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Abstract C4 photosynthesis exemplifies convergent evolution of complex traits. Herein, we construct chromosome-scale genome assemblies and perform multi-omics analysis for five Flaveria species, which represent evolutionary stages from C3 to C4 photosynthesis. Chromosome-scale genome sequence analyses reveal a gradual increase in genome size during the evolution of C4 photosynthesis attributed to the expansion of transposable elements. Systematic annotation of genes encoding C4 enzymes and transporters identify additional copies of three C4 enzyme genes through retrotranspositions in C4 species. C4 genes exhibit elevated mRNA and protein abundances, reduced protein-to-RNA ratios, and comparable translation efficiencies in C4 species, highlighting a critical role of transcriptional regulation in C4 evolution. Furthermore, we observe an increased abundance of ethylene response factor (ERF) transcription factors and cognate cis-regulatory elements associated with C4 genes regulation. Altogether, our study provides valuable genomic resources for the Flaveria genus and sheds lights on evolutionary and regulatory mechanisms underlying C4 photosynthesis.

Abstract The role of circulating metabolome in cognitive impairment is inconclusive, and whether the associations are in the severity-dependent manner remains unclear. We aimed to identify plasma metabolites associated with cognitive impairment and evaluate the added predictive capacity of metabolite biomarkers on incident cognitive impairment beyond traditional risk factors. In the Rugao Longevity and Ageing Study (RuLAS), plasma metabolome was profiled by nuclear magnetic resonance spectroscopy. Participants were classified into the cognitively normal, moderately impaired, and severely impaired groups according to their performance in two objective cognitive tests. A two-step strategy of cross-sectional discovery followed by prospective validation was applied. In the discovery stage, we included 1643 participants (age: 78.9 ± 4.5 years) and conducted multinomial logistic regression. In the validation stage, we matched 68 incident cases of cognitive impairment (moderately-to-severely impaired) during the 2-year follow-up with 204 cognitively normal controls by age and sex at a 1:3 ratio, and conducted conditional logistic regression. We identified 28 out of 78 metabolites cross-sectionally related to severely impaired cognition, among which IDL particle number, ApoB in IDL, leucine, and valine were prospectively associated with 28%, 28%, 29%, and 33% lower risk of developing cognitive impairment, respectively. Incorporating 13 metabolite biomarkers selected through Lasso regression into the traditional risk factors-based prediction model substantially improved the ability to predict incident cognitive impairment (AUROC: 0.839 vs. 0.703, P

Abstract Background A short cervix in mid-trimester pregnancy is a risk factor for spontaneous preterm birth. However, there is currently a lack of predictive models and classification systems for predicting spontaneous preterm birth in these patients, especially those without additional risk factors for spontaneous preterm birth. Methods A retrospective observational cohort study of low-risk singleton pregnant women with a short cervix (≤ 25 mm) measured by transvaginal ultrasonography between 22 and 24 weeks was conducted. A multivariate logistic regression model for spontaneous preterm birth 146, respectively. The predicted probabilities of spontaneous preterm birth

Abstract Molecular ferroelectrics have garnered significant attention due to their structural tunability, low synthesis temperature, and high flexibility. Herein, we successfully synthesized imidazole perchlorate (ImClO4) single crystals and high-quality, highly-oriented thin films on Si substrates. These films demonstrated a high inverse piezoelectric coefficient of 55.7 pm/V. Two types of domain bands were observed: type-I bands tilted ~60° relative to the horizontal axis, and type-II bands positioned perpendicular to the horizontal axis. Under a + 20 V bias, type-I bands showed a reduction and detachment of 180° domain walls to form a needle-like domain. It extended toward the band boundary after applying −20 V bias, which grew along the boundary upon contact. In contrast, type-II bands showed straight domain wall motion and displayed a higher piezoresponse than type-I bands. The growth of high quality molecular ferroelectric thin films on Si substrates paves the way for the development of on-chip devices.

Objective Current biomarkers for predicting immunotherapy response in non-small-cell lung cancer (NSCLC) are derived from invasive procedures with limited predictive accuracy. Thus, identifying a non-invasive predictive biomarker would improve patient stratification and precision immunotherapy.Methods and analysis In this retrospective multicohort study, the discovery cohort included 205 NSCLC patients screened from ORIENT-11 and an external validation (EV) cohort included 99 real-world NSCLC patients. The ‘onion-mode segmentation’ method was developed to extract ‘onion-mode perfusion’ (OMP) from contrast-enhanced CT images. The predictive performance of OMP or its combination with the PD-L1 Tumour Proportion Score (TPS) was evaluated by the area under the curve (AUC).Results High baseline OMP was associated with significantly longer survival and predicted patient response to combination anti-PD-(L)1 therapy in the discovery and EV cohorts. OMP complemented the PD-L1 TPS with superior predictive sensitivity (p=0.02). In the PD-L1 TPS

Abstract. Background:. Previous studies have reported associations of specific maternal and paternal lifestyle factors with offspring's cognitive development during early childhood. This study aimed to investigate the prospective associations between overall parental lifestyle and offspring's cognitive performance during adolescence and young adulthood in China. Methods:. We included 2531 adolescents aged 10–15 years at baseline in 2010 from the China Family Panel Studies. A healthy parental lifestyle score (ranged 0–5) was constructed based on the following five modifiable lifestyle factors: Smoking, drinking, exercise, sleep, and diet. Generalized estimating equation models were used to examine the association between baseline parental healthy lifestyle scores and offspring's fluid and crystallized intelligence in subsequent years (2012, 2014, 2016, and 2018). Results:. Offspring in the top tertile of parental healthy lifestyle scores performed better in overall fluid intelligence (multivariable-adjusted β = 0.53, 95% confidence interval [CI]: 0.29–0.77) and overall crystallized intelligence (multivariable-adjusted β = 0.35, 95% CI: 0.16–0.54) than those in the bottom tertile of parental healthy lifestyle scores. The results were similar after further adjustment for the offspring's healthy lifestyle scores and persisted across the subgroups of parental socioeconomic status. Additionally, maternal and paternal healthy lifestyle scores were independently associated with better offspring's cognitive performance, with significant contribution observed for paternal never-smoking, weekly exercise, and diversified diet. When both parents and offspring adhered to a healthier lifestyle, we observed the highest level of the offspring's overall crystallized intelligence. Conclusions:. Our study indicates that parental adherence to a healthier lifestyle is associated with significantly better offspring's cognitive performance during adolescence and early adulthood, regardless of socioeconomic status. These findings highlight the potential cognitive benefits of promoting healthy lifestyles among parents of adolescents.

Abstract Background Little is known regarding the association of interviewer-reported cognitive problems (ICP) with age-related cognitive decline. We aimed to investigate the independent associations of ICP and the combined associations of ICP and self-reported cognitive problems (SCP) with subsequent cognitive decline and dementia in two prospective cohort studies. Methods We included 10,976 Chinese (age = 57.7 ± 8.7) and 40,499 European (age = 64.6 ± 9.4) adults without dementia from the China Health and Retirement Longitudinal Study (CHARLS) and the Survey of Health, Ageing, and Retirement in Europe (SHARE). Self-rated memory (5-point scale) and interviewer-rated frequencies of asking for clarification (6-point scale) were used to define SCP and ICP (dichotomized). Outcomes included objective cognitive test scores (z-score transformation) and incident dementia. Generalized estimating equation models were performed to evaluate mean differences in objective cognitive decline. Logistic and Cox regression models were used to estimate the relative risk of dementia. Results from two cohorts were pooled using the random-effects models. Results ICP was associated with faster cognitive decline in CHARLS (β CHARLS = −0.025 [−0.044, −0.006] z-score/year). ICP and SCP were also independently associated with higher risk of dementia in two cohorts (pooled relative risk for SCP = 1.73 [1.30, 2.29]; pooled relative risk for ICP = 1.40 [1.10, 1.79]). In the joint analysis, participants with coexistence of SCP and ICP had the fastest cognitive decline (β CHARLS = −0.051 [−0.080, −0.021]; β SHARE = −0.024 [−0.043, −0.004]; pooled β = −0.035 [−0.061, −0.009] z-score/year) and highest risk of dementia (ORCHARLS = 1.77 [1.42, 2.20]; HRSHARE = 2.94 [2.42, 3.59]; pooled relative risk = 2.29 [1.38, 3.77]). Conclusions The study suggested that interviewer-reported cognitive problems may be early indicators of cognitive decline and dementia in middle-aged and older adults. A combination of self- and interviewer-reported cognitive problems showed the strongest associations with cognitive decline and dementia.

The challenge of achieving a reliable and safe synchronization process for microgrids under weak communication conditions is a significant issue in distributed grid-connected energy storage. This is also the core motivation of this study. First, the concept of weak communication is introduced, and weak communication conditions are simulated by limiting the number of communications. Additionally, a fast synchronization method based on the frequency difference at the grid connection point is proposed, which allows for the rapid synchronization of converters under weak communication conditions. This method also includes an analysis of the range for key parameters, providing practical and feasible guidance for its real-world application. Finally, the validity of the theory is verified through PSCAD/EMTDC simulations and physical experiments.

Abstract With the large-scale vaccination of lipid nanoparticles (LNP)-based COVID-19 mRNA vaccines, elucidating the potential polyethylene glycol (PEG)-associated immune responses triggered by clinically relevant LNP has become imminent. However, inconsistent findings were observed across very limited population-based studies. Herein we initiated a study using LNP carrier of Comirnaty® as a representative, and simulated real-world clinical practice covering a series of time points and various doses correlated with approved LNP-delivered drugs in a rat model. We demonstrated the time- and dose-dependency of LNP-induced anti-PEG antibodies in rats. As a thymus-independent antigen, LNP unexpectedly induced isotype switch and immune memory, leading to rapid enhancement and longer lasting time of anti-PEG IgM and IgG upon re-injection in rats. Importantly, initial LNP injection accelerated the blood clearance of subsequent dosing in rats. These findings refine our understandings on LNP and possibly other PEG derivatives, and may promote optimization of related premarket guidelines and clinical protocols.

Porous Ti–6Al–4V alloys have been widely applied in industry, serving as structural or functional materials. However, the conventional method of producing porous Ti–6Al–4V by metal injection molding (MIM) with a space holder has several drawbacks, such as long debinding time, sintering furnace contamination, uncontrolled porosity and pore size, poor mechanical properties and process instability. This study presents a novel method of fabricating porous Ti–6Al–4V by MIM without space holder that can overcome these challenges. The porosity, average pore size, tensile strength, and compressive strength were studied by scanning electron microscope characterization, optical micrograph analysis, and mechanical testing. Flexible changes in porosity (17 %–74 %), pore size (14 μm–84 μm), and tensile strength (15 MPa–457 MPa) could be achieved by adjusting the powder size and sintering temperature. A higher porosity can be achieved as compared to the previous studies. The relationships among particle size, sintering temperature, porosity, and tensile strength are investigated by multiple regression models, which facilitate the tailored design of porous Ti–6Al–4V alloys for industrial applications and enhance the combination of experimental and theoretical methods in the design of porous alloys using MIM.

Polyvinylidene fluoride (PVDF) and its copolymers present extensive application prospects, especially in the field of wearable electronics. However, utilizing nanofillers for enhanced β‐phase and piezoelectric properties faces challenges like noncontinuous interfaces, poor compatibility between nanofillers and PVDF matrix, and the requirement of high‐voltage polarization, hindering extensive domain alignment on a large scale. Herein, a method is proposed to synthesize high‐performance PVDF composites by introducing hydroxylated barium titanate (H@BTO) nanoparticles and a directional freeze‐drying method to enhance β‐phase content and piezoelectric properties without polarization. Molecular dynamics simulations reveal robust binding interactions between Ba and F atoms along with OH surface terminations on H@BTO, facilitating hydrogen bonding within the PVDF matrix, resulting in dipole alignment and increased spontaneous polarization. The composite film achieves an 86.69% β phase content and a piezoelectric coefficient of ≈14.49 pm V−1 without electric polarization. The freeze‐drying PVDF‐H@BTO composite film paired with a PA6 membrane is used to fabricate triboelectric nanogenerator, demonstrating a current density of ≈107.5 mA m−2 and an output voltage of ≈832 V. Results demonstrate that the utilization of strong binding interactions between various atoms, the hydroxyl anchoring effect, and directional freeze‐drying method as a strategy holds promising prospects for synthesizing high‐performance piezoelectric composites.

Abstract In recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from computer science. Graph neural networks (GNNs) are new machine learning models with powerful feature extraction, relationship inference, and compositional generalization capabilities. These advantages drive researchers to design computational models to accelerate material property prediction and new materials design, dramatically reducing the cost of traditional experimental methods. This review focuses on the principles and applications of the GNNs. The basic concepts and advantages of the GNNs are first introduced and compared to the traditional machine learning and neural networks. Then, the principles and highlights of seven classic GNN models, namely crystal graph convolutional neural networks, iCGCNN, Orbital Graph Convolutional Neural Network, MatErials Graph Network, Global Attention mechanism with Graph Neural Network, Atomistic Line Graph Neural Network, and BonDNet are discussed. Their connections and differences are also summarized. Finally, insights and prospects are provided for the rapid development of GNNs in materials science at the atomic scale.

Abstract COVID-19 has dramatically changed people's mobility geste patterns and affected the operations of different functional spots. In the environment of the successful reopening of countries around the world since 2022, it's pivotal to understand whether the reopening of different types of locales poses a threat of wide epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the data handed by the Safegraph website, and taking into account the crowd inflow characteristics and the changes of susceptible and latent populations, the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the perpetration of continuing strategies were simulated. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the perpetration of the continuing strategy, especially the general dine-in restaurants were at higher risk levels. Religious exertion centers were the points of interest with the loftiest average infection rates after the perpetration of the continuing strategy. Points of interest such as convenience stores, large shopping malls, and pharmacies were at a lower risk for outbreak impact after the continuing strategy was enforced. Based on this, continuing forestallment and control strategies for different functional points of interest are proposed to provide decision support for the development of precise forestallment and control measures for different spots.

Summary: Neoadjuvant chemotherapy (NACT) is a therapeutic option for locally advanced cervical cancer (LACC) patients. This study was aimed to identify potential liquid biopsy biomarkers to monitor the NACT response. Through targeted next-generation sequencing (NGS) analysis of circulating tumor DNA (ctDNA) and tumor tissue DNA (ttDNA) taken from LACC patients undergoing platinum-based NACT, 64 genes with mutations emerge during NACT in the non-responders but none in the responders. Among them, the PBRM1, SETD2, and ROS1 mutations were frequently detected in the ctDNA and ttDNA of the non-responders, and mutant PBRM1 was associated with poorer survival of patients. In vitro, PBRM1 knockdown promoted resistance to cisplatin through boosting STAT3 signaling in cervical cancer cells, while it sensitized tumor cells to poly-ADP-ribose-polymerase inhibitor olaparib. These findings suggest that mutant PBRM1 is a potential ctDNA marker of emerging resistance to NACT and of increased sensitivity to olaparib, which warrants further clinical validation.

Non-contacting finger seals represent an advanced non-contacting and compliant seal in gas turbine sealing technology. This paper proposes a new structure of non-contacting finger seals with double interlocking pads. The numerical analysis model based on the thermo-fluid-structure coupling method for the new type finger seal was established. The influence of working conditions on leakage of the seal was studied and compared with the single padded non-contacting finger seal. The results show that the interface between the bottom of the finger pad and rotor surface is the main leakage path that forms the gas film with obvious variations of pressure and flow velocity. Under high temperature and high pressure operating conditions, the hydrodynamic effect of the gas film is enhanced, and lifting force is significantly improved. The deformation of fingers is composed of elastic deformation and thermal deformation. At room temperature, the deformation of fingers is mainly elastic deformation and points to the center of the rotor, which reduces the gas film clearance. The deformation of fingers at high temperature and high pressure creates a circumferentially convergent gap between the bottom of the pad and the rotor, which is beneficial to improve the loading capacity and to reduce leakage of the seal. Compared with the typical single padded non-contacting finger seal, the double interlocking padded finger seal proposed in this paper reduces the leakage factor by about 37%, which provides an advanced seal concept with the potential to improve sealing performance under high temperature and high pressure working conditions.

Considering the electrochemical reaction and chemical reaction near the crack in the local corrosion environment, the stress corrosion cracking growth rate prediction model for nuclear power turbine rotor steel was established based on the slip dissolution mechanism and finite element method. The calculated parameters were defined based on the material and electrochemical properties of steam turbine rotor in the simulated environment, and the calculated results can agree well with the experiment results. Results revealed that the stress intensity factor has an important influence on the local corrosion environment at the crack tip, the electrode potential is decreased and the process of anodic dissolution is accelerated with the increase of the stress intensity factor. Furthermore, the prediction model can describe stage-I and stage-II of stress corrosion cracking propagation well, and provide a possible trend prediction for practical production practice.

The influence of stress-controlled fatigue (SCF) damage on the fracture location shifting of welded joints for NiCrMoV steels in the corrosive solution of 3.5wt.% NaCl is systematically investigated by electrochemical measurements, immersion tests, slow strain rate tensile (SSRT) tests, and fractural morphology observations in the present work. The results show that both the yield strength (YS) and the ultimate tensile strength (UTS) of welded joints are increased after the SCF tests. The susceptibility of stress corrosion cracking (SCC) and galvanic corrosion of the SCF damaged welded joints are higher than that of the as-received specimens. In addition, the fracture locations of welded joints after SSRT tests in the corrosive solution are shifted with the degree of SCF damage. Different degrees of fatigue damage are accumulated in the three zones of welded joints during the cyclic deformation, which could change the competitive relation of the most susceptive SCC zone between the fusion zone and the middle of WM.

The Rocket-Based Combined-Cycle (RBCC) engine integrates the preponderance of ramjet and rocket engines. It can perform excellently at a lower Mach number than a ramjet while consuming less fuel than the rocket. The higher specific impulse under lower flight Mach conditions guarantees the competitiveness and application prospect of an RBCC engine for reusable space transportation and hypersonic cruise vehicles. With a wide range of Mach numbers, the flow choking between primary and secondary streams in the inner flow passage of the engine becomes complicated. The flow choking not only affects the mass flow ratio between the air stream and rocket plume but also determines the thrust performance of the engine. However, the relationship between flow-choking states and thrust performance has not been revealed. This investigation aimed to provide a better design reference for the RBCC trajectory design and a basis for the RBCC engine geometry design so that the thrust performance under different flow choking states was studied. The findings indicate that the engine is more favorable in the supersonic regime status than others. At the premise of the lower total pressure air stream condition, the thrust of the RBCC engine is attributed to the rocket pressurization effect. On the contrary, its thrust is ascribed to the air stream when the air stream total pressure is higher. Besides, the thrust augmentation of the RBCC engine is essentially due to the larger entrainment ratio. There is a rocket mass flow rate range in different mixer diameters, named region B in this paper, in which the air mass flow rate can be boosted with the rocket mass flow rate and the specific impulse ratio is higher than the rocket specific impulse. Significantly, the RBCC design state should be maintained in region B to meet the operational requirements of engines under the precondition of an insufficient flight Mach number.

ObjectiveThe aim of this study is to identify risk factors of intra-cystic hemorrhage in microwave ablation of mixed solid and cystic microwave ablation s, and to design a preoperative nomogram to predict the risk value of intraoperative bleeding with the goal of individualizing the surgical approach toward different types of cystic and solid thyroid nodules.MethodsA total of 241 patients with cystic-solid thyroid nodules who underwent ultrasound-guided percutaneous microwave ablation were retrospectively divided into a bleeding group and a non-bleeding group to compare the diameter, cystic proportion, cystic fluid nature, color Doppler flow imaging, Contrast-enhanced ultrasound (CEUS) findings, and operative methods. Based on univariate and multivariate analysis, the important risk factors of nodular intracapsular hemorrhage in the ablation procedure were projected to a nomogram for predicting the possibility of intraoperative hemorrhage in the thyroid cystic solid nodules.ResultsIntra-cystic hemorrhage was developed in 37 cases during the ablation of mixed thyroid nodules with a total incidence of 15% (37/241). Significant differences were found statistically between the two groups on the diameter of the lesions, CEUS findings, the cystic fluid ratio, and operative methods (P = 0.000, P = 0.001, P = 0.024, P = 0.002). The possibility of intraoperative nodular intracapsular hemorrhage was predicted by the model based on the risk factors with the accuracy of 81% and prediction consistency index (C-index) of 0.78.ConclusionA new and efficient prediction model was developed based on the identified risk factors for intracapsular hemorrhage during microwave ablation of mixed thyroid nodules, which will aid in the development of targeted surgical planning for different types of cystic thyroid nodules, thus reducing the risk of hemorrhage during ablation.

AimsTo verify whether the oral insulin N11005 is administered as a prandial insulin by assessing the pharmacokinetics (PK), pharmacodynamics (PD), and safety profiles of N11005 with a short-acting biosynthetic human insulin (Novolin R) as reference.MethodsThis was a randomized, open-label, single-dose, crossover hyperinsulinemic-euglycemic clamp study in healthy Chinese male subjects. A total of 12 subjects were enrolled in the test (T) group (N11005, 300 IU, p.o.) and the reference (R) group (Novolin R, 0.1 IU/Kg, i.h.) with a washout period of 14 days. All subjects were administered on the same day of the clamp study. Glucose Infusion Rates (GIR), serum insulin, and C-peptide concentration were determined during every 8-hour clamp cycle. Trial registration: Clinicaltrials.gov identifier NCT04975022.ResultsAfter administration, the ratios of mean serum C-peptide concentration to baseline concentration in both T and R groups were lower than 50%, which confirmed the stability of the clamp platform. T group (N11005) showed a more rapid onset of action (tGIR10%max≈11 min) and a comparable duration of action to the R group, which was basically in line with the characteristics of prandial insulins. No adverse events (AEs) occurred throughout the study, which demonstrated that N11005 and Novolin R are safe and well-tolerated.ConclusionsThe PD profiles of the single-dose N11005 in the human body are similar to those of prandial insulins, with an excellent safety profile.Clinical trial registrationClinicaltrials.gov, identifier NCT04975022.

Abstract Background To investigate whether carboplatin-liposomal paclitaxel neoadjuvant chemotherapy (NACT) benefits patients with locally advanced cervical cancer (LACC) through avoiding or delaying postoperative radiation. Methods A total of 414 patients with cervical cancer of International Federation of Gynecology and Obstetrics (FIGO 2009) stages IB2–IIA2 were included in the retrospective cohort study, who had received carboplatin-liposomal paclitaxel chemotherapy followed by radical surgery (NACT group) or primary radical surgery (PRS group) between 2007 and 2017 at our hospital. The baseline clinicopathological characteristics at diagnosis, postoperative pathological risk factors, and oncological outcomes after surgery, including postoperative radiation (as adjuvant treatment or treatment of recurrent diseases), progression-free survival (PFS), and overall survival (OS), were compared between the groups. Before treatment, the patients in the NACT group had significantly more advanced tumor stages and larger tumor sizes than those in the PRS group. Results The NACT reduced the tumor volumes remarkedly with a response rate of 62.4%, and the tumors in the NACT group were smaller than those in the PRS group when the patients were subjected to radical surgery. Furthermore, postoperative pathology examination revealed less frequent deep stromal invasion in the NACT group than in the PRS group. According to the presence of pathological risk factors for recurrence, 54.82% of women in the NACT group needed adjuvant radiotherapy, while 60.87% in the PRS group, and in fact, 33.00% of NACT patients and 40.09% of PRS patients received adjuvant radiation. In addition, 8.12% of NACT patients and 9.68% of PRS patients underwent radiotherapy after relapse. The cumulative postoperative radiation rate was significantly lower in the NACT group (P = 0.041), while the differences in 5-year OS and PFS were not statistically significant between the groups. Conclusions NACT reduces the pathological risk factors and the use of radiation without compromising survival in patients with LACC, which may protect younger patients from radiation-related side effects and subsequently improve the quality of life. Trial registration ISRCTN Registry, ISRCTN24104022

Multi-exposure image fusion (MEF) is a computational approach that amalgamates multiple images, each captured at varying exposure levels, into a singular, high-quality image that faithfully encapsulates the visual information from all the contributing images. Deep learning-based MEF methodologies often confront obstacles due to the inherent inflexibilities of neural network structures, presenting difficulties in dynamically handling an unpredictable amount of exposure inputs. In response to this challenge, we introduce Ref-MEF, a method for color image multi-exposure fusion guided by a reference image designed to deal with an uncertain amount of inputs. We establish a reference-guided exposure correction (REC) module based on channel attention and spatial attention, which can correct input features and enhance pre-extraction features. The exposure-guided feature fusion (EGFF) module combines original image information and uses Gaussian filter weights for feature fusion while keeping the feature dimensions constant. The image reconstruction is completed through a gated context aggregation network (GCAN) and global residual learning GRL. Our refined loss function incorporates gradient fidelity, producing high dynamic range images that are rich in detail and demonstrate superior visual quality. In evaluation metrics focused on image features, our method exhibits significant superiority and leads in holistic assessments as well. It is worth emphasizing that as the number of input images increases, our algorithm exhibits notable computational efficiency.

The propagation behavior of stress corrosion cracking (SCC) for two CrNiMoV steels in the simulated environment of low-pressure nuclear steam turbine rotor was systematically investigated by microstructural characterizations, electrochemical measurements, slow strain rate tensile tests, and crack growth rate (CGR) measurements. The results showed that both the corrosion susceptibility and stress corrosion susceptibility increased with the increase of localized residual strain, yield strength, and the decrease of grain size. With the higher stress corrosion susceptibility, the SCC CGR of Steel A was higher than Steel B in the corrosive solution at 80 °C, however, at 180 °C the SCC CGR of Steel A was lower than Steel B under the lower applied K. The change of SCC CGR was strongly affected by crack branches, crack deflections and plastic deformation during the SCC propagation, which may have some correlations with the microstructural distribution.

Piezoelectric materials that can effectively convert natural mechanical energy into electrical energy without time and space constraints have been widely applied for energy harvesting and conversion. The piezocomposites with high piezoelectricity and flexibility have shown great promise for renewable electric energy generation that can power implantable and wearable electronics. This minireview aims to summarize the recent progress of the piezocomposites with different composite structures, as well as the role of the theoretical understandings and designs in the development of new piezoelectric nanogenerator materials. Thereinto, the most common composite structural types (0-3, 1-3, and 3-3) have been discussed systematically. Several strategies for high output performance of piezocomposites are also proposed on the basis of current experimental and simulation results. Finally, the review concludes with perspectives on the future design of flexible piezoelectric nanocomposites for energy harvesters.

Background Ferroptosis and long-noncoding RNAs (lncRNAs) play crucial roles in doxorubicin (DOX)-induced myocardial injury (DIMI). Nevertheless, there is no research to construct competing endogenous RNAs (ceRNAs) network between lncRNAs and ferroptosis-related key gene. So our research was designed to screen ferroptosis-related genes from differentially expressed mRNAs in DIMI and construct lncRNAs regulated ferroptosis-related key gene ceRNAs network. Methods The male mice were injected with DOX intraperitoneally to induce myocardial injury, myocardial injury was evaluated by hematoxylin and eosin (HE) staining, and ferroptosis-related protein-glutathione peroxidase 4 (GPx4) protein expression was detected. The differentially expressed lncRNAs and mRNAs were detected by microarray, and the ferroptosis-related genes were screened to construct a protein-protein associations (PPA) network, the highest maximal clique centrality (MCC) score gene were identified by Cytoscape software, miRNAs bound to key genes and lncRNAs bound to miRNAs were predicted; then, the obtained lncRNAs were intersected with differentially expressed lncRNAs detected by microarray. Finally, the lncRNA/miRNA/mRNA ceRNA network of the highest MCC score gene regulating ferroptosis in DIMI was constructed. The expressions of the key components in ceRNA network were detected by qRT-PCR. Results Compared with the control group, in the DOX group, myocardial enzymes and HE staining showed that myocardium structure was changed, and GPx4 protein expression was decreased. The differentially expressed 10,265 lncRNAs and 6,610 mRNAs in the DOX group were detected via microarray. Among them, 114 ferroptosis-related genes were obtained to construct PPA networks, and Becn1 was identified as the key gene. Finally, the ceRNA network including Becn1, three miRNAs and four lncRNAs was constructed by predicting data of the Starbase database. The relative expressions of these components in ceRNA net were up-regulated and consistent with microarray results. Conclusions Based on the microarray detection results and bioinformatics analysis, we screened ferroptosis-related gene Becn1 and constructed the lncRNA/miRNA/mRNA ceRNA network of regulated ferroptosis in DIMI.

Anti-PD-L1 therapy exhibits durable efficacy, but only in a small fraction of cancer patients. The immunosuppressive tumor microenvironment (TME) is a crucial obstacle that impedes cancer immunotherapy. Here, we found that anti-PD-L1 therapy coupled with CD4+ T cell depletion induced colorectal tumor regression and vascular normalization, while monotherapy only retarded tumor growth without affecting the tumor vasculature. Moreover, simultaneous PD-L1 blockade and CD4+ T cell depletion eradicated intratumoral PD-L1+ lymphoid and myeloid cell populations, while additively elevating the proportions of CD44+CD69+CD8+, central memory CD44+CD62L+CD8+, and effector memory CD44+CD62L-CD8+ T cells, suggesting a reduction in immunosuppressive cell populations and the activation of CD8+ T cells in the TME. Moreover, anti-PD-L1 therapy reduced the proportions of intratumoral PD-L1+ immune cells and suppressed tumor growth in a CD8+ T cell dependent manner. Together, these results suggest that anti-PD-L1 therapy induces tumor vascular normalization and colorectal tumor regression via CD8+ T cells, which is antagonized by CD4+ T cells. Our findings unveil the positive correlation of tumor regression and vascular normalization in colorectal tumor models upon anti-PD-L1 therapy, providing a potential new strategy to improve its efficacy.

Abstract Understanding the shifts in competitive ability and its driving forces is key to predict the future of plant invasion. Changes in the competition environment and soil biota are two selective forces that impose remarkable influences on competitive ability. By far, evidence of the interactive effects of competition environment and soil biota on competitive ability of invasive species is rare. Here, we investigated their interactive effects using an invasive perennial vine, Mikania micrantha. The competitive performance of seven M. micrantha populations varying in their conspecific and heterospecific abundance were monitored in a greenhouse experiment, by manipulating soil biota (live and sterilized) and competition conditions (competition‐free, intraspecific, and interspecific competition). Our results showed that with increasing conspecific abundance and decreasing heterospecific abundance, (1) M. micrantha increased intraspecific competition tolerance and intra‐ vs. interspecific competitive ability but decreased interspecific competition tolerance; (2) M. micrantha increased tolerance of the negative soil biota effect; and (3) interspecific competition tolerance of M. micrantha was increasingly suppressed by the presence of soil biota, but intraspecific competition tolerance was less affected. These results highlight the importance of the soil biota effect on the evolution of competitive ability during the invasion process. To better control M. micrantha invasion, our results imply that introduction of competition‐tolerant native plants that align with conservation priorities may be effective where M. micrantha populations are long‐established and inferior in inter‐ vs. intraspecific competitive ability, whereas eradication may be effective where populations are newly invaded and fast‐growing.

Abstract Objective Although radiotherapy (RT) has been widely used in cancer treatment, it provides limited benefits in patients with metastatic cancers due to rare abscopal antitumor effects. Recent progress in cancer immunotherapy provides a potential new strategy to boost the abscopal antitumor effects of RT. Methods We fused interleukin 10 (IL10) dimer onto the anti‐epidermal growth factor receptor antibody cetuximab (Erbitux) to form a new bispecific protein Erb‐(IL10)2. The antitumor effect and biological activity of Erb‐(IL10)2 were measured in the B16‐EGFR‐OVA tumor model. In vivo cell depletion and flow cytometry analyses were used to assess the mechanism of antitumor effects. Results Erb‐(IL10)2 treatment alone showed modest tumor growth inhibition, while local single dose RT (10 Gy) retarded irradiated tumor growth without affecting the growth of nonirradiated tumors. Notably, the combination therapy of RT and Erb‐(IL10)2 not only additively inhibited irradiated tumor growth but also induced abscopal antitumor responses. In vivo depletion of CD8+ T cells abrogated the combinational antitumor effects, while blocking lymphocyte trafficking through FTY720 treatment abolished the abscopal antitumor responses without affecting the antitumor effects on the irradiated tumor sites. Conclusion This study provides evidence for the radiosensitive role of Erb‐(IL10)2 in the B16‐EGFR‐OVA tumor model. Our findings suggest a novel strategy to elicit the abscopal antitumor effects of RT by combining tumor‐targeted therapy with IL10.

Summary: Phase-field simulation is a powerful tool for understanding lithium metal electrodeposition. This protocol outlines the process of numerically solving the phase-field equations using the MOOSE framework. Here, we describe steps to obtain the spatiotemporal distribution of major physical characteristics such as phase-field, ion concentration, overpotential, and driving force. Such an approach may help to reveal the underlying physics and kinetics of dendrite growth, while also providing design principles for suppressing lithium dendrites.For complete details on the use and execution of this protocol, please refer to Hong and Viswanathan (2018). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Anlotinib is a new multitarget tyrosine kinase inhibitor for tumor angiogenesis, and its monotherapy exhibits a decent clinical efficacy. However, the process of combining Anlotinib and immune checkpoint therapy to achieve optimal antitumor effects while limiting side effects remains unclear. In this study, we found that effective low-dose Anlotinib was sufficient to inhibit tumor growth while reducing side effects compared with high doses. Effective low-dose Anlotinib treatments induced durable tumor vascular normalization and improved anti-PD-1 therapy in both short- and long-term treatment regimens. Mechanistically, the combination therapy increased the proportions of intratumoral CD4+ T, CD8+ T, and NK cells. Anlotinib-associated antitumor effects were independent of interferon γ; however, the combination therapy required CD8+ T cells to suppress tumor growth. Together, these results suggest that the combination of effective low-dose Anlotinib and PD-1 blockade induces durable antitumor effects with fewer side effects. Our findings indicate that antiangiogenic treatments combined with immune checkpoint therapy at an effective low-dose, rather than a tolerable high dose, would be more efficacious and safer.

Multiple myeloma (MM) remains a common hematologic malignancy with a 10-year survival rate below 50%, which is largely due to disease relapse and resistance. The lack of a simple and practical approach to establish myeloma patient-derived xenograft (PDX) hampers translational myeloma research. Here, we successfully developed myeloma PDXs by subcutaneous inoculation of primary mononuclear cells from MM patients following series tumor tissue transplantations. Newly established myeloma PDXs retained essential cellular features of MM and recapitulated their original drug sensitivities as seen in the clinic. Notably, anlotinib therapy significantly suppressed the growth of myeloma PDXs even in bortezomib-resistant model. Anlotinib treatments polarized tumor-associated macrophages from an M2- to an M1-like phenotype, decreased tumor vascular function, and accelerated cell apoptosis in myeloma PDXs. Our preclinical work not only unveiled the potency of anlotinib to overcome bortezomib resistance, but also provided a more practical way to establish MM PDX to facilitate myeloma research.

BackgroundArtificial intelligence (AI) has shown promising results in various fields of medicine. It has the potential to facilitate shared decision making (SDM). However, there is no comprehensive mapping of how AI may be used for SDM. ObjectiveWe aimed to identify and evaluate published studies that have tested or implemented AI to facilitate SDM. MethodsWe performed a scoping review informed by the methodological framework proposed by Levac et al, modifications to the original Arksey and O'Malley framework of a scoping review, and the Joanna Briggs Institute scoping review framework. We reported our results based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting guideline. At the identification stage, an information specialist performed a comprehensive search of 6 electronic databases from their inception to May 2021. The inclusion criteria were: all populations; all AI interventions that were used to facilitate SDM, and if the AI intervention was not used for the decision-making point in SDM, it was excluded; any outcome related to patients, health care providers, or health care systems; studies in any health care setting, only studies published in the English language, and all study types. Overall, 2 reviewers independently performed the study selection process and extracted data. Any disagreements were resolved by a third reviewer. A descriptive analysis was performed. ResultsThe search process yielded 1445 records. After removing duplicates, 894 documents were screened, and 6 peer-reviewed publications met our inclusion criteria. Overall, 2 of them were conducted in North America, 2 in Europe, 1 in Australia, and 1 in Asia. Most articles were published after 2017. Overall, 3 articles focused on primary care, and 3 articles focused on secondary care. All studies used machine learning methods. Moreover, 3 articles included health care providers in the validation stage of the AI intervention, and 1 article included both health care providers and patients in clinical validation, but none of the articles included health care providers or patients in the design and development of the AI intervention. All used AI to support SDM by providing clinical recommendations or predictions. ConclusionsEvidence of the use of AI in SDM is in its infancy. We found AI supporting SDM in similar ways across the included articles. We observed a lack of emphasis on patients’ values and preferences, as well as poor reporting of AI interventions, resulting in a lack of clarity about different aspects. Little effort was made to address the topics of explainability of AI interventions and to include end-users in the design and development of the interventions. Further efforts are required to strengthen and standardize the use of AI in different steps of SDM and to evaluate its impact on various decisions, populations, and settings.

BackgroundRapid intravenous thrombolysis (IVT) for acute ischemic stroke (AIS) is crucial for improving outcomes. However, few randomized trials of interventions aimed at reducing in-hospital delay have been carried out in China. We aimed to evaluate the effect of a multicomponent intervention on thrombolytic door-to-needle time (DNT) of AIS patients via video teleconference based on the Behavior Change Wheel (BCW) method.Methods and findingsThis cluster-randomized trial, conducted between January 1, 2019 and December 31, 2019, randomly allocated 22 hospitals equally to PEITEM (Persuasion Environment reconstruction Incentivization Training Education Modeling) intervention or routine care plus stroke registry and subsequently enrolled 1,634 AIS patients receiving IVT within 4.5 hours upon stroke onset from participant hospitals. The PEITEM group received a 1-year PEITEM 6-component intervention based on the behavioral theory monthly via video teleconference. The primary outcome was the proportion of patients with a DNT of 60 minutes or less. A total of 987 patients participated in the PEITEM group (mean age, 69 years; female, 411 [41.6%]) and 647 patients in the control group (mean age, 70 years; female, 238 [36.8%]). Of all participants, the proportion of DNT ≤60 minutes in the PEITEM group was higher than in the control group (82.0% versus 73.3%; adjusted odds ratio, 1.77; 95% confidence interval (CI), 1.17 to 2.70; ICC, 0.04; P = 0.007). Among secondary outcomes, the average DNT was 43 minutes in the PEITEM group and 50 minutes in the control group (adjusted mean difference: -8.83; 95% CI, -14.03 to -3.64; ICC, 0.12; P = 0.001). Favorable functional outcome (score of 0 to 1 on the modified Rankin scale (mRS)) was achieved in 55.6% patients of the PEITEM group and 50.4% of the control group (adjusted odds ratio, 1.38; 95% CI, 1.00 to 1.90; ICC, 0.01; P = 0.049). Main study limitations include non-blinding of clinicians, and that specific interventions component responsible for the observed changes could not be determined.ConclusionsThe teleconference-delivered PEITEM intervention resulted in a moderate but clinically relevant shorter DNT and better functional outcome in AIS patients receiving IVT.Trial registrationClinicaltrials.gov NCT03317639.

Abstract Metal oxide-based Resistive Random-Access Memory (RRAM) exhibits multiple resistance states, arising from the activation/deactivation of a conductive filament (CF) inside a switching layer. Understanding CF formation kinetics is critical to achieving optimal functionality of RRAM. Here a phase-field model is developed, based on materials properties determined by ab initio calculations, to investigate the role of electrical bias, heat transport and defect-induced Vegard strain in the resistive switching behavior, using MO2−x systems such as HfO2−x as a prototypical model system. It successfully captures the CF formation and resultant bipolar resistive switching characteristics. High-throughput simulations are performed for RRAMs with different material parameters to establish a dataset, based on which a compressed-sensing machine learning is conducted to derive interpretable analytical models for device performance (current on/off ratio and switching time) metrics in terms of key material parameters (electrical and thermal conductivities, Vegard strain coefficients). These analytical models reveal that optimal performance (i.e., high current on/off ratio and low switching time) can be achieved in materials with a low Lorenz number, a fundamental material constant. This work provides a fundamental understanding to the resistive switching in RRAM and demonstrates a computational data-driven methodology of materials selection for improved RRAM performance, which can also be applied to other electro-thermo-mechanical systems.

Abstract Piezoelectric nanocomposites with oxide fillers in a polymer matrix combine the merit of high piezoelectric response of the oxides and flexibility as well as biocompatibility of the polymers. Understanding the role of the choice of materials and the filler‐matrix architecture is critical to achieving desired functionality of a composite towards applications in flexible electronics and energy harvest devices. Herein, a high‐throughput phase‐field simulation is conducted to systematically reveal the influence of morphology and spatial orientation of an oxide filler on the piezoelectric, mechanical, and dielectric properties of the piezoelectric nanocomposites. It is discovered that with a constant filler volume fraction, a composite composed of vertical pillars exhibits superior piezoelectric response and electromechanical coupling coefficient as compared to the other geometric configurations. An analytical regression is established from a linear regression‐based machine learning model, which can be employed to predict the performance of nanocomposites filled with oxides with a given set of piezoelectric coefficient, dielectric permittivity, and stiffness. This work not only sheds light on the fundamental mechanism of piezoelectric nanocomposites, but also offers a promising material design strategy for developing high‐performance polymer/inorganic oxide composite‐based wearable electronics.

Non-contact detection methods based on computer vision are widely used in industrial production. When collecting images, different light source schemes are often used to meet the detection requirements of different objects. The image styles collected under different lighting scenes are diverse, and the image semantic segmentation model trained with a specific dataset has unsatisfactory performance when processing images in different domain. This paper designs a mechanical part image semantic segmentation model based on domain adaptation and GAN. In order to verify the effectiveness of the proposed method, this paper collects and labels some gear images and constructs a dataset. The first step is to train encode-decoder structure with an enhanced memory module with source dataset to achieve semantic segmentation, then the dataset is transformed into a target domain by GAN, and finally the image semantic segmentation model is fine-tuned with the target dataset. The advantage is that since the cycle-consistency loss is used to constrain the spatial structure of the reconstructed image, the two datasets can share a semantic segmentation label. Experiments show that the image semantic segmentation method based on domain adaptation has achieved good results on different styles of gear parts image datasets, and achieved a pixel accuracy of 94.1%.

Replant disease caused by continuous cropping commonly occurs in yam with consecutive monoculture. However, little is known about how the continuous cropping of yam affects the rhizospheric soil bacterial community structure. In this study, the effects of continuous cropping on rhizospheric soil characteristics, bacterial diversity, and community structure were investigated in the Yongfeng yam fields under monoculture for 1, 5, 10, 15, and 20 years. Long-term monoculture caused soil acidification and increased the concentration of available potassium (AK) and available phosphorus (AP), and soil bacterial richness, but decreased the soil bacterial diversity. An exception was for the field under monoculture for 20 years as it showed the highest bacterial diversity. The relative abundance of beneficial bacteria, such as Proteobacteria, Actinobacteria, and Chloroflexi decreased while the relative abundance of harmful bacteria, including Gemmatimonadetes and Acidobacteria, increased with an extended continuous cultivation time. The networks varied among yams with different cultivation years and became complex with the increase in cultivation years. However, after time in monoculture, the bacterial network decreased gradually and existed stably. These changes in bacterial community composition and co-occurrence of networks may increase the potential risk of soil-borne disease and reduce the yield and quality of Yongfeng yam.

Convolutional neural networks have long dominated semantic segmentation of very-high-resolution (VHR) remote sensing (RS) images. However, restricted by the fixed receptive field of convolution operation, convolution-based models cannot directly obtain contextual information. Meanwhile, Swin Transformer possesses great potential in modeling long-range dependencies. Nevertheless, Swin Transformer breaks images into patches that are single-dimension sequences without considering the position loss problem inside patches. Therefore, Inspired by Swin Transformer and Unet, we propose SUD-Net (Swin transformer-based Unet-like with Dynamic attention pyramid head Network), a new U-shaped architecture composed of Swin Transformer blocks and convolution layers simultaneously through a dual encoder and an upsampling decoder with a Dynamic Attention Pyramid Head (DAPH) attached to the backbone. First, we propose a dual encoder structure combining Swin Transformer blocks and reslayers in reverse order to complement global semantics with detailed representations. Second, aiming at the spatial loss problem inside each patch, we design a Multi-Path Fusion Model (MPFM) with specially devised Patch Attention (PA) to encode position information of patches and adaptively fuse features of different scales through attention mechanisms. Third, a Dynamic Attention Pyramid Head is constructed with deformable convolution to dynamically aggregate effective and important semantic information. SUD-Net achieves exceptional results on ISPRS Potsdam and Vaihingen datasets with 92.51%mF1, 86.4%mIoU, 92.98%OA, 89.49%mF1, 81.26%mIoU, and 90.95%OA, respectively.

During laser-metal inert gas (MIG) hybrid welding, a large amount of welding status information is generated in droplet transfer, keyhole and molten pool. In this paper, austenitic stainless steel was adopted as an experimental object, with a dual high-speed camera system used to obtain real-time images of droplet transfer, keyhole and molten pool in a laser-MIG hybrid welding process. The changing regulation of a keyhole in three different penetration states (i.e., non-penetration, partial penetration and normal penetration) was analyzed by extracting the morphological characteristics of a keyhole shape, and combining the droplet transition information and the shape of the weld pool. Experimental results show that the proposed method could effectively reflect the variation characteristics of the keyhole, and the correlation among the keyhole characteristics, the droplet transfer information, the weld pool shape and the welding status, and provide a new perspective for online detection of the laser-MIG welding quality.

Dicranopteris is an ancient and widespread genus of ferns in pantropical regions. Some species of the genus can form dense thickets, and dominate the understory, which are common and key species in tropical and subtropical ecosystems. However, they were mostly cut or burned in forest management because of forming dense thickets which were considered to interfere with forest regeneration and succession. In the current review, we argue that the Dicranopteris species which are able to rapidly colonize barren areas may contribute to ecosystem recovery, resistance to environmental stress, and succession control. Rapid colonization involves prolific spore production, rapid clonal growth, the generation of high surface cover, and the ability to fill gaps; stress resistance includes resistance to abiotic stress, and the ability to reduce soil erosion from rainfall, alien species invasion, and soil contamination and toxicity; and succession facilitation consists of carbon and nutrient sequestration in soil, moderation of the microclimate, alteration of the soil microbial and faunal communities, and determination of which plant species to be established in the next successional stage. All of these ecosystem functions may be beneficial to ecosystem resilience. We expect that the distribution of Dicranopteris will expand in response to global warming, changes in precipitation patterns, increases in soil pollution, deforestation, and land degradation. We recommend that Dicranopteris, as a pioneer fern and a valuable component of tropical and subtropical ecosystems, needs more attention in future research and better management practices to promote forest regeneration and succession.

Background: Innate lymphoid cells (ILCs) are a newly discovered family of immune cells that have similar cytokine-secreting profiles as T helper cell subsets. Although ILCs are critical for host defense against infections and tissue homeostasis, their roles in tumor development are not well established. Methods: We studied the function of ILC3 cells in the liver for the development of hepatocellular carcinoma (HCC) in murine HCC models using flow cytometry, adoptive transfer, and in vitro functional assays. Findings: We found that ILC3 lacking the natural cytotoxicity-triggering receptor (NCR−ILC3) promoted the development of HCC in response to interleukin 23 (IL-23). IL-23 serum level is elevated in HCC patients and its high expression is associated with poor clinical outcomes. We found that IL-23 could promote tumor development in murine HCC tumor models. IL-23 promoted the expansion of NCR−ILC3 and its differentiation from group 1 ILCs (ILC1s). Furthermore, NCR−ILC3 initiated IL-17 production upon IL-23 stimulation and directly inhibited CD8+ T cell immunity by promoting lymphocyte apoptosis and limiting their proliferation. Interpretation: Together, our findings suggest that NCR−ILC3 initiates the IL-17-rich immunosuppressive tumor microenvironment and promotes the development of HCC, thus may serve as a promising target for future cancer immunotherapy. Fund: This work was supported by grants from National Natural Science Foundation of China (81471586, 81571556), the Priority Academic Program Development of Jiangsu Higher Education Institutions, the collaborative Innovation Center of Hematology, start-up grant from National University of Singapore, the Cancer Prevention and Research Institute of Texas CPRIT (RR180017), and the National Cancer Institute's Cancer Center Support (Core) Grant CA016672 (to The University of Texas MD Anderson Cancer Center). Keywords: IL-23, IL-17, ILC, Tumor microenvironment, HCC

With the large-scale configuration of distribution automation and the widespread implementation of distributed generation (DG) and flexible load (FL), the distribution network can achieve active regulation, which makes the traditional fault restoration strategy and distribution network reliability evaluation more complicated. Given this background, a method of reliability evaluation for the active distribution network considering ‘supply-grid-load’ is proposed. On the supply side, a distribution network islanding model considering DG is established, with the objective of supplying power to the maximised amount of equivalent power load. On the grid side, the influence of distribution network automation on the distribution network reliability is elaborately analysed and the time-varying failure rate model of equipment which considers the life cycle characteristics is presented. On the load side, an incentive-based FL model is proposed based on the elastic coefficient matrix. Finally, simulation results of RBTS-BUS6 system demonstrate the effectiveness of the proposed model and method.

Abstract Background Antiangiogenic agents are commonly used in lung and colon cancer treatments, however, rapid development of drug resistance limits their efficacy. Methods Lentinan (LNT) is a biologically active compound extracted from Lentinus edodes. The effects of LNT on tumor angiogenesis were evaluated by immunohistochemistry in murine LAP0297 lung and CT26 colorectal tumor models. The impacts of LNT on immune cells and gene expression in tumor tissues were determined by flow cytometry, qPCR, and ELISA. Nude mice and IFNγ blockade were used to investigate the mechanism of LNT affecting on tumor angiogenesis. The data sets were compared using two-tailed student’s t tests or ANOVA. Results We found that LNT inhibited tumor angiogenesis and the growth of lung and colon cancers. LNT treatments elevated the expression of angiostatic factors such as IFNγ and also increased tumor infiltration of IFNγ-expressing T cells and myeloid cells. Interestingly, IFNγ blockade, but not T cell deficiency, reversed the effects of LNT treatments on tumor blood vessels. Moreover, long-lasting LNT administration persistently suppressed tumor angiogenesis and inhibited tumor growth. Conclusions LNT inhibits tumor angiogenesis by increasing IFNγ production and in a T cell-independent manner. Our findings suggest that LNT could be developed as a new antiangiogenic agent for long-term treatment of lung and colon cancers.

Abstract Recurrence following chemotherapy is observed in the majority of patients with pancreatic ductal adenocarcinoma (PDAC). Recent studies suggest that cancer stem cells (CSCs) may be involved in PDAC recurrence and metastasis. However, an efficient approach to targeting pancreatic CSCs remains to be established. Here we show that in cancer cells overexpressing the 67-kDa laminin receptor (67LR)-dependent cyclic GMP (cGMP) inducer, epigallocatechin-3-O-gallate (EGCG) and a phosphodiesterase 3 (PDE3) inhibitor in combination significantly suppressed the Forkhead box O3 and CD44 axis, which is indispensable for the CSC properties of PDAC. We confirmed that the EGCG and PDE3 inhibitor in combination strongly suppressed tumour formation and liver metastasis in vivo. We also found that a synthesized EGCG analog capable of inducing strong cGMP production drastically suppressed the CSC properties of PDAC and extended the survival period in vivo. In conclusion, the combination treatment of EGCG and a PDE3 inhibitor as a strong cGMP inducer could be a potential treatment candidate for the eradication of CSCs of PDAC.