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Sustainable management of the water–energy–food (WEF) nexus remains an urgent challenge, as interactions between WEF and human sensitivity and reservoir operation in the water system are typically neglected. This study proposes a new approach for modeling the WEF nexus by incorporating human sensitivity and reservoir operation into the system. The co-evolution behaviors of the nexus across water, energy, food, and society (WEFS) were simulated using the system dynamic model. Reservoir operation was simulated to determine the water supply for energy and food systems by the Interactive River–Aquifer Simulation water resources allocation model. Shortage rates for water, energy, and food resulting from the simulations were used to qualify their impacts on the WEFS nexus through environmental awareness in society. Human sensitivity indicated by environmental awareness can then adjust the co-evolution behaviors of the WEFS nexus through feedback loops. The proposed approach was applied to the mid–lower reaches of the Hanjiang River basin in China as a case study. Results indicate that environmental awareness shows the potential to capture human sensitivity to shortages from water, energy, and food systems. Parameters related to boundary conditions and critical values can dominate environmental awareness feedback to regulate socioeconomic expansion to maintain the integrated system from constant resource shortages. The annual average energy shortage rate thereby decreased from 17.16 % to 5.80 % by taking environmental awareness feedback, which contributes to the sustainability of the WEFS nexus. Rational water resources allocation can ensure water supply through reservoir operation. The annual average water shortage rate decreased from 15.89 % to 7.20 % as water resources allocation was considered. Threats from water shortage on the concordant development of the WEFS nexus are significantly alleviated, particularly for the area with a limited regulating capacity for the water project. Therefore, this study contributes to the understanding of interactions across the WEFS systems and helps in improving the efficiency of resource management.

To investigate the optimal androgen concentration for culturing Hetian sheep wool follicle and to detect effects of androgen concentration on wool follicle cell proliferation and apoptosis using immunofluorescence labeling and real-time quantitative fluorescence determinations of wool keratin-associated protein gene expression levels. Wool follicles were isolated by microdissection and wool follicles and skin pieces were cultured in various concentrations of dihydrotestosterone (DHT) in culture medium. Next, daily lengthwise growth measurements of wool follicles were obtained using a microscopic micrometer. Cultured Hetian wool follicles were stained using the SACPIC method to reveal wool follicle structure, while sheep skin slices were used to observe cell proliferation by immunostaining and cell apoptosis using the TUNEL method. At the molecular biological level, keratin-associated protein (Kap) gene expression was studied using wool follicles cultured for various numbers of days in vitro. Effects of androgen concentrations on Hetian wool follicle growth and development were experimentally studied. EdU proliferation assays revealed that androgen promoted cell proliferation within wool follicle dermal papillae. TUNEL apoptosis detection demonstrated that androgen treatment could delay cell apoptosis. Quantitative reverse transcription polymerase chain reaction (qPCR) results demonstrated that gene expression level patterns of Hetian mountain sheep super-high sulfur protein. Kap1.1, KIF1.2, Kap2.12 and Kap4.2 gene expression level of the mountainous experimental group was significantly higher than plains Hetian sheep. An androgen concentration of 100 nM can promote the growth of Hetian wool follicle cells in vitro, resulting in overexpression of some genes of the Kap family. Resumo Investigar a concentração ideal de andrógenos em cultura de folículos pilosos de carneiro Hetiano e detectar os efeitos da concentração de andrógenos na proliferação e apoptose de células foliculares, por meio de imunofluorescência e de determinação quantitativa, em tempo real, da fluorescência dos níveis de expressão gênica de proteína associada à queratina. Folículos pilosos foram isolados por microdissecção, e folículos de lã e pedaços de pele foram cultivados em várias concentrações de di-hidrotestosterona (DHT) em meio de cultura. Em seguida, medições diárias de crescimento longitudinal dos folículos capilares foram obtidas usando um micrômetro microscópico. Folículos de lã cultivados de Hetianos foram corados pelo método SACPIC para revelar a estrutura do folículo piloso, enquanto fatias de pele de carneiro foram usadas para observar a proliferação celular por imunocoloração e apoptose celular por meio do método TUNEL. Em âmbito da biologia molecular, a expressão gênica da proteína associada à queratina (Kap) foi estudada usando folículos capilares cultivados por vários dias, in vitro. Os efeitos das concentrações de andrógenos no crescimento e desenvolvimento dos folículos de lã de Hetianos foram estudados experimentalmente. Ensaios de proliferação de EdU revelaram que o andrógeno promoveu a proliferação celular dentro das papilas dérmicas do folículo piloso. A detecção de apoptose por TUNEL demonstrou que o tratamento com andrógeno poderia atrasar a apoptose celular. Os resultados da reação em cadeia da polimerase transcrição reversa quantitativa (qPCR) demonstraram que os padrões de expressão gênica da proteína de enxofre Kap1.1, KIF1.2, Kap2.12 e Kap4.2 foram significativamente maiores no grupo de ovinos Hetianos de montanha. Uma concentração de androgênio de 100 nM pode promover o crescimento de células foliculares de lã de Hetianos in vitro, resultando na superexpressão de alguns genes da família Kap.

Hitherto, there has been insufficient work to quantify the degree of mutual feedback among the different objectives of multi-objective reservoir operation. The purpose of this study is to use a Multi-objective Correlation Index (MCI) to quantitatively analyze the complicated relationship of a multi-objective reservoir operation under changing inflow and water demands. First, an improved maximum probability density function method has been used to generate the adaptive ecological flow thresholds in long-term, wet-year, normal-year and dry-year inflow scenarios. Based on these ecological flow thresholds, a multi-objective reservoir optimal operation including the three objectives of power generation, water supply and ecology is constructed to research the tradeoff relationship among the objectives. Moreover, using the optimal solution set as determined by the Progressive Optimality Algorithm-Particle Swarm Optimization (POA-PSO) algorithm, the MCI is used to quantify the degree of the tradeoffs and their trends in responding to the changing conditions. The results show that the synergistic degree between the water supply and ecological objective decreases when the climatic condition changes from wet years to dry years, while the conflicting degree of power generation with respect to water supply or ecological objectives increases. Furthermore, the major degree of tradeoffs changes from the power generation-ecological flow objective pair to the power generation-water supply objective pair. In general, the MCI is able to quantify the extent and characteristics of the tradeoffs between different objectives. Hence, this index is useful for managers to make more informed and transparent decisions.

ObjectiveStem cells hold promise for treating hair loss. Here an in vitro mouse model was developed using outer root sheaths (ORSs) isolated from hair follicles for studying stem cell-mediated dermal papillary regeneration.MethodsUnder sterile conditions, structurally intact ORSs were isolated from hair follicles of 3-day-old Kunming mice and incubated in growth medium. Samples were collected daily for 5 days. Stem cell distribution, proliferation, differentiation, and migration were monitored during regeneration.ResultsCell proliferation began at the glass membrane periphery then spread gradually toward the membrane center, with the presence of CD34 and CD200 positive stem cells involved in repair initiation. Next, CD34 positive stem cells migrated down the glass membrane, where some participated in ORS formation, while other CD34 cells and CD200 positive cells migrated to hair follicle centers. Within the hair follicle matrix, stem cells divided, grew, differentiated and caused outward expansion of the glass membrane to form a dermal papillary structure containing alpha-smooth muscle actin. Neutrophils attracted to the wound site phagocytosed bacterial and cell debris to protect regenerating tissue from infection.ConclusionIsolated hair follicle ORSs can regenerate new dermal papillary structuresin vitro. Stem cells and neutrophils play important roles in the regeneration process.

Sustainable management of water-energy-food (WEF) nexus remains an urgent challenge, as interactions between WEF and community sensitivity and reservoir operation in water system are often neglected. This paper aims to provide a new approach for modeling WEF nexus by incorporating community sensitivity and reservoirs operation into the system. The co-evolution behaviors of the nexus across water, energy, food and society (WEFS) were simulated by the system dynamic model. The reservoirs operation was simulated to determine water supply for energy and food systems by the Interactive River-Aquifer Simulation water resources allocations model. Shortage rates for water, energy and food resulted from the simulations were used to qualify their impacts on WEFS nexus through environmental awareness (EA) in society system. Community sensitivity indicated by EA can adjust the co-evolution behaviors of WEFS nexus through feedback loops. The proposed approach was applied to the mid-lower reaches of Hanjiang river basin in China as a case study. Results show that EA accumulation is mainly from shortages of water and energy, and the available water and energy are the vital resources to sustain WEFS nexus. Feedback driven by EA effectively keeps the system from collapsing and contributes to the concordant development of WEFS nexus. Water resources allocation can remarkably ensure water supply through reservoirs operation, decreasing water shortage rate from 16.60 % to 7.53 %. The resource constraining the WEFS nexus is transferred from water to energy. This paper therefore contributes to the understanding of interactions across WEFS system and helps the efficiency improving of resources management.

Natural killer group 2, member D (NKG2D) receptor is a crucial activating receptor in the immune recognition and eradication of abnormal cells by natural killer (NK) cells, and T lymphocytes. NKG2D can transmit activation signals and activate the immune system by recognizing the NKG2D ligands (NKG2D-L) on acute myeloid leukemia (AML) cells. Downregulation of NKG2D-L in AML can circumvent resistance to chemotherapy and immune recognition. Considering this effect, the exploration of targeting the NKG2D/NKG2D-L axis is considered to have tremendous potential for the discovery of novel biomacromolecule antibodies and pharmacological modulators in AML. This review was to outline the impact of NKG2D/NKG2D-L axis on intrinsic immunosurveillance and the development of AML. Furthermore, the NKG2D/NKG2D-L axis related modulators and progress in preclinical and clinical trials was also to be reviewed.

The CXC chemokines belong to a family which includes 17 different CXC members. Accumulating evidence suggests that CXC chemokines regulate tumor cell proliferation, invasion, and metastasis in various types of cancers by influencing the tumor microenvironment. The different expression profiles and specific function of each CXC chemokine in head and neck squamous cell carcinoma (HNSCC) are not yet clarified. In our work, we analyzed the altered expression, interaction network, and clinical data of CXC chemokines in patients with HNSCC by using the following: the Oncomine dataset, cBioPortal, Metascape, String analysis, GEPIA, and the Kaplan–Meier plotter. The transcriptional level analysis suggested that the mRNA levels of CXCL1, CXCL2, CXCL3, CXCL5, CXCL6, CXCL8, CXCL9, CXCL10, CXCL11, and CXCL13 increased in HNSCC tissue samples when compared to the control tissue samples. The expression levels of CXCL9, CXCL10, CXCL11, CXCL12, and CXCL14 were associated with various tumor stages in HNSCC. Clinical data analysis showed that high transcription levels of CXCL2, CXCL3, and CXCL12, were linked with low relapse-free survival (RFS) in HNSCC patients. On the other hand, high CXCL14 levels predicted high RFS outcomes in HNSCC patients. Meanwhile, increased gene transcription levels of CXCL9, CXCL10, CXCL13, CXCL14, and CXCL17 were associated with a higher overall survival (OS) advantage in HNSCC patients, while high levels of CXCL1, and CXCL8 were associated with poor OS in all HNSCC patients. This study implied that CXCL1, CXCL2, CXCL3, CXCL8, and CXCL12 could be used as prognosis markers to identify low survival rate subgroups of patients with HNSCC as well as be potential suitable therapeutic targets for HNSCC patients. Additionally, CXCL9, CXCL10, CXCL13, CXCL14, and CXCL17 could be used as functional prognosis biomarkers to identify better survival rate subgroups of patients with HNSCC.

The system of total water use control index (TWUCI) has been established in China since 2011. However, current TWUCI is static all year round, making a poor adaptation to dynamic inflow and a devia...

Oxidative stress plays an important role in the pathology of liver disorders. Total C-21 steroidal glycosides (TCSGs), isolated from the root tuber of Cynanchum auriculatum Royle ex Wight, have been reported to exert numerous effects, including liver protective and antioxidant effects. In order to investigate the potential mechanisms underlying the protective effects of TCSGs on liver function, the present study used the human normal liver cell line, L02, to evaluate the effects of TCSGs on hydrogen peroxide (H2O2)-induced oxidative injury and inflammatory responses. The L02 cells were pretreated with various concentrations of TCSGs, followed by exposure to 1.5 mM H2O2. Cell viability was determined by a 3-(4,5-dimethylthiazol-2-yl)-2,5-di-phenyltetrazolium bromide (MTT) assay. The levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH) and nitric oxide (NO) were measured using colorimetric assays. The activities of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px) and the production of malondialdehyde (MDA) were also determined. Intracellular reactive oxygen species (ROS) levels were detected using a fluorescent probe. H2O2-induced oxidative toxicity was attenuated following treatment with TCSGs, as indicated by the increase in cell viability, the decreased levels of ALT, AST, LDH, NO, MDA and ROS, and the increased activities of SOD, CAT and GSH-Px. To further explore the possible mechanisms of action of TCSGs, the nuclear factor erythroid 2-related factor 2 (Nrf2) and nuclear factor-κB (NF)-κB pathways were examined. The results revealed that treatment with TCSGs markedly induced Nrf2 nuclear translocation and upregulated the expression of heme oxygenase-1 (HO-1) in the L02 cells damaged by H2O2. In addition, pretreatment with TCSGs inhibited the NF-κB signaling pathway by blocking the degradation of the inhibitor of nuclear factor κBα (IκBα), thereby reducing the expression and nuclear translocation of NF-κB, as well as reducing the expression of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), inducible nitric oxide synthase (iNOS) and cyclooxygenase 2 (COX-2). On the whole, the findings of this study demonstrate that TCSGs can protect L02 cells against H2O2-induced oxidative toxicity and inflammatory injury by increasing the expression of Nrf2 and HO-1, mediated by the NF-κB signaling pathway.

Efficient and accurate photovoltaic (PV) modeling plays an important role in optimal evaluation and operation of PV power systems. Using current–voltage (I-V) curves measured at different operating conditions, a novel extreme learning machine (ELM) based modeling method is proposed for characterizing the electrical behavior of PV modules, which features high training speed and generalization performance. Firstly, a voltage-current grid based method is used to resample each raw measured I-V curve for reducing data redundancy of I-V curves, a slope change based detection method is proposed to exclude the abnormal I-V curves for improving the data quality, and an irradiance-temperature grid based method is applied to downsample the dataset. Secondly, a single hidden-layer feedforward neural network (SLFN) is proposed as the model structure, which is then trained by the ELM learning algorithm. Particularly, the configuration of the ELM is optimized by cross-validation. Finally, the proposed ELM based PV modeling method is verified and tested on the preprocessed large datasets of I-V curves of six PV modules from the National Renewable Energy Laboratory. Moreover, in order to verify the advantage, the ELM based PV modeling is further compared with some commonly used machine learning based methods. Experimental results demonstrate that the proposed ELM based PV modeling method features fast training, high accuracy and generalization performance. The comparison results further indicate that the ELM based model is greatly superior in terms of the training time, and is slightly better than other algorithms in terms of the model accuracy and generalization performance.

Hepatocellular carcinoma (HCC) cases are closely associated with chronic inflammation and fibrosis which is known as hepatic inflammation-fibrosis-cancer (IFC) axis. The aim of this study is to elucidate the development characteristics of the rat HCC model based on IFC axis. The diethylnitrosamine (DEN)-induced rat HCC, which presents a stepwise histopathological progression that is similar to human HCC, was used to analyze the features of the different stages (inflammation, fibrosis, cancer). Rats were injected DEN at a dose of 30 mg/kg body weight twice a week for 11 weeks and the animals were observed until week 20. Time series sera and organ samples from the DEN animal model were collected to evaluate the dynamic changes. It was found that serum biochemical indicators (AST, ALT, ALP, TP, T-BIL, IL-6, TNF-α) from DEN-treated group were higher than that from control group. Fibrosis-related index in serum and live tissue were increased, respectively, from week 4 after DEN treatment. The expression of TGF-β1 and α-SMA in DEN-treated group was higher than that in control group. JAK2/STAT3 signaling was significantly up-regulated in DEN-treated group compared to that in control group. The histological examination confirmed that the hepatocarcinogenesis model was successfully established, and 100% of the animals in the DEN-exposed group developed liver tumors at 20 weeks. According to the pathological changes, the model characterized resulted in three stages: the inflammation stage (week 2–6), the fibrosis stage (week 8–12), and the HCC stage (week 14–20). The results suggested that the HCC development was associated with IFC axis. The serial progression of hepatocarcinogenesis was according to the sequence of hepatic inflammation, fibrosis and then hepatic tumor.

Crack detection is a critical task in routine inspection of building structures. Most of the traditional crack detection methodologies are conducted by human inspectors that may submit inaccurate damage assessments. In recent years, deep learning has produced extremely promising results for various tasks. In this work, a lightweight end-to-end pixel-wise classification architecture called SegNet is employed to segment the structure surface cracks. Compared with other semantic segmentation architectures, SegNet uses pooling indices calculated in the pooling step of the encoder to perform non-linear upsampling in the corresponding decoder, which doesn’t require to learn in the upsample. In this paper, a crack image dataset collected under a variety of complex environment are utilized to train and test the SegNet model, i.e. a self-labeled dataset with 2068 bridge cracks images at the size of \(1024\times 1024\). In order to improve the generalization ability of network data augmentation is used. The experimental results show that the SegNet outperforms the traditional edge detection algorithm, such as Canny and Sobel, in the dataset. The trained SegNet model can be used to segment the cracks in images at any size with the assistant of sliding window scanning technique.

Interlaced roads and unexpected obstacles restrict the blind from traveling. Existing outdoor blind auxiliary systems are bulky or costly, and some of them cannot even feedback the type or distance of obstacles. It is important for auxiliary blind systems to provide navigation, obstacle detection and ranging functions with affordable price and portable size. This paper presents an outdoor navigation system based on smartphone for the visually impaired, which can also help them avoid multi-type dangerous obstacles. Geographic information obtained from GPS receiving module is processed by professional navigation API to provide directional guidance. In order to help the visually impaired avoid obstacle, SSD-MobileNetV2 is retrained by a self-collected dataset with 4500 images, for better detecting the typical obstacles on the road, i.e. car, motorcycle, electric bicycle, bicycle, and pedestrian. Then, a light-weight monocular ranging method is employed to estimate the obstacle’s distance. Based on category and distance, the risk level of obstacle is evaluated, which is timely conveyed to the blind via different tunes. Field tests show that the retrained SSD-MobileNetV2 model can detect obstacles with considerable precision, and the vision-based ranging method can effectively estimate distance.

In recent years, researchers around the world have been researching and improving the technique of 1D calibration of cameras. The previous work has been primarily focused on reducing the motion constraints of one-dimensional calibration objects, however the accuracy of the existing methods still needs to be improved when random noise is introduces. In order to improve the accuracy of the one-dimensional calibration of the camera, in this paper, we propose a new calibration method by combining a weighted similar invariant linear algorithm and an improved nonlinear optimization algorithm. Specifically, we use the weighted similar invariant linear algorithm to obtain the camera parameters as the initial calibration parameters, and then optimize the parameters by using improved nonlinear algorithm. Finally, in the case of introducing random noise, the results of computer simulations and laboratory experiments show that when the noise level reaches 2 pixels, the parameter error of this method is mostly reduced to 0.2% compared with other methods, which verifies the feasibility of our proposed method.

The study of the relationship between water pollution and economic growth holds great significance for sustainable development. Under Environmental Kuznets Curve (EKC) hypothesis, this paper focuses attention on a Chinese context and investigates the relationship between water pollution discharge--waste water (WW), chemical oxygen demand (COD) and ammonia nitrogen (NH4–N) and economic growth--per capita Gross Domestic Production (GDPPC), based on a comparison of the results from two variable coefficient panel data models–-a Locally Weighted Smoothed Regression Estimator and Smoothing Scatterplots Model (LOWESSM) which is a nonparametric model, and a Semi-parametric Seemingly Unrelated Regression Model (SSURM) which considers the contemporaneous correlation of water pollutants that most previous studies have ignored. The empirical results indicate that there exist differences that can be represented by the characteristics of different EKC types, or different turning points under the same EKC type and that the SSURM may be more conducive to reflecting the real relationship between water pollution and economic growth. The study also finds that there are eight types of EKC which can be categorized as “good EKCs” (negative monotonic shape, inverted N-shape, inverted U-shape and M-shape), “bad EKCs” (positive monotonic shape, N-shape and U-shape) and “transition EKC” (positive monotonic and flat-tailed shape) and the proportion of “good EKCs” in economically developed areas (71.43%) is lower than that in the less economically developed areas (76.47%) in terms of COD discharge. Results suggest that addressing the state of water pollution will not occur automatically as GDPPC increases, but requires the regulatory power of government environmental policies.

In this study, a coupled water&ndash, energy balance equation at an arbitrary time scale was proposed as an extension of the Budyko hypothesis. The second mixed partial derivative was selected to represent the magnitude of the interaction. The extended hydrological sensitivity method was used to quantitatively evaluate the impacts of climate change, anthropogenic activities, and their interaction on dry season runoff in the Lhasa River. In addition, an ABCD model, which is a monthly hydrological model included a snowmelt module, was used to calculate the change in soil water and groundwater storage. The Mann&ndash, Kendall test, Spearman&rsquo, s test, dynamic linear model (DLM), and Yamamoto&rsquo, s method were used to identify trends and change points in hydro-climatic variables from 1956&ndash, 2016. The results found that dry season runoff increased non-significantly over the last 61 years. Climate change, which caused an increase in dry season runoff, was the dominant factor, followed by anthropogenic activities and their interaction, which led to varying degrees of decrease. This study concluded that the methods tested here performed well in quantifying the relative impacts of climate change, anthropogenic activities, and their interaction on dry season runoff change.

Ion parameters in electron cyclotron resonance (ECR) microwave plasma were measured by ion sensitive probe and were compared with the electron parameters obtained by double Langmuir probe. The effects of gas pressure and microwave power on the ion temperature and density were analyzed. The spatial distribution of the ion parameters was also investigated by the ion sensitive probes with a tunable radial depth installed on different probe windows along the chamber axis. Results showed that the ion density measured by the ion sensitive probe was in good agreement with the electron density measured by the double Langmuir probe. The influence of gas pressure on the ion parameters was stronger than that of microwave power. With the increase in working pressure, the ion temperature decreased monotonously with a decreasing rate larger than that at higher pressure. The ion density first increased to a peak (42.3×1010 cm-3) at 1 Pa and then decreased. The ion temperature and density increased little with the increase in the microwave power from 400 W to 800 W. The plasma far away from the resonant point is found to be radially uniform.

This paper investigates the decentralized stabilization of large scale feedforward nonlinear systems via saturated delayed feedback. A class of saturated delayed controllers is first proposed for the single-input feedforward nonlinear systems: an appropriate normal form rather than the homogeneous property of feedforward systems is depended on, and a small parameter contained in the normal form is assigned into the control law. By tuning the small parameter, the control design allows an arbitrarily large input delay. This scheme is then extended to the setting of decentralized feedforward nonlinear systems. The effectiveness of the suggested algorithm is verified by simulations.

Because of nonlinearity and strong coupling of reaction-jet and aerodynamics compound control missile, a missile autopilot design method based on adaptive fuzzy sliding mode control (AFSMC) is proposed in this paper. The universal approximation ability of adaptive fuzzy system is used to approximate the nonlinear function in missile dynamics equation during the flight of high angle of attack. And because the sliding mode control is robustness to external disturbance strongly, the sliding mode surface of the error system is constructed to overcome the influence of approximation error and external disturbance so that the actual overload can track the maneuvering command with high precision. Simulation results show that the missile autopilot designed in this paper not only can track large overload command with higher precision than traditional method, but also is robust to model uncertainty and external disturbance strongly.

Crystalline silicon carbon nitrides were synthesized on quartz glass substrates by microwave plasma chemical vapor deposition,using precursors produced from a pulsed nitrogen ion beam sputtering dicyandiamide target. The effects of deposition temperature on the morphology,composition and structure of the samples were investigated by scanning electron microscopy,energy dispersive X-ray,X-ray diffraction and X-ray photoelectron spectroscopy. It is found that the surface morphologies of the deposits change from well crystallized hexagonal crystals at 800 ℃ to semi-developed multi-sheet crystals at 700 ℃ and to an amorphous solid with small grains at 550 ℃. The intensity of the diffraction peaks decreases and the values of the cell parameters a and c increase with the deposition temperature. The as-prepared crystalline films are silicon carbonitride with the crystalline structure of Si3N4 modified by replacing some of the Si atoms with C atoms. The N atoms are mainly bonded to Si,and C atoms are in the form of sp3C—N,sp2CN and sp2CC bonds. Decreasing deposition temperature favors an increase of C atom content and sp3C—N bond fraction.