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Background: Distributed wind farms cover a wide area, the internal units are far apart, and the output correlation is low. With the gradual expansion of its development scale, accurate prediction of distributed wind power has become an important means to ensure the smooth operation of the power system. Objective: This paper proposes a decentralized wind power forecasting method based on cluster analysis and multi-model stacking ensemble learning. Method: Firstly, the average output model of multiple units is established through cluster analysis to eliminate redundant and repetitive features; secondly, a feature complex model is established to construct multi-dimensional combined features to fully extract feature information; then, an improved Stacking integrated learning model is proposed. Sets and model sets, while reducing the amount of model calculation, give full play to the feature adaptability of different models. Results: The results of the proposed model are analyzed by comparison with traditional machine learning models such as SVM. Root Mean Square Error (RMSE) can be reduced by up to 6.3% Conclusion: The results show that, compared with traditional single-model forecasting, the decentralized wind power forecasting method based on cluster analysis and multi-model stacking ensemble learning has higher forecasting accuracy.

Background: With the access to large-scale distributed energy, the voltage off-limit problem of the distribution network has become more and more serious, and the traditional centralized voltage control method has been difficult to meet the demand of distribution network control and market development. Methods: In this paper, a voltage regulation strategy based on decentralized wind power clustering is proposed. Considering the active and reactive power regulation ability of decentralized wind power, based on the cluster division of distributed energy, the goal is to minimize power regulation and voltage fluctuation within the cluster. Combining the K-means clustering algorithm and optimized PSO algorithm for voltage regulation within the cluster ensures that the voltagecrossing problem is solved. After the voltage regulation of all clusters, the auxiliary service transaction of voltage regulation is implemented to complete the voltage regulation of the whole network. Results: Taking ieee33 bus system as an example, the MATLAB simulation shows that the decentralized wind power access triggered voltage overrun. Using the proposed cluster division method to cluster the model, the clustered voltage regulation of the nodes with voltage overrun or reaching the limit resulted in greater voltage down-regulation than the overall voltage regulation. Conclusion: The proposed voltage regulation strategy has better advantages. It is verified that the proposed voltage regulation strategy not only solves the voltage overrun problem, but also reduces voltage fluctuations and the amount of power regulation used for voltage regulation, increasing the utilization of distributed energy with better superiority.

Background: Improving the accuracy of decentralized wind power forecasting has become an important means to ensure the safe and stable operation of the power grid. Traditional prediction methods cannot meet the requirements of precise wind power prediction for long time series Methods: A decentralized wind power prediction model based on Informer is established. Introducing the model's sparse self-attention mechanism, self-attention distillation mechanism and The generative decoder model realizes the long-term sequence power prediction of decentralized wind power. Results: The results of the proposed model are analyzed by comparing it with traditional neural network models such as LSTM and feed-forward neural networks. The root mean square error (RMSE) can be reduced by up to 6.3% Conclusion: It is proved that the Informer model prediction has a lower prediction error and performs better in long-term sequence power pre-tasks.

Tanzanite is a blue-purple zoisite currently produced only in Tanzania. Tanzanite is loved by the public for its attractive blue-violet color. In this study, the color appearance of tanzanite will be studied chromatically using the CIE 1976 L*a*b* unified color space and the CIECAM16 color appearance model, respectively. The blue color of tanzanite is quantitatively characterized in CIE 1976 L*a*b* uniform color space using an X-Rite SP62 spectrophotometer. The tristimulus values XYZ of tanzanite’s color are input into the CIECAM16 forward model to calculate the color appearance parameters of tanzanite under different light sources, different illumination levels, and different surroundings. And the results show that D65 light source is more suitable for the display of tanzanite. The change in illumination will have a significant effect on Brightness (Q). Darkening of the surroundings will lead to an increase in the lightness of tanzanite. The blue-violet color of tanzanite has a better visual effect when observed in darker conditions.

To explore the clinical effects of total laparoscopic radical gastrectomy under the guidance of the concept of enhanced recovery after surgery (ERAS). Fifty-five patients were perioperatively treated under the concept of ERAS (ERAS group), while the remaining 55 patients were treated under the traditional perioperative concept (control group). The operation time, intraoperative blood loss, the time of first anal exhaust and first postoperative off-bed activity, postoperative length of stay, and incidence of postoperative complications were recorded in both groups. The pain of patients was assessed using VAS system. The nausea and vomiting and abdominal distension were assessed using the NVDS and abdominal distension score, respectively, within 24 h after operation. The patient’s daily living ability was evaluated by the ADL scale at 3 d after the operation. The time of first anal exhaust, the time of first postoperative off-bed activity time, and the postoperative in-hospital time were all significantly shorter in the ERAS group than those in the control group ( P P P P

Reservoir simulation is essential for various reservoir engineering processes such as history matching and field development plan optimization but is typically an intensive and time-consuming process. The aim of this study is to compare various deep-learning algorithms for constructing a machine-learning (ML) proxy model, which reproduces the behavior of a reservoir simulator and results in significant speedup compared to running the numerical simulator. Initially, we generate an ensemble of realizations via the reservoir simulator to train the different ML algorithms. The data set consists of a comprehensive set of uncertainty parameters and the corresponding simulation data across all wells. The system utilizes recent advances in deep learning based on deep neural networks, convolutional neural networks, and autoencoders to create machine-learning-based proxy models that predict production and injection profiles as well as the bottomhole pressure of all wells. Thus, the proposed workflows replace the time-consuming simulation process with fast and efficient proxy models. In this work we provide a comparative study of various ML-based algorithms utilizing deep neural networks and convolutional neural networks for constructing a surrogate reservoir model. The trained models can simulate the behavior of the physics-based reservoir simulator by correlating uncertainty parameters to various history-matched reservoir properties. The algorithms were tested on a mature oilfield with a notable number of wells and several decades of production and injection data. We analyze the performance of each ML approach and provide recommendations on the optimal one. The best performing workflow for building the ML proxy model consists of two steps. The first step uses stacked autoencoders to learn a low-dimensional latent space representation of the highly dimensional simulation data. This step allows to reduce the complexity of predicting the simulation data and enhances the prediction quality. The following step constructs an ML model to predict the latent space features from input uncertainty parameters and produces highly accurate results. Reservoir simulation is of paramount importance for various reservoir engineering workflows. Traditional approaches require running physics-based simulators for multiple iterations, which results in time-consuming and labor-intensive processes. We implement and compare several deep-learning-based methods to construct ML proxy models that automate and remarkably reduce the runtime of the reservoir simulation process.

In recent years, with the rapid development of renewable energy, the penetration rate of renewable energy generation in the active distribution network (ADN) has increased. Because of the instability of renewable energy generation, the operation stability of ADN has decreased. Due to the ability to cut peak load and fill valley load, battery energy storage systems (BESSs) can enhance the stability of the electric system. However, the placement and capacity of BESSs connected to ADN are extremely significant, otherwise, it will lead to a further decline in the stability of ADN. To ensure the effectiveness of the BESSs connected to the grid, this work uses the fuzzy kernel C-means (FKCM) method for scene clustering. Meanwhile, a multi-objective optimization model of BESS configuration is established with the objective of BESS configuration cost, voltage fluctuation, and load fluctuation, and solved by non-dominated sorting genetic algorithm-II (NSGA-II). In this work, the grey target decision method based on the entropy weight method (EWM) is used to obtain the optimal compromise solution from the Pareto non-dominated set. Moreover, the proposed method is tested and verified in the extended IEEE-33 node system and the extended IEEE-69 node system. The results show that the BESSs configuration scheme obtained by NSGA-II can effectively reduce the fluctuation of voltage and load, and improve the stability of ADN operation.

Multi-User Multiple-Input and Multiple-Output (MU-MIMO) is a technique that allows concurrent transmissions between one access point (AP) and multiple clients to improve spectral efficiency. In practice, however, the MU-MIMO is sensitive to client mobility and is sometimes even harmful to the performance in networks with moving clients. In this paper, we identify that it is essential to optimize the MUMIMO performance with moving clients by jointly selecting the sounding period, the number of spatial streams, and client grouping with the consideration of the client density of the network. We develop a data-driven model that estimates client throughput with the consideration of these parameters, as well as an algorithm that jointly determines the parameters for each client with low computational complexity. Using a commodity 802.11ax network, we experimentally demonstrate the significant impact of the key factors on MU-MIMO performance. Based on experimental data, we develop an emulation model to evaluate network performance with different client densities and mobility. Emulation results show that our proposed algorithm outperforms conventional schemes by over 20% in MU-MIMO networks with moving clients.

Background Slow delayed rectifier potassium current (IKs) is an important component of repolarization reserve during sympathetic nerve excitement. However, little is known about age-related functional changes of IKs and its involvement in age-dependent arrhythmogenesis. Objective The purpose of this study was to investigate age-related alteration of the IKs response to β-adrenergic receptor (βAR) activation. Methods Dunkin–Hartley guinea pigs were used. Whole-cell patch-clamp recording was used to record K+ currents. Optical mapping of membrane potential was performed in ex vivo heart. Results There was no difference in IKs density in ventricular cardiomyocytes between young and old guinea pigs. However, in contrast to IKs potentiation in young hearts, isoproterenol (ISO) evoked an acute inhibition on IKs in a concentration-dependent manner in old guinea pig hearts. The β2AR antagonist, but not β1AR antagonist, reversed the inhibitory response. Preincubation of cardiomyocytes with the inhibitory G protein (Gi) inhibitor pertussis toxin (PTX) also reversed the inhibitory response. In HEK293 cells cotransfected with cloned IKs channel and β2AR, ISO enhanced the current but reduced it when cells were cotransfected with Gi2, and PTX restored the ISO-induced excitatory response. Moreover, in aging cardiomyocytes, Gβγ inhibitor gallein, PLC inhibitor U73122, or protein kinase C inhibitor Bis-1 prevented the reduction of IKs by ISO. Furthermore, cardiac-specific Gi2 overexpression in young guinea pigs predisposed the heart to ventricular tachyarrhythmias. PTX pretreatment protected the hearts from ventricular arrhythmias. Conclusion βAR activation acutely induces an inhibitory IKs response in aging guinea pig hearts through β2AR-Gi signaling, which contributes to increased susceptibility to arrhythmogenesis in aging hearts.

Objective: To develop an automated method for 3D magnetic resonance (MR) vessel wall image analysis to facilitate morphologic quantification of intra- and extracranial arteries, including vessel centerline tracking, vessel straightening and reformation, vessel wall segmentation based on convoluted neural networks (CNNs), and morphological measurement. Methods: MR vessel wall images acquired using DANTE-SPACE sequences and corresponding time-of-flight-MRA images of 67 subjects (including 47 healthy volunteers and 20 patients with atherosclerosis) were included in this study. The centerline of the vessel was firstly extracted from the MRA images and copyed to the 3D MR vessel wall images through the registration relationship between the MRA images and the MR vessel wall images to extract, straighten, and reconstruct interested vessel segments into 2D slices. Then a complete CNN-based Unet-like method was used to automatically segment the vessel wall to obtain quantitative morphological measurements such as maximum wall thicknesses and normalized wall index (NWI). Results: The proposed automatic segmentation network was trained and validated with 11,735 slices and tested on 2517 slices. The method showed satisfactory agreement with manual segmentation method. The Dice coefficients of intracranial arteries were 0.90 for lumen and 0.78 for vessel wall, while the Dice coefficients of extracranial arteries were 0.90 for lumen and 0.82 for vessel wall. The maximum wall thickness and NWI obtained from the proposed automatic method were slightly larger than those obtained from the manual method for both intra- and extracranial arteries. However, there was no significant difference of the quantitative measurements between the two methods (p > 0.05). In addition, the automatically measured NWI of plaque slice was significantly larger than that of normal slice. Conclusion: We propose an automatic analysis method of MR vessel wall images, which can realize automatic segmentation of intra- and extracranial vessel wall. It is expected to facilitate large-scale arterial vessel wall morphological quantification. Advances in knowledge: We have proposed an automatic method for analysis of intra- and extracranial MR vessel wall images simultaneously based on CNN, which can facilitate large-scale quantitative analyses of vessel walls.

Capturing fluid composition variation and distribution in the reservoir is an essential first step for reservoir modeling. Vertical fluid composition variation is commonly considered. However, fluid samples taken from wells in different areas of a reservoir can highlight significant areal variation in the composition to be modeled. Fluid contacts may be tilted, and despite many studies on the subject, the setup is not straightforward. The combination of all three makes the initialization challenging and time consuming. In this paper we describe an automated workflow that integrates fluid sample data and petrophysical data to generate an initial fluid composition distribution that captures vertical and areal composition variations and accurately computes the initial fluid distribution to represent tilted contact configurations and their associated transition zones. Fluid sample data such as composition, pressure, temperature, and sampling depth are provided to an engine that computes composition variation with depth for each sample based on the equation of state (EOS) that characterizes the fluid behavior of the reservoir. The generated composition variation with depth for each sample is spatially distributed at their associated wells and is used to compute the areal composition distribution between the wells in the reservoir. This results in a tridimensional distribution of each fluid component representing the fluid model that captures both vertical and areal composition variations. In parallel, saturation height functions and hysteresis models are used to automatically generate drainage capillary pressure data and associated imbibition and scanning curves used to capture the drainage and imbibition processes responsible for the paleo and current water saturation distribution in the reservoir. The fully automated reservoir initialization process accounts for all available pressure-volume-temperature (PVT) samples. The solution is portable, significantly faster, and accurately captures complex reservoir geology and reservoir history. We present field examples of the proposed approach and illustrate its flexibility and associated comprehensiveness and efficiency. The complete automation of complex initialization methods considering areal and vertical composition variation, combined with tilted contacts modeling, is helping to resolve significant challenges faced across the industry.

Infill well placement performed as part of field-development planning is traditionally performed by identifying areas of high remaining mobile hydrocarbons and good reservoir rock quality to be targeted. The identification of hotspots was also largely performed on single-model realizations and, therefore, not robust considering the reservoir characterization uncertainties. Increasing efforts were put into incorporating the uncertainties as a key element of the infill well placement workflow by computing probability maps to identify the hotspots with higher chances of success for infill production. The maps were still generated solely based on dynamic reservoir-simulation model results. In this paper we present an intelligent workflow that integrates the opportunity index probability maps concept derived exclusively from dynamic reservoir simulation models, with drilling risk maps derived from drilling data analysis and completions quality maps derived from geomechanical studies, and artificial-intelligence-driven reservoir target classification. The integration provides more depth in the hotspot selection by identifying the most productive and feasible locations for infill drilling. The locations are then used for well placement and trajectory design optimization. The well trajectories optimize factors in the hotspot locations, locations of existing drilling centers, surface topology for new drilling centers to be designed, numbers of available slots on each drilling center, and capital costs such as drilling economics and drilling center cost. Infill injection wells are placed in conjunction with the infill production wells either following a pattern-type of design or peripheral injection. The designed wells are evaluated via an automated pipeline using reservoir simulation where the set of wells will be tested against the ensemble of realizations under uncertainty. A probabilistic approach is taken for the infill well performance and the economics evaluation for candidate screening and selection for the field-development plan optimization. This approach provides higher confidence in the decision making through the early integration of drilling risks and geomechanics data, and provides a more robust assessment of the technical and economic performance of the proposed infill wells under uncertainty. The solution combines various concepts including opportunity index, advanced ML methods for target identification, as well as multidisciplinary integration for well target identification. Well trajectory design evaluation considering both production and injection wells and the evaluation of the performance of the proposed candidates under uncertainty in this context provides more robust results under uncertainty compared to widely used industry practices that lack integration and uncertainty considerations.

Nanodiamonds are interesting materials from the point of view of their biocompatibility and their chemical, spectroscopic, and mechanical properties. Current synthetic methods for nanodiamonds involve harsh environments, which are potentially hazardous in addition to being expensive. We report a low-temperature (423 K) hydrothermal approach to form nanodiamonds by using graphene-oxide or nitrated polycyclic aromatic hydrocarbons (naphthalene, anthracene, phenanthrene, or pyrene) as a starting material. The reaction products contain single-crystalline or twinned nanodiamonds with average diameters in the 2-3 nm range. Theoretical calculations prove that, at the nanoscale, sub-4 nm nanodiamonds may adopt a structure that is more stable than graphene-oxide and nitrated polycyclic aromatic hydrocarbons. Our findings show that sp2 carbon in the polycyclic aromatic precursor can be converted to sp3 carbon under unexpectedly moderate temperature conditions by using nanoscale precursors and thus offer a low-temperature approach for the synthesis of sub-4 nm nanodiamonds.

As governments of various countries attach importance to green transportation, governments have focused their attention to understanding how they can shift the cargo shipping in coastal areas from ...

With the large-scale development of renewable energy sources such as wind and solar energy, the grid connection of renewable energy sources poses a certain threat to the security and stability of the power system, and also brings a great challenge to the economic dispatch of the distribution network. Traditional and single dispatching methods such as load demand response or network reconfiguration alone cannot meet the needs of a safe and economic operation of the distribution network. This article proposes an economic dispatching method for distribution networks with dispersed wind power considering network reconfiguration, and establishes an economic dispatching model with the objective function of minimizing distribution network operation cost, reconfiguration cost, and total system network loss. Based on the optimal scheduling of energy storage and reactive power compensation devices in the distribution network and the full utilization of demand response, a mixed integer second-order cone programming (MISOCP) method with multi-objective collaborative optimization is proposed. The results of the tests using the IEEE33 node system verify the feasibility and applicability of the proposed method in this article.

BackgroundRadiation-induced lung injury (RILI) is a severe side effect of radiotherapy for non-small cell lung cancer (NSCLC) ,and one of the major hindrances to improve the efficacy of radiotherapy. Previous studies have confirmed that sodium butyrate (NaB) has potential of anti-radiation toxicity. However, the mechanism of the protective effect of NaB against RILI has not yet been clarified. This study aimed to explore the underlying protective mechanisms of NaB against RILI in NSCLC through network pharmacology, molecular docking, molecular dynamic simulations and in vivo experiments.MethodsThe predictive target genes of NaB were obtained from the PharmMapper database and the literature review. The involved genes of RILI and NSCLC were predicted using OMIM and GeneCards database. The intersectional genes of drug and disease were identified using the Venny tool and uploaded to the Cytoscape software to identify 5 core target genes of NaB associated with RILI. The correlations between the 5 core target genes and EGFR, PD-L1, immune infiltrates, chemokines and chemokine receptors were analyzed using TIMER 2.0, TIMER and TISIDB databases. We constructed the mechanism maps of the 3 key signaling pathways using the KEGG database based on the results of GO and KEGG analyses from Metascape database. The 5 core target genes and drug were docked using the AutoDock Vina tool and visualized using PyMOL software. GROMACS software was used to perform 100 ns molecular dynamics simulation. Irradiation-induced lung injury model in mice were established to assess the therapeutic effects of NaB.ResultsA total of 51 intersectional genes involved in NaB against RILI in NSCLC were identified. The 5 core target genes were AKT1, TP53, NOTCH1, SIRT1, and PTEN. The expressions of the 5 core target genes were significantly associated with EGFR, PD-L1, immune infiltrates, chemokines and chemokine receptors, respectively. The results from GO analysis of the 51 intersectional genes revealed that the biological processes were focused on the regulation of smooth muscle cell proliferation, oxidative stress and cell death, while the three key KEGG pathways were enriched in PI3K-Akt signal pathway, p53 signal pathway, and FOXO signal pathway. The docking of NaB with the 5 core target genes showed affinity and stability, especially AKT1. In vivo experiments showed that NaB treatment significantly protected mice from RILI, with reduced lung histological damage. In addition, NaB treatment significantly inhibited the PI3K/Akt signaling pathway.ConclusionsNaB may protect patients from RILI in NSCLC through multiple target genes including AKT1, TP53, NOTCH1, SIRT1 and PTEN, with multiple signaling pathways involving, including PI3K-Akt pathway, p53 pathway, and FOXO pathways. Our findings effectively provide a feasible theoretical basis to further elucidate the mechanism of NaB in the treatment of RILI.

[Image: see text] We report herein the discovery of quinazolindiones as potent and selective tankyrase inhibitors. Elucidation of the structure–activity relationship of the lead compound 1g led to truncated analogues that have good potency in cells, pharmacokinetic (PK) properties, and excellent selectivity. Compound 21 exhibited excellent potencies in cells and proliferation studies, good selectivity, in vitro activities, and an excellent PK profile. Compound 21 also inhibited H292 xenograft tumor growth in nude mice. The synthesis, biological, pharmacokinetic, in vivo efficacy studies, and safety profiles of compounds are presented.

Multi-user MIMO (MU-MIMO) is a technique that improves spectral efficiency by allowing concurrent communication between one access point (AP) and multiple clients. In practice, the expected gain is not always achieved and is sometimes even negative. We experimentally demonstrate that the downlink MU-MIMO performance in a practical network not only depends on the client’s channel but is also influenced by factors that are not captured by conventional models, such as client motion and device type. We propose a data-driven algorithm with a low computational complexity that determines whether a client should operate in MU mode and the MU-MIMO group for clients in MU mode. Such a mode and group selection algorithm is based on a sequence of channel state information (CSI), SNR, and client device type. The algorithm can automatically adapt to the motion and characteristics of individual clients. Experimental results using implementation on a commodity 802.11ac AP show that the proposed data-driven mode and group selection algorithm can improve network throughput by up to 35% over existing algorithms based on conventional models. We also show that the proposed data-driven algorithm has limited sensitivity to environmental changes and can be deployed into new environments without retraining.

Purpose Stereotactic body radiotherapy (SBRT) may have significant immunomodulatory effects that enhance tumor response to immune checkpoint inhibitors. This phase 2 clinical trial was conducted to evaluate the safety and efficacy of combining palliative SBRT with camrelizumab (an anti-PD1 monoclonal antibody) in patients with unresectable hepatocellular carcinoma (uHCC). Methods Patients with uHCC, Child–Pugh A/B liver function, and at least one measurable lesion were enrolled between April 2020 and August 2022. Patients were administered 200 mg camrelizumab intravenously from the first day of palliative SBRT and then every 3 weeks. Palliative SBRT was delivered daily over five fractions per week, with a dose range of 30–50 Gy. The primary endpoints were objective response rate (ORR) and safety. This trial was registered at ClinicalTrials.gov (NCT04193696). Results Twenty-one patients were enrolled; the median radiation dose was 40 Gy, and the median number of cycles of camrelizumab was five. The ORR was 52.4%. After a median follow-up of 19.7 months, the median progression-free and overall survival were 5.8 and 14.2 months, respectively. The overall survival probability was 85.7% at 6 months, 76.2% at 9 months, and 59.9% at 12 months. All grade 3 treatment-related adverse events (TRAEs) occurred in five patients (23.8%) and were manageable. No grade 4/5 TRAEs were observed. Conclusion Palliative SBRT plus camrelizumab showed promising antitumor activity against uHCC. Toxicities were manageable with no unexpected safety issues. This study provides evidence of a new therapeutic method for the treatment of uHCC.

The human ether-á-go-go-related gene (hERG) encodes the pore-forming subunit (Kv11.1), conducting a rapidly delayed rectifier K+ current (IKr). Reduction of IKr in pathological cardiac hypertrophy (pCH) contributes to increased susceptibility to arrhythmias. However, practical approaches to prevent IKr deficiency are lacking. Our study investigated the involvement of ubiquitin ligase Nedd4-2-dependent ubiquitination in IKr reduction and sought an intervening approach in pCH. Angiotensin II (Ang II) induced a pCH phenotype in guinea pig, accompanied by increased incidences of sudden death and higher susceptibility to arrhythmias. Patch-clamp recordings revealed a significant IKr reduction in pCH cardiomyocytes. Kv11.1 protein expression was decreased whereas its mRNA level did not change. In addition, Nedd4-2 protein expression was increased in pCH, accompanied by an enhanced Nedd4-2 and Kv11.1 binding detected by immunoprecipitation analysis. Cardiac-specific overexpression of inactive form of Nedd4-2 shortened the prolonged QT interval, reversed IKr reduction, and decreased susceptibility to arrhythmias. A synthesized peptide containing the PY motif in Kv11.1 C-terminus binding to Nedd4-2 and a cell-penetrating sequence antagonized Nedd4-2-dependent degradation of the channel and increased the surface abundance and function of hERG channel in HEK cells. In addition, in vivo administration of the PY peptide shortened QT interval and action potential duration, and enhanced IKr in pCH. We conclude that Nedd4-2-dependent ubiquitination is critically involved in IKr deficiency in pCH. Pharmacological suppression of Nedd4-2 represents a novel approach for antiarrhythmic therapy in pCH.

Computed tomography (CT)-assessed body composition is considered a novel prognostic factor for cancer patients. Owing to the need for new prognostic markers for hepatocellular carcinoma (HCC) patients undergoing radiotherapy, we investigated the impact of body composition on outcomes in this patient population. We retrospectively evaluated 109 HCC patients receiving radiotherapy. The skeletal muscle index, subcutaneous adipose tissue index (SATI), and visceral adipose tissue index within 1 mo, before radiotherapy were assessed based on a single CT image slice at the level of the third lumbar (L3) vertebra. The impact of body composition parameters on progression-free survival (PFS) and overall survival (OS) was assessed. Overall, 62 (56.9%) patients died, and 47 (43.1%) patients experienced recurrence during a median follow-up period of 20.5 mo. Multivariate analysis revealed that SATI was an independent prognostic factor for both PFS (hazard ratio [HR] 0.542

Serine and Arginine Rich Splicing Factor 1 (SRSF1) is a splicing factor that binds to exonic enhancers and stimulates splicing and is previously implicated with autoimmunity. Herein, we investigate the role of SRSF1 in regulating innate immune functions that are pertinent in the pathogenesis of auto-inflammatory diseases. Specifically, we show that conditional deletion of SRSF1 in mature lymphocytes resulted in higher expression of il-17a and il-17 f and an expansion of IL17A

Mortality and morbidity after cardiac arrest remain high due to ischemia/reperfusion (I/R) injury causing multi-organ damages, even after successful return of spontaneous circulation. We previously generated H2O2-activatable antioxidant nanoparticles formulated with copolyoxalate containing vanillyl alcohol (PVAX) to prevent I/R injury. In this study, we examined whether PVAX could effectively reduce organ damages in a rat model of whole-body ischemia/reperfusion injury (WBIR). To induce a cardiac arrest, 70µl/100 g body weight of 1 mmol/l potassium chloride was administered via the jugular venous catheter. The animals in both the vehicle and PVAX-treated groups had similar baseline blood pressure. After 5.5 minutes of cardiac arrest, animals were resuscitated via intravenous epinephrine followed by chest compressions. PVAX or vehicle was injected after the spontaneous recovery of blood pressure was noted, followed by the same dose of second injection 10 minutes later. After 24 hours, multiple organs were harvested for pathological, biochemical, molecular analyses. No significant difference on the restoration of spontaneous circulation was observed between vehicle and PVAX groups. Analysis of organs harvested 24 hours post procedure showed that whole body I/R significantly increased reactive oxygen species (ROS) generation, inflammatory markers, and apoptosis in multiple organs (heart, brain, and kidney). PVAX treatment effectively blocked ROS generation, reduced the elevation of pro-inflammatory cytokines, and decreased apoptosis in these organs. Taken together, our results suggest that PVAX has potent protective effect against WBIR induced multi-organ injury, possibly by blocking ROS-mediated cell damage.

Site trials of a two-stage vacuum, temperature and vacuum swing adsorption (VTVSA) process using carbon fiber composites was, for the first time, carried out at an Australian coal mine to enrich ve...

Purpose To develop a novel three-dimensional (3D) hybrid-encoding framework using compressed sensing (CS) and Toeplitz encoding with variable phase-scrambled radio-frequency (RF) excitation, which has the following advantages: low power deposition of RF pulses, reduction of the signal dynamic range, no additional hardware requirement, and signal-to-noise ratio (SNR) improvement. Methods In light of the actual imaging framework of magnetic resonance imaging (MRI) scanners, we applied specially tailored RF pulses with phase-scrambled RF excitation to implement a 3D hybrid Fourier-Toeplitz encoding method based on 3D gradient-recalled echo pulse (GRASS) sequence. This method exploits Toeplitz encoding along the phase encoding direction, while keeping Fourier encoding along the readout and slice encoding directions. Phantom experiments were conducted to optimize the amplitude of specially tailored RF pulses in the 3D GRASS sequence. In vivo experiments were conducted to validate the feasibility of the proposed method, and simulations were conducted to compare the 3D hybrid-encoding method with Fourier encoding and other non-Fourier encoding methods. Results An optimized low RF amplitude was obtained in the phantom experiments. Using the optimized specially tailored RF pulses, both the watermelon and knee experiments demonstrated that the proposed method was able to preserve more image details than the conventional 3D Fourier-encoded methods at acceleration factors of 3.1 and 2.0. Additionally, SNR was improved because of no additional gradients and 3D volume encoding, when compared with single-slice scanning without 3D encoding. Simulation results demonstrated that the proposed scheme was superior to the conventional Fourier encoding method, and obtained comparative performance with other non-Fourier encoding methods in preserving details. Conclusions We developed a practical hybrid-encoding method for 3D MRI with specially tailored RF pulses of phase-scrambled RF excitation. The proposed method improves image SNR and detail preservation compared with the conventional Fourier encoding methods. Furthermore, our proposed method exhibits superior performance in terms of detail preservation, compared with the conventional Fourier encoding method.

We introduce an accelerated gradient echo (GRE) sequence combining simultaneous multislice excitation (SMS) with echo-shifting technique for high spatial resolution blood oxygen level dependent (BOLD) functional MRI (fMRI). The simulation was conducted to optimize scan parameters. To validate the feasibility of the proposed technique, the visual and motor task experiments were performed at 7.0 Tesla (T). The single-shot EPI sequence was also applied in comparison with the proposed technique. The simulation results showed that an optimized flip angle of 9° provided maximal BOLD contrast for our scanning scheme, allowing low power deposition and SMS acceleration factor of 5. Additionally, parallel acquisition imaging with acceleration factor of 2 was utilized, which allowed a total acceleration factor of 10 in volunteer study. The experiment results showed that geometric distortion-free BOLD images with voxel size of 1.0 × 1.0 × 2.5 mm3 were obtained. Significant brain activation was identified in both visual and motor task experiments, which were in accordance with previous investigations. The proposed technique has potential for high spatial resolution fMRI at ultra-high field because of its sufficient BOLD sensitivity as well as improved acquisition speed over conventional GRE-based techniques.

In the archetypal monolayer semiconductor WSe$_2$, the distinct ordering of spin-polarized valleys (low-energy pockets) in the conduction band allows for studies of not only simple neutral excitons and charged excitons (i.e., trions), but also more complex many-body states that are predicted at higher electron densities. We discuss magneto-optical measurements of electron-rich WSe$_2$ monolayers, and interpret the spectral lines that emerge at high electron doping as optical transitions of 6-body exciton states ("hexcitons") and 8-body exciton states ("oxcitons"). These many-body states emerge when a photoexcited electron-hole pair interacts simultaneously with multiple Fermi seas, each having distinguishable spin and valley quantum numbers. In addition, we identify the energies of primary and satellite optical transitions of hexcitons in the photoluminescence spectrum.

Current utilization practices have not fully appreciated the potential of coal mine drainage gas and have resulted in significant greenhouse gas emissions. This paper introduces a novel pathway of ...

Due to the unique properties of flexible electronic devices, various microelectromechanical systems (MEMS) devices based on flexible substrate have been proposed for miniaturization, lightweight, and intelligent applications. Obviously, the deformation of flexible substrate inevitably affects the performance of flexible MEMS devices. This article first investigates the static modeling of bending characteristics on the flexible V-shaped beam actuator, which is the most representative MEMS thermal actuator. This article focused on a comprehensive static analysis of bending mechanical property based on the flexible substrate. The innovation of static modeling is obtaining variation regularity of bending characteristics by establishing the deformation coupling model of V-beam structure and flexible substrate. The V-shaped beam thermal actuators are designed and fabricated on flexible liquid crystal polymer (LCP) substrate, and the influence of bending substrate on the actuation performance of the device is revealed by calculation, simulation, and experiment. The experimental results demonstrate that the actuation current of the V-shaped beam actuator rises up proportionally with the increase of angle and the decrease of beam length. When flexible substrate bends gradually from curvature 0 to 33.3 (1/m), the rates of measured actuation current change are 17.05%, 13.70%, and 10.24%, corresponding to different angles of 13°, 20°, and 27° at $600~\mu \text{m}$ length of V-shaped beams. Meanwhile, the rates of measured actuation current change are 9.22%, 13.22%, and 13.70%, corresponding to different lengths of 400, 500, and $600~\mu \text{m}$ at 20° angle of V-shaped beams. The experimental results are consistent with the theoretical calculation results of static modeling, and the error is less than 3.7%.

Background: The combination of transcatheter arterial chemoembolization (TACE) plus sorafenib prolonged progression-free survival (PFS) and overall survival (OS) than sorafenib or TACE monotherapy for patients with hepatocellular carcinoma (HCC). This study assessed the efficacy and safety of radiotherapy (RT) plus monoclonal antibody against programmed cell death 1 (anti-PD1) versus TACE plus sorafenib for patients with advanced HCC.Methods: Patients with advanced HCC who treated with RT plus anti-PD1 and TACE plus sorafenib were enrolled. Objective response rate (ORR), PFS, disease control rate (DCR) and OS were calculated to assess the antitumor response and the treatment-related adverse events to the safety.Results: Between January 2018 to March 2021, 37 patients underwent RT plus anti-PD1 and 41 patients underwent TACE plus sorafenib. The baseline characteristics between the two groups were comparable. The ORR and DCR were significantly higher in the RT+PD1 group than the TACE plus sorafenib group according to RECIST 1.1 (54.05% vs 12.20%, P < 0.001; 70.27% vs 46.37%, P = 0.041; respectively) and according to mRECIST (56.76% vs 31.71%, P = 0.039; 70.27% vs 46.37%, P = 0.041; respectively). RT plus anti-PD1 provided significantly better PFS (HR, 0.51; 95% CI 0.30-0.86; p=0.017) than TACE plus sorafenib. Moreover, patients with RT plus anti-PD1 had significantly higher 3-, 6-, and 9-month OS rates than those with TACE plus sorafenib(97.3% vs 92.30%, P < 0.001; 91.89% vs 68.60%, P < 0.001; 75.5% vs 60.60%, P < 0.001; respectively). The median OS was more favorable 17.4 months for the RT+PD1 group and 11.9 months for the TACE plus sorafenib group. No treatment-related death was observed. Grade 3 or more treatment-related adverse events (TRAEs) occurred significantly less in patients in the RT+PD1 group than the TACE plus sorafenib group (29.7% vs 75.6%, p < 0.001), and all TRAEs were manageable.Conclusions: In this real-world study, RT plus anti-PD1 showed significantly promising efficacy and manageable safety than TACE plus sorafenib in patients with advanced HCC. Toxicities were manageable, with no unexpected safety signals. The study provides evidence on a new therapeutic method in the treatment of advanced HCC.

Background The combination of transcatheter arterial chemoembolization (TACE) plus sorafenib prolonged progression-free survival (PFS) and overall survival (OS) than sorafenib or TACE monotherapy for patients with hepatocellular carcinoma (HCC). This study assessed the efficacy and safety of radiotherapy (RT) plus monoclonal antibody against programmed cell death 1 (anti-PD1) versus TACE plus sorafenib for patients with advanced HCC. Methods Patients with advanced HCC who treated with RT plus anti-PD1 and TACE plus sorafenib were enrolled. Objective response rate (ORR), PFS, disease control rate (DCR) and OS were calculated to assess the antitumor response and the treatment-related adverse events to the safety. Results Between January 2018 to March 2021, 37 patients underwent RT plus anti-PD1 and 41 patients underwent TACE plus sorafenib. The baseline characteristics between the two groups were comparable. The ORR and DCR were significantly higher in the RT + PD1 group than the TACE plus sorafenib group according to RECIST 1.1 (54.05% vs. 12.20%, P P = 0.041; respectively) and according to mRECIST (56.76% vs. 31.71%, P = 0.039; 70.27% vs. 46.37%, P = 0.041; respectively). RT plus anti-PD1 provided significantly better PFS (HR, 0.51; 95% CI 0.30–0.86; P = 0.017) than TACE plus sorafenib. Moreover, patients with RT plus anti-PD1 had significantly higher 3-, 6-, and 9-month OS rates than those with TACE plus sorafenib(97.3% vs. 92.30%, P P P P Conclusions In this real-world study, RT plus anti-PD1 showed significantly promising efficacy and manageable safety than TACE plus sorafenib in patients with advanced HCC. Toxicities were manageable, with no unexpected safety signals. The study provides evidence on a new therapeutic method in the treatment of advanced HCC.

This paper presents an integrated subsurface study that focuses on delivering field development planning of two reservoirs via comprehensive reservoir characterization workflows. The upper gas reservoir and lower oil reservoir are in communication across a major fault in the crest area of the structure. Gas from the upper reservoir, which is not under development, is being produced along with some oil producers from the oil reservoir as per acquired surveillance data. Pressure depletion is observed in observer wells of the upper reservoir, which substantiate both reservoirs communication. The oil reservoir is on production since 1994, under miscible hydrocarbon water alternating gas injection (HCWAG) and carbon dioxide (CO2) injection. The currently implemented development plan has been facing several complexities and challenges including, but not limited to, maintaining miscibility conditions, sustainability of production and injection in view of reservoirs communication, reservoir modeling challenges, suitability of monitoring strategy, associated operating costs and expansion of field development in newly appraised areas. In this study, an assessment of multiple alternative field development scenarios was conducted; with an aim to tackle field management and reservoir challenges. It commenced by a comprehensive synthesis of seismic, petrophysical (including extensive core characterizations), geological, production and reservoir engineering data to ensure data adequacy and effectiveness for development planning. The process was followed by evaluation of the historical reservoir management, HCWAG and CO2 injection practices using advanced analytics to identify areas for improvement and accelerate decision making process. The identified areas of improvement were incorporated into a dynamic model via diverse set of field management logics to screen wide range of scenarios. In the final step, the optimal scenarios were selected, in line of having strong economic indicators, honoring operational constraints, corporate business plan and strategic objectives. The comprehensive and flexible field management logic was set up to target different challenges and was used to extensively screen hundreds of different field development scenarios varying several parameters. Examples of such parameters are WAG ratio, injection pressures for both water/gas and CO2, cycle duration, well placement, reservoir production and injection guidelines, different co-development production schemes coupled with static and dynamic uncertainty properties against incremental oil production and discounted cash flow. The simulation results were analyzed using standardized approach where a number of key indicators was cross-referenced to produce optimal field development scenarios with regards to co-development effect of both reservoirs, miscibility conditions, balanced pressure depletion, harmonized sweep as well as robust discounted cash flow. Strong management support, multi-disciplinary data integration, agility of decision making and revisions in a controlled timeframe are considered as the key pillars for success of this study. The adopted workflow covers subsurface modeling aspects from A-Z and following reservoir characterization and modeling best practices. The methodology applied in this study uses an integrated subsurface structured approach to tackle reservoirs challenges and co-development, generate alternative development options leveraging on data analytics techniques and advanced field management strategies.

Effective well placement and design planning accounts for subsurface uncertainties to estimate production and economic outcomes. Reservoir modelling and simulation workflows build on ensemble approaches to manage uncertainties for production forecasting. Ensemble generation and interpretation requires a higher degree of automation analytics and artificial intelligence for fast value extraction and decision support. This work develops practical intelligent workflow steps for a robust infill well placement and design scenario in multi-layered/stacked reservoirs under uncertainty. Potential well targets are classified by an opportunity index defined by a combination of rock and hydrocarbon flow properties as well as connected volumes above a minimum economic volume. Unsupervised learning techniques are applied to automate the search for alternative target areas, so-called hotspot regions. Supervised machine/learning models are used to predict infill well performance based on simulated and/or past production experience. A stochastic evaluation including all ensemble cases is used to capture uncertainty. Vertical, deviated, horizontal and multilateral wells are proposed to optimally target single or connect to multiple hotspot regions under technical and economic constraints. A structured workflow design is applied to a multi-layered/stacked reservoir model. Subsurface uncertainties are described and captured by multiple model realizations, which are constrained in areas of historical wells. An infill well program for a multi-layered/stacked reservoir is defined for incremental production increase under economic constraints. This work shows how robust well location and design builds on the full ensemble of cases with a high degree of automation using analytics and machine-learning techniques. Both production and economic targets are calculated and compared to a reference case for robust solution verification and probability of success. In conclusion, an overall reservoir-driven field development strategy is required for efficient execution. However, automation is well applicable to repetitive workflow steps which includes hotspot search in an ensemble of validated reservoir models. This work presents an integrated, intelligent solution for informed decision making on infill drilling locations and refined well design. Higher degree of automation with embedded intelligence are discussed from case generation to hotspot identification. Aspects of model calibration in a producing field environment are addressed.

Radiation-induced lung injury (RILI) is a severe side effect of radiotherapy for non-small cell lung cancer (NSCLC) ,and one of the major hindrances to improve the efficacy of radiotherapy. Previous studies have confirmed that sodium butyrate (NaB) has potential of anti-radiation toxicity. However, the mechanism of the protective effect of NaB against RILI has not yet been clarified. This study aimed to explore the underlying protective mechanisms of NaB against RILI in NSCLC through network pharmacology, molecular docking, molecular dynamic simulations andThe predictive target genes of NaB were obtained from the PharmMapper database and the literature review. The involved genes of RILI and NSCLC were predicted using OMIM and GeneCards database. The intersectional genes of drug and disease were identified using the Venny tool and uploaded to the Cytoscape software to identify 5 core target genes of NaB associated with RILI. The correlations between the 5 core target genes and EGFR, PD-L1, immune infiltrates, chemokines and chemokine receptors were analyzed using TIMER 2.0, TIMER and TISIDB databases. We constructed the mechanism maps of the 3 key signaling pathways using the KEGG database based on the results of GO and KEGG analyses from Metascape database. The 5 core target genes and drug were docked using the AutoDock Vina tool and visualized using PyMOL software. GROMACS software was used to perform 100 ns molecular dynamics simulation. Irradiation-induced lung injury model in mice were established to assess the therapeutic effects of NaB.A total of 51 intersectional genes involved in NaB against RILI in NSCLC were identified. The 5 core target genes were AKT1, TP53, NOTCH1, SIRT1, and PTEN. The expressions of the 5 core target genes were significantly associated with EGFR, PD-L1, immune infiltrates, chemokines and chemokine receptors, respectively. The results from GO analysis of the 51 intersectional genes revealed that the biological processes were focused on the regulation of smooth muscle cell proliferation, oxidative stress and cell death, while the three key KEGG pathways were enriched in PI3K-Akt signal pathway, p53 signal pathway, and FOXO signal pathway. The docking of NaB with the 5 core target genes showed affinity and stability, especially AKT1.NaB may protect patients from RILI in NSCLC through multiple target genes including AKT1, TP53, NOTCH1, SIRT1 and PTEN, with multiple signaling pathways involving, including PI3K-Akt pathway, p53 pathway, and FOXO pathways. Our findings effectively provide a feasible theoretical basis to further elucidate the mechanism of NaB in the treatment of RILI.

Background Ischemia/reperfusion (I/R) injury causes overproduction of reactive oxygen species, which are the major culprits of oxidative stress that leads to inflammation, apoptosis, myocardial damage, and dysfunction. Bilirubin acts as a potent endogenous antioxidant that is capable of scavenging various reactive oxygen species. We have previously generated bilirubin nanoparticles (BRNPs) consisting of polyethylene glycol–conjugated bilirubin. In this study, we examined the therapeutic effects of BRNPs on myocardial I/R injury in mice. Methods and Results In vivo imaging using fluorophore encapsulated BRNPs showed BRNPs preferentially targeted to the site of I/R injury in the heart. Cardiac I/R surgery was performed by first ligating the left anterior descending coronary artery. After 45 minutes, reperfusion was achieved by releasing the ligation. BRNPs were administered intraperitoneally at 5 minutes before and 24 hours after reperfusion. Mice that received BRNPs showed significant improvements in their cardiac output, assessed by echocardiogram and pressure volume loop measurements, compared with the ones that received vehicle treatment. BRNPs treatment also significantly reduced the myocardial infarct size in mice that underwent cardiac I/R, compared with the vehicle‐treatment group. In addition, BRNPs effectively suppressed reactive oxygen species and proinflammatory factor levels, as well as the amount of cardiac apoptosis. Conclusions Taken together, BRNPs could exert their therapeutic effects on cardiac I/R injury through attenuation of oxidative stress, apoptosis, and inflammation, providing a novel therapeutic modality for myocardial I/R injury.

In recent years, coal mine has applied shield tunneling machines to roadway excavation to improve production efficiency. Geological condition is an important factor that determines the efficiency of shield machines. The shield machine is most favorable for medium to hard surrounding rocks such as limestone and sandstone. Therefore, the lithology prediction of the location of a planned excavation roadway becomes the core issue in improving the efficiency of the shield machine. At present, seismic inversion is an essential method for lithology prediction. However, in Yangquan Xinjing area, missing P-wave logging curves affects the impedance inversion. Therefore, using existing logging curves to generate missing P-wave logging curves and using sandstone exposure data to continuously update lithology distribution prediction results are of great interest. In this study, logging curves were first pretreated by standardization to ensure the inversion effect. Because of the missing acoustic logging curves, the random forest regression algorithm was introduced using density, natural gamma, apparent resistivity, and spontaneous potential curves as characteristic variables to establish a curve regression prediction model. Then, P-wave logging curves were acquired. After a full analysis of the principles of acoustic and gamma curves, a quasi-acoustic curve is constructed, and a quasi-acoustic inversion was performed. The top and bottom interfaces of the K7 sandstone were interpreted on the inversion data body. The interpreted horizon information was converted from the time domain to the depth domain. The predicted results agreed well with the exposed data. At the same time, combined with the lithology exposure data from the shield tunneling machine, the distribution prediction of the K7 sand body in the target roadway section was updated and iterated many times, which provided effective guidance for the optimization of the tunneling route of the shield tunneling machine.

At present, lane line detection is still one of the unsolved problems in the field of computer vision. The realization of automatic driving perception technology cannot lack lane line detection. However, most of the lane line detection algorithms proposed at present cannot meet the accuracy and real-time performance at the same time.[1] as a newly proposed polynomial regression lane line detection algorithm, has reached an excellent level in terms of accuracy and speed. This paper further explores on the basis of this paper and proposes a method that does not share Top-y. Experiments show that the newly proposed method is also very effective for lane line detection.

ObjectiveDue to common practice of hypofractionated radiotherapy in pancreatic cancer and heterogeneous chemotherapy regimens in previous studies, modified nomograms are required. Therefore, we aim to develop and validate prognostic nomograms for locally advanced pancreatic cancer (LAPC) after stereotactic body radiation therapy (SBRT) and chemotherapy.MethodsThe development cohort comprised 925 patients with LAPC receiving SBRT and gemcitabine-based chemotherapy in our center, while 297 patients from another two centers formed the validation cohort. Nomograms were created from COX models and internally validated by bootstrap. Model discriminations were evaluated by calibration plots and concordance index (C-index). A decision curve analysis (DCA) was performed to evaluate clinical benefits of nomograms. Additionally, recursive partitioning analysis (RPA) was used for stratifications of survival probability based on the total score of each patient calculated by nomograms.ResultsWeight loss, tumor diameter, radiation dose, CA19-9 kinetics after treatment and surgical resection were included in the nomogram for overall survival (OS), while the five factors plus performance status formed the nomogram for progression free survival (PFS). The corrected C-indexes for estimated 1-year and 2-year OS of the development cohort were 0.88 (95% CI: 0.85-0.91) and 0.86 (95% CI: 0.83-0.90). For those of the validation cohort, it was 0.88 (95% CI: 0.82-0.94) and 0.83 (95% CI: 0.74-0.91). Additionally, the corrected C-index for predicted 1-year PFS in the development and validation cohort was 0.83 (95% CI: 0.81-0.86) and 0.82 (95% CI: 0.78-0.87), respectively. The calibration plots showed good agreement of 1- and 2-year OS and 1-year PFS between the estimations and actual observations. Potential clinical benefits were demonstrated with DCA. Additionally, for 1- and 2-year OS and 1-year PFS, patients were stratified into four groups with different survival probability by RPA.ConclusionThe validated nomograms provided useful predictions of OS and PFS for LAPC with chemoradiotherapy.

A two-stage methane enrichment process with initial vacuum, temperature and final vacuum swing adsorption (VTVSA) was applied to enrich simulated ventilation air methane (VAM) ( 15 vol.% CH4 in the second stage product. Simulated ventilation air streams with 0.30, 0.60 and 0.98 vol.% CH4 were enriched up to 19.28, 24.24 and 36.92 vol.% CH4, respe...