic’. The previous three-year dynamic zero epidemic prevention policy was then replaced with a full liberalization policy. On 5 May 2023, the World Health Organization declared that COVID-19 no longer constituted a ‘public health emergency of international concern.’ However, given the ongoing prevalence of coronavirus, emerging mutations, and the liberalization of restrictions, there are increased risks of vulnerable people contracting new variants. Low vaccination coverage among older people with compromised immune systems, puts them at further risk. The policy shift will increase pressure on already stretched health infrastructure and medical resources. This short article adds to the current debate arguing that the Chinese government should take commensurate preventive measures, including strengthening medical facilities and equipment and targeting ongoing vaccination in older people.

Oceanic mesoscale eddies are prevalent throughout the global ocean, playing a critical role in material and energy transport while significantly influencing climate change. Accurate characterization of their three-dimensional structures and movement is essential for a quantitative analysis of their transport processes. Traditional eddy detection algorithms have lower successful detection rate and with more limitations, so they fall short in the complex and dynamic ocean environment. The rising trend of applying artificial intelligence (AI) algorithms, due to their efficiency, precision, and automation, addresses this challenge. This study employs the 3D-U-Res-Net algorithm to identify the three-dimensional structures of mesoscale eddies in the Southern Ocean using GLORYS12V1 data from 2011 to 2020. A vector geometry-based eddy detection algorithm (VG) initially identified 1587292 eddy snapshots in the Southern Ocean (2011–2019), which were used for training the 3D-U-Res-Net algorithm. Data from 2020 served as the ground truth and validation set. The successful detection rate of 3D-U-Res-Net algorithm is 100%, which means that it identified all 135734 eddy snapshots from the VG dataset in 2020. For eddy tracking, the VG algorithm counted 18168 eddy tracks, whereas the 3D-U-Res-Net counted 18559, reflecting a 2.15% bias. To reduce uncertainty, eddies with lifespans shorter than two weeks were excluded. The average lifespans and traveling distances for eddies detected by the 3D-U-Res-Net (VG) algorithm were 29.35 (29.61) days and 77.78 (37.60) km, respectively, with the 3D-U-Res-Net identifying eddies with longer traveling distances. The mean radius of eddies detected by the VG algorithm was 43.16 km, while the 3D-U-Res-Net detected eddies with a mean radius of 43.74 km, a 0.58 km increase. We categorized eddies into four three-dimensional structures: bowl-shaped, cone-shaped, lens-shaped, and cylindrical. The VG algorithm identified these structures in proportions of 32%, 31%, 25%, and 12%, respectively, whereas the 3D-U-Res-Net algorithm found 19.48%, 19.58%, 0.04%, and 60.9%, respectively. The 3D-U-Res-Net identified more cylindrical eddies and was approximately ten times faster than the VG algorithm. Overall, this algorithm has good performance and higher efficiency. It is an attempt of using AI for oceanic research, and more works can be carried out in the future.

Testicular aging manifests as impaired spermatogenesis and morphological alterations in Drosophila. Nonetheless, the comprehensive molecular regulatory framework remains largely undisclosed. This investigation illustrates the impact of copper overload on testicular aging and underscores the interplay between copper overload and lncRNA. Copper overload triggers Cuproptosis through the mitochondrial TCA cycle, facilitating intracellular interactions with Ferroptosis, thereby governing testicular aging. Dysfunction of lncRNA:CR43306 also contributes to testicular aging in Drosophila, emphasizing the significance of lncRNA:CR43306 as a novel aging-associated lncRNA. Moreover, copper overload exacerbates spermatid differentiation defects mediated by lncRNA:CR43306 deficiency through oxidative stress, copper, and iron transport. Therapeutically, Ferrostatin-1 and Resveratrol emerge as potential remedies for addressing testicular aging. This study offers perspectives on the regulatory mechanisms involving copper overload and lncRNA:CR43306 deficiency in the context of testicular aging.

Objectives: The aims of this study were to screen for phagocytosis regulator-related genes in tissue samples from children with medulloblastoma (MB) and to construct a prognostic model based on those genes. Methods: Differentially expressed genes between the MB and control groups were identified using the GSE50161 dataset from the Gene Expression Omnibus database. Prognosis-related phagocytosis regulator genes were selected from the GSE85217 dataset. Intersecting genes of the two datasets (differentially expressed prognosis-related phagocytosis regulator genes) were submitted to unsupervised cluster analysis to identify disease subtypes, after which the association between the subtypes and the immune microenvironment was analyzed. A prognostic risk score model was constructed, and functional, immune-related, and drug sensitivity analyses were performed. Results: In total, 23 differentially expressed prognosis-related phagocytosis regulator genes were identified, from which two disease subtypes (clusters 1 and 2) were classified. The prognoses of the patients in cluster 2 were significantly worse than those of the patients in cluster 1. The immune microenvironment differed significantly between the two subtypes. Finally, 10 genes (FAM81A, EZR, NDUFB9, RCOR1, FOXO4, NHLRC2, KIF23, PTPN6, SMAGP, and MED13) were selected to establish the prognostic risk score model. The prognosis in the low-risk group was better than that in the high-risk group. The model genes NDUFB9 and PTPN6 were positively correlated with M2 macrophages. Conclusion: Ten key phagocytosis regulator genes were screened to construct a prognostic model for MB. These genes may serve as key biomarkers for predicting the prognosis of patients with this type of brain cancer.

The Lofoten Vortex (LV), which is identified as a quasi-permanent anticyclonic eddy, strengthens through continuous merging with external anticyclonic eddies. Our investigation used the Lagrangian method to monitor the LV on a daily basis. Utilizing satellite altimeter data, we conducted multi-year tracking and statistical analysis of merging events involving the LV. The results indicate a characteristic radius of approximately 42.72 km and a mean vorticity at the eddy center of approximately −2.23 × 10−5 s−1. The eddy exhibits oscillatory motion within the sea basin depression, centered at 70°N, 3°E, characterized by counterclockwise trajectories between 0.5°E and 6°E and between 69°N and 70.5°N. There are two types of merging events: fusion events (55%), in which eddies of similar strengths interact within a closed flow line and then merge to form a new eddy; and absorption events (45%), in which the stronger LV absorbs the weaker anticyclonic eddies without destroying the structure of the LV itself. The nodes where strong vorticity advection occurs correspond to the nodes where merging occurs, suggesting that their effect on merging can be well characterized by the vorticity advection time series. We also observe occasional fluctuations and substitution events involving the LV and external anticyclonic eddies, suggesting a dynamic succession rather than a single vortex entity.

Natural gas hydrate (NGH) trials have been performed successfully with different development methods and gas recovery drainage technologies. Multiphase flow in a wellbore and the drainage of natural gas hydrate are two important parts for its whole extraction process. Additionally, the choice of the drainage method is linked to the development method, making the drainage of NGH more complex. Jet pump drainage is usable for NGH production wells with the combined depressurization and thermal stimulation method. The objective of this study is to shed more light on the multiphase flow behavior in jet pump drainage and NGH production wells and put forward suggestions for adjusting heat injection parameters. The mechanism of jet pump drainage recovery technology for NGH wells was analyzed and its applicability to NGH development by the combined depressurization and thermal stimulation method was demonstrated. In addition, multiphase flow models of tubing and annulus were established, respectively, for the phenomenon of the countercurrent flow of heat exchange in the process of jet pump drainage and gas production, and the corresponding multiphase flow laws were derived. On the basis of these studies, sensitivity analysis and the optimization of thermal stimulation parameters were conducted. It is demonstrated that jet pump drainage gas recovery technology is feasible for the development of onshore NGH with the combined depressurization and thermal stimulation method. The laws of multiphase flow in the tubing and annulus of jet pump drainage and NGH production wells were disclosed in this study. Numerical simulation results show that the temperature and pressure profiles along the wellbore of jet pump drainage and NGH production wells during the drainage recovery process are affected by injection conditions. Increasing injection rate and injection temperature can both improve the effect of heat injection and reduce the hydrate reformation risk in the bottom of the annulus. This study offers a theoretical basis and technical support for production optimization and hydrate prevention and control in the wellbore of jet pump drainage and NGH production wells.

Background and purpose: To examine the diagnostic value of imaging features in cavernous sinus hemangioma (CSH). Materials and Methods: The clinical and imaging data of patients with pathologically confirmed CSH, cavernous sinus meningioma, trigeminal schwannoma and pituitary adenoma invading the cavernous sinus between May 2017 and May 2022 were retrospectively analyzed. The cases were divided into the CSH and non-CSH groups to summarize the magnetic resonance imaging (MRI) characteristics of CSH. Univariate χ2 analysis was performed to assess five indexes, including signal intensity on T2WI, homogeneity of T2WI, enhancement of enhanced T1, enhanced T1 with dural tail sign, and cavernous sinus swelling and extrusion sign. Results: There were significant differences in four features, including hyperintensity on T2WI, homogeneity of T2WI, T1-enhanced without meningeal tail sign, and cavernous sinus swelling and extrusion sign between the CSH and non-CSH groups, with cavernous sinus swelling and extrusion sign showing the most pronounced distinction, with a sensitivity of 100%, a specificity of 93.02%, and an accuracy of 94.23%. The four features could be jointly used as diagnostic criteria, with a sensitivity of 94.44%, a specificity of 100.00%, and an accuracy of 99.04%. Conclusion: Cavernous sinus swelling and extrusion sign is a reliable imaging index for CSH diagnosis. Homogenous hyperintensity or marked hyperintensity on T2WI, enhanced T1 without dural tail sign, and cavernous sinus swelling and extrusion sign could be jointly used as diagnostic criteria, which may improve the accuracy of CSH diagnosis.

The marine environment of the Arctic Ocean has changed rapidly in recent decades. We used reanalysis data and observational data to explore the variations in the upper ocean heat content (OHC) of the Canadian Basin (CB) and the variations in the temperature profiles of the Southern Canadian Basin (SCB). Both the reanalysis data and observational data show increasing trends for the OHC of the CB from 1993 to 2023. Compared to the World Ocean Atlas data (WOA 18/23), the reanalysis data (ORAS5 or GLORYS12V1) significantly underestimated the values of the upper OHC of the Canadian Basin. To explain the OHC differences, the Ice-Tethered Profiler (ITP) observational data were used to analyze the variations in the vertical temperature profiles. We found that the reanalysis data remarkably underestimated the maximum temperatures of the subsurface Pacific warm water and its increasing trend. Based on the short-term prediction results from the Bi-LSTM neural network, we forecasted that the upper OHC will continue to increase in the SCB, mainly due to the warming of the intermediate Atlantic warm water. The research results provide a valuable reference for assessing and improving climate-coupled models.

The East Greenland Polar Front (EGPF) is an important front with strong salinity and temperature gradients in the Nordic Seas. It is formed by the interaction between Arctic-origin and Atlantic-origin water. The variations of EGPF are closely linked with sea ice melting and heat content transport associated with North Atlantic water recirculation. For a three-dimensional (3D) daily analysis, we use the global ocean eddy resolution reanalysis product (GLORYS12V1) from 1993 to 2018 to calculate the salinity and temperature horizontal gradient in the upper ocean and obtain the spatiotemporal distribution and intensity characteristics of EGPF. After assessment, the thresholds of the salinity and temperature fronts are set to 0.04 psu/km and 0.09°C/km, respectively. Compared with satellite observations of sea ice concentration, a significant spatial relationship is observed between the main position of EGPF and the ice edge before the sea ice shrinks to the continental shelf sea area. Affected by the freshening of the Arctic-origin water due to the melting of the sea ice, the intensity and area of EGPF show significant seasonal variations. Against the background of global warming, the sea ice area presents an obvious decreasing trend in the Greenland Sea. The melting of sea ice increased annually every summer from 1998 to 2018. The heat content transport of the Atlantic-origin water has also increased in recent years. The 3D characteristics (intensity and volume) of EGPF as salinity and temperature fronts exhibit increasing trends.

The Drake Passage is known for its abundant mesoscale eddies, but little is known about their three-dimensional characteristics, which hinders our understanding of their impact on eddy-induced transport and deep-sea circulation. A 10-year study was conducted using GLORYS12 Mercator Ocean reanalysis data from 2009 to 2018. The study analyzed the statistical characteristics of eddies in the Drake Passage, spanning from the surface down to a depth of 2000 m in three dimensions. The findings indicate that the mean radius of the eddies is 35.5 km, with a mean lifespan of 12.3 weeks and mean vorticity of 2.2 × 10−5 s−1. The eddies are most active and energetic near the three main fronts and propagate north-eastward at an average distance of 97.8 km. The eddy parameters vary with water depth, with more anticyclones detected from the surface to 400 m, displaying a larger radius and longer propagation distance. Cyclones have longer lifespans and greater vorticity. However, beyond 400 m, there is not much difference between anticyclones and cyclones. Approximately 23.3% of the eddies reach a depth of 2000 m, with larger eddies tending to penetrate deeper. The eddies come in three different shapes, bowl-shaped (52.7%), lens-shaped (27.1%) and cone-shaped (20.2%). They exhibit annual and monthly distribution patterns. Due to its high latitude location, the Drake Passage has strong rotation and weak stratification, resulting in the generation of small and deep-reaching eddies. These eddies contribute to the formation of Antarctic intermediate water and lead to modulation of turbulent dissipation.

Deep neural networks have recently demonstrated high performance for deblurring. However, few methods are designed for both non‐uniform image blur estimation and removal with highly efficient. In this study, the authors proposed a fully convolutional network that outputs estimated blur and restored image in one feed‐forward pass for the non‐uniformly blurred image of any input‐size. The proposed network contains two subnets. The parameter estimation subnet P‐net predicts pixel‐wise parameters of multiple blur types with high accuracy. The output of P‐net is used as a condition, which guides the blur removal subnet G‐net to restore a high quality latent sharp image. P‐net and G‐net are ultimately integrated into a single framework called PG‐net, which guarantees the consistency of parameter estimation and blur removal, thereby improves algorithm efficiency. Experiment results show that the authors blur parameter estimation method as well as their deblurring method outperforms the comparison methods both quantitatively and qualitatively.

Unconventional well technology is often used as one of the most important means to reduce costs and increase efficiency in oil fields. There are many methods for unconventional well type optimization, but there are relatively few studies on whether the well type is suitable for the reservoir. In this paper, a method for matching unconventional wells and reservoirs is established. In our method, we first simplify the reservoir and choose initial suitable well types based on experience and then use a semi-analytical model to calculate the production rate in different producing sections. After that, we define some parameters to evaluate the matching degree of well types and reservoirs. Finally, we determine whether these well types are suitable for the reservoir based on these parameters. At the end of the paper, we apply the method to a specific case. The result shows that a stepped well is suitable for exploiting thin interbed reservoirs, but it is necessary that the permeability and fluid viscosity in different layers are within a certain range, and the shape of the stepped well is also limited. This paper gives a specific value for this range and limitation. The method for matching unconventional wells and reservoirs proposed in this paper is helpful for guiding the selection of unconventional well types before drilling.

The Lofoten Vortex (LV) is a quasi-permanent anticyclonic eddy with the characteristic of periodic regeneration in the Lofoten Basin (LB), which is one of the major areas of deep vertical mixing in the Nordic Sea. Our analysis of the LV contributes to our understanding of the variations in convective mixing in the LB. Based on drifter data and satellite altimeter data, the climatological results show that the LV has the sea surface characteristics of relative stability in terms of its spatial position and significant seasonal variations in its physical characteristics. Combined with the temperature and salinity data of Argo profiles, the vertical structures of the LV are presented here in terms of their spatial distribution and monthly variations. The wavelet analysis of the satellite sea surface temperature (SST) data shows that the period of SST anomaly (SSTA) in the LV sea area is 8–16 years. In the stage marked by a decreasing (increasing) trend of SSTA, the vertical mixing is strengthened (weakened). Current vertical mixing is clearly revealed by the Argo profiles, and the SSTA shows a significant impact of cooling. However, against a background of warming and freshening, this vertical mixing will be greatly weakened in the next increasing trending stage of the SSTA.

Cross-slope eddies play an important role in the exchange of water, salt, heat, nutrients, chlorophyll, phytoplankton and other biogeochemical elements between basin and shelf in the South China Sea. The cross-slope transport process by a mesoscale anticyclonic eddy is studied by ROMS model and satellite data. The 1000 m isobath was considered as a proxy for the slope. The anticyclone shows different features at different places on the slope: (a) the volume transport at the northeast of the slope was off-slope, while at the southwest was on-slope; (b) both on and off-slope transports were greatly enhanced during the cross-slope process, and gradually weakened after crossing the slope. The total cross-slope water transport was 5.97 Sv, which was higher than the along-slope component with −0.58 Sv. The Eulerian results also showed that enhanced cross-slope transport was related to the distance between the eddy and slope, the eddy radius, and the eccentricity of the eddy. The offline passive tracer experiment showed that particles were floating up during and after the crossing process, mainly due to the strong Ekman pumping.

With the availability of petabytes of oceanographic observations and numerical model simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of applications. In this paper, these applications are reviewed from the perspectives of identifying, forecasting, and parameterizing ocean phenomena. Specifically, the usage of AI algorithms for the identification of mesoscale eddies, internal waves, oil spills, sea ice, and marine algae are discussed in this paper. Additionally, AI-based forecasting of surface waves, the El Niño Southern Oscillation, and storm surges is discussed. This is followed by a discussion on the usage of these schemes to parameterize oceanic turbulence and atmospheric moist physics. Moreover, physics-informed deep learning and neural networks are discussed within an oceanographic context, and further applications with ocean digital twins and physics-constrained AI algorithms are described. This review is meant to introduce beginners and experts in the marine sciences to AI methodologies and stimulate future research toward the usage of causality-adherent physics-informed neural networks and Fourier neural networks in oceanography.

Deep learning methods have excellent prospects for application in wave forecasting research. This study employed the convolutional LSTM (ConvLSTM) algorithm to predict the South China Sea (SCS) significant wave height (SWH). Three prediction models were established to investigate the influences of setting different parameters and using multiple training data on the forecasting effects. Compared with the SWH data from the China–France Ocean Satellite (CFOSAT), the SWH of WAVEWATCH III (WWIII) from the pacific islands ocean observing system are accurate enough to be used as training data for the ConvLSTM-based SWH prediction model. Model A was preliminarily established by only using the SWH from WWIII as the training data, and 20 sensitivity experiments were carried out to investigate the influences of different parameter settings on the forecasting effect of Model A. The experimental results showed that Model A has the best forecasting effect when using three years of training data and three hourly input data. With the same parameter settings as the best prediction performance Model A, Model B and C were also established by using more different training data. Model B used the wind shear velocity and SWH as training and input data. When making a 24-h SWH forecast, compared with Model A, the root mean square error (RMSE) of Model B is decreased by 17.6%, the correlation coefficient (CC) is increased by 2.90%, and the mean absolute percentage error (MAPE) is reduced by 12.2%. Model C used the SWH, wind shear velocity, wind and wave direction as training and input data. When making a 24-h SWH forecast, compared with Model A, the RMSE of Model C decreased by 19.0%, the CC increased by 2.65%, and the MAPE decreased by 14.8%. As the performance of the ConvLSTM-based prediction model mainly rely on the SWH training data. All the ConvLSTM-based prediction models show a greater RMSE in the nearshore area than that in the deep area of SCS and also show a greater RMSE during the period of typhoon transit than that without typhoon. Considering the wind shear velocity, wind, and wave direction also used as training data will improve the performance of SWH prediction.

The ocean chlorophyll-a (Chl-a) concentration is an important variable in the marine environment, the abnormal distribution of which is closely related to the hazards of red tides. Thus, the accurate prediction of its concentration in the East China Sea (ECS) is greatly important for preventing water eutrophication and protecting the coastal ecological environment. Processed by two different pre-processing methods, 10-year (2011–2020) satellite-observed chlorophyll-a data and logarithmic data were used as the long short-term memory (LSTM) neural network training datasets in this study. The 2021 data were used for comparison to prediction results. The past 15 days’ data were used to predict the concentration of chlorophyll-a for the five following days. Results showed that the predictions obtained by both pre-processing methods could simulate the seasonal distribution of the Chl-a concentration in the ECS effectively. Moreover, the prediction performance of the model driven by the original values was better in the medium- and low-concentration regions. However, in the high-concentration region, the prediction of extreme concentrations by the two data-driven LSTM models showed underestimation, considering that the prediction performance of the model driven by the original values was better. Results of sensitivity experiments showed that the prediction accuracy of the model decreased considerably when the backward prediction time step increased. In this study, the neural network was driven only by chlorophyll-a, whose concentration in the ECS was forecasted, and the effect of other relevant marine elements on Chl-a was not considered, which is the current weakness of this study.

Using the 13 m diameter LEGI-Coriolis rotating platform, the evolution processes of a generated anticyclonic eddy throughout its lifecycle are analyzed. Experimental results have shown that the eddy lasted for approximately 3T0, where T0 is the rotating period of 90 s. After T = 0.3T0, the eddy enters its mature phase, whereby following this event, eddy intensity slowly decreases from its maximum rotation speed. By T = 2.6T0, the eddy enters a stage of rapid weakening. In its decay period, two underlying mechanisms for this decay have been identified as inertial instability and eddy–eddy interactions.

Sea surface temperature (SST) is an important physical factor in the interaction between the ocean and the atmosphere. Accurate monitoring and prediction of the temporal and spatial distribution of SST are of great significance in dealing with climate change, disaster prevention, disaster reduction, and marine ecological protection. This study establishes a prediction model of sea surface temperature for the next five days in the East China Sea using long-term and short-term memory neural networks (LSTM). It investigates the influence of different parameters on prediction accuracy. The sensitivity experiment results show that, based on the same training data, the length of the input data of the LSTM model can improve the model’s prediction performance to a certain extent. However, no obvious positive correlation is observed between the increase in the input data length and the improvement of the model’s prediction accuracy. On the contrary, the LSTM model’s performance decreases with the prediction length increase. Furthermore, the single-point prediction results of the LSTM model for the estuary of the Yangtze River, Kuroshio, and the Pacific Ocean are accurate. In particular, the prediction results of the point in the Pacific Ocean are the most accurate at the selected four points, with an RMSE of 0.0698 °C and an R2 of 99.95%. At the same time, the model in the Pacific region is migrated to the East China Sea. The model was found to have good mobility and can well represent the long-term and seasonal trends of SST in the East China Sea.

Cerebral cavernous malformations (CCMs) are common vascular malformations in the central nervous system. Familial CCMs (FCCMs) are autosomal dominant inherited disease with incomplete penetrance and variable symptoms. Mutations in the KRIT1, CCM2, and PDCD10 genes cause the development of FCCM. Approximately 476 mutations of three CCM-related genes have been reported, most of which were case reports, and lack of data in stable inheritance. In addition, only a small number of causative missense mutations had been identified in patients. Here, we reported that 8/20 members of a Chinese family were diagnosed with CCMs. By direct DNA sequencing, we found a novel variant c.331G > C (p.A111P) in exon 4 of the CCM2 gene, which was a heterozygous exonic variant, in 7/20 family members. We consider this variant to be causative of disease due to a weaken the protein–protein interaction between KRIT1 and CCM2. In addition, we also found the exon 13 deletion in KRIT1 coexisting with the CCM2 mutation in patient IV-2, and this was inherited from her father (patient III-1H). This study of a Chinese family with a large number of patients with CCMs and stable inheritance of a CCM2 mutation contributes to better understanding the spectrum of gene mutations in CCMs.

Aiming at the problems of the current production and operation status of the progressive cavity pump (PCP) in coalbed methane (CBM) wells which cannot be timely monitored, quantitatively evaluated, and accurately predicted, a five-step method for evaluating and predicting the health status of PCP wells is proposed: data preprocessing, principal parameter optimization, health index construction, health degree division, and health index prediction. Therein, a health index (HI) formulation was made based on deep learning, and a statistical method was used to define the health status of PCP wells as being healthy, subhealthy, or faulty. This allowed further research on the HI prediction model of PCP wells based on the long short-term memory (LSTM) network. As demonstrated in the study, they can reflect both the change trend and the contextual relevance of the health status of PCP wells with high accuracy to achieve real-time, quantitative, and accurate assessment and prediction. At the same time, the conclusion gives good guidance on the production performance analysis and failure warning of the PCP wells and suggests a new direction for the health status assessment and warning of other artificial lift equipment.

Multistage hydraulic fracturing is one of the most prevalent approaches for shale reservoir development. Due to the complexity of constructing reservoir environments for experiments, numerical simulation is a vital method to study flow behavior under reservoir conditions. In this paper, we propose a numerical model that considers a multistage fractured horizontal well with a hilly-terrain trajectory in a shale reservoir with the presence of natural fractures. The model was constructed based on the MATLAB Reservoir Simulation Toolbox and used the Embedded Discrete Fractured Model (EDFM) to describe the interrelationship between the matrix, fractures, and wellbore. The model was then applied to an actual condensate gas well producing from a shale reservoir, and the effects of reservoir parameters on the simulation data were studied based on this well case. The simulation results were highly consistent with the actual production data, which validates the accuracy of this model and proves its potential for predicting future production trends. We extended the discussion to two examples with extreme well trajectories by reviewing the inflow contribution of each hydraulic fracture with respect to fracture pressure, and the changes in static pressure with time. In conclusion, the proposed model is capable of providing simulation results close to reality and thus guiding field design and operation in the fracturing and drilling process.

Although currently, large-scale and multilateral horizontal wells are an important way to improve the oil recovery in the unconventional reservoirs, the flow behavior of fluid from the reservoir into the horizontal wellbore becomes more challenged compared to the single small-scale horizontal well. One of the main challenges is that pressure loss from the well completion section and wellbore cannot be ignored in the coupling process between the reservoir and the horizontal well. In this paper, a new method is presented to solve the coupling flow between the reservoir and the horizontal well with different well completions. The new coupling model is compared with Ouyang’s model (1998) and Penmatcha’s model (1997), and the predicted data are consistent with each other at both early and late times. Meanwhile, four different cases have been proposed to verify the application of the new coupling model with different well completions, and the results indicate that the uneven inflow profile can be effectively alleviated via reasonable completion parameters and different well completions. Based on two types of flow-node units, it can quickly model and solve the coupling problem between the reservoir and the horizontal well with complex completion cases. It can also depict the inflow profile of the horizontal well with different well completions, which is conducive to understand the coupling process. The new coupling model can provide theoretical support for further optimization of completion parameters and well completions and finally improve oil recovery.

Based on both satellite remote sensing sea surface temperature (SST) data and numerical model results, SST warming differences in the Mozambique Channel (MC) west of the Madagascar Island (MI) were found with respect to the SST east of the MI along the same latitude. The mean SST west of the MI is up to about 3.0 °C warmer than that east of the MI. The SST differences exist all year round and the maximum value appears in October. The area of the highest SST is located in the northern part of the MC. Potential factors causing the SST anomalies could be sea surface wind, heat flux and oceanic flow advection. The presence of the MI results in weakening wind in the MC and in turn causes weakening of the mixing in the upper oceans, thus the surface mixed layer depth becomes shallower. There is more precipitation on the east of the MI than that inside the MC because of the orographic effects. Different precipitation patterns and types of clouds result in different solar radiant heat fluxes across both sides of the MI. Warm water advected from the equatorial area also contribute to the SST warm anomalies.

Emerging evidence indicates that human mesenchymal stem cells (hMSCs) can be recruited to tumor sites, and affect the growth of human malignancies. However, little is known about the underlying molecular mechanisms. Here, we observed the effects of hMSCs on the human cholangiocarcinoma cell line, HCCC-9810, using an animal transplantation model, and conditioned media from human umbilical cord-derived mesenchymal stem cells (hUC-MSCs). Animal studies showed that hUC-MSCs can inhibit the growth of cholangiocarcinoma xenograft tumors. In cell culture, conditioned media from hUC-MSCs inhibited proliferation and induced apoptosis of tumor cells in a dose- and time-dependent manner. The proliferation inhibition rate increased from 6.21% to 49.86%, whereas the apoptosis rate increased from 9.3% to 48.1% when HCCC-9810 cells were cultured with 50% hUC-MSC conditioned media for 24 h. Immunoblot analysis showed that the expression of phosphor-PDK1 (Ser241), phosphor-Akt (Ser 437 and Thr308), phosphorylated glycogen synthase kinase 3β (phospho-GSK-3β(Ser9)), β-catenin, cyclin-D1, and c-myc were down-regulated. We further demonstrated that CHIR99021, a GSK-3β inhibitor reversed the suppressive effects of hUC-MSCs on HCCC-9810 cells and increased the expression of β-catenin. The GSK-3β activator, sodium nitroprusside dehydrate (SNP), augmented the anti-tumor effects of hUC-MSCs and decreased the expression of β-catenin. IGF-1 acted as an Akt activator, and also reversed the suppressive effects of hUC-MSCs on HCCC-9810 cells. All these results suggest that hUC-MSCs could inhibit the malignant phenotype of HCCC-9810 human cholangiocarcinoma cell line. The cross-talk role of Wnt/β-catenin and PI3K/Akt signaling pathway, with GSK-3β as the key enzyme bridging these pathways, may contribute to the inhibition of cholangiocarcinoma cells by hUC-MSCs.

To study the effects of recombinant human erythropoietin on inflammatory factors in rats with traumatic brain injury is the main objective. A total of 45 specific-pathogen-free grade male Sprague-Dawley rats were randomly assigned into sham operation group (sham group), model group and recombinant human erythropoietin intervention group (treatment group) (n=15). Model and treatment groups were prepared into traumatic brain injury model by hitting the head through the modified Feeney's free-fall impact method, while the head of sham group was not hit. After modeling, treatment group was intraperitoneally injected with recombinant human erythropoietin at 5000 IU/kg daily and sham and model groups were intraperitoneally injected with the same dose of normal saline. The rats were killed after 7 d of continuous administration. The changes of brain mitochondrial membrane potential were detected through rhodamine 123 staining and immunocytochemistry and Western blotting were separately employed to measure the expressions of interleukin-1ß, interleukin-6 and tumor necrosis factor-a in brain tissues and the expression levels of dynamin-related protein 1, fission 1, mitofusin 2 and optic atrophy 1, mitochondrial dynamics related proteins in brain tissues. Compared with sham group, model group exhibited significantly weakened rhodamine 123 fluorescence intensity, increased expressions of interleukin-1ß, interleukin-6 and tumor necrosis factor-a, dynamin-related protein 1 and fission 1 and reduced expressions of mitofusin 2 and optic atrophy 1 in brain tissues (p<0.05). In comparison with model group, treatment group had significantly enhanced rhodamine 123 fluorescence intensity, reduced expressions of interleukin-1ß, interleukin-6 and tumor necrosis factor-a, dynamin-related protein 1 and fission 1 and elevated expressions of mitofusin 2 and optic atrophy 1 in brain tissues (p<0.05). Recombinant human erythropoietin can protect the brain after traumatic brain injury by relieving the inflammatory response and mitochondrial injury after traumatic brain injury. [ABSTRACT FROM AUTHOR]