The shallow neural network (SNN) is a popular algorithm in atmospheric parameters retrieval from microwave remote sensing. However, the deep neural network (DNN) has a stronger nonlinear mapping capability compared to SNN and has great potential for applications in microwave remote sensing. The Microwave Humidity and Temperature Sounder  onboard the Fengyun-3 (FY-3) satellite has the ability to independently retrieve atmospheric temperature and humidity profiles. A study on the application of DNN in retrieving atmospheric temperature and humidity profiles from MWHTS was carried out. Three retrieval schemes of atmospheric parameters in microwave remote sensing based on DNN were performed in the study of bias correction of MWHTS observation and the retrieval of the atmospheric temperature and humidity profiles using MWHTS observations. The experimental results show that, compared with SNN, DNN can obtain better bias-correction results when applied to MWHTS observation, and can obtain higher retrieval accuracy of temperature and humidity profiles in all three retrieval schemes. Meanwhile, DNN shows higher stability than SNN when applied to the retrieval of temperature and humidity profiles. The comparative study of DNN and SNN applied in different atmospheric parameter retrieval schemes shows that DNN has a more superior performance. As the basic parameters of the atmosphere, temperature and humidity profiles play an important role in the research and applications of atmospheric science, such as numerical weather forecast, climate change research, and strong convective weather forecast and analysis ,2,3,4. GThe atmospheric temperature and humidity profile retrieval algorithms based on passive microwave observation have been developed for more than 60 years, and the retrieval algorithms can be summarized into two categories: physical retrieval algorithms and statistical retrieval algorithms ,8. The eAmong the many retrieval schemes of atmospheric temperature and humidity profiles using the passive microwave observations, three neural-network-based schemes have been successfully and widely applied for most of the atmospheric scenes, especially the clear sky scene ,18. The At present, although NNs are widely used to retrieve atmospheric temperature and humidity profiles using passive microwave observations, it is almost the SNNs that are extensively used, and they usually contain a hidden layer with a small number of neurons that are based on the error backward propagation algorithm. Each neuron in the hidden layer performs a nonlinear computation, and the small number of neurons in the hidden layer limits the nonlinear mapping capability of SNN. When there are thick clouds or/and rain in the sight of the microwave radiometer, the nonlinear relationship between the measurements of the microwave radiometer and the atmospheric temperature and humidity profiles is complicated, and SNN may not be able to accurately model the nonlinearity between the input and output samples, which leads to the poor accuracy or even failure of the retrieval of the temperature and humidity profiles based on SNN. However, compared with SNN, the nonlinear mapping capability of DNN has been greatly improved ,26. TherMWHTS is a key payload onboard the new generation of polar-orbiting meteorological satellites Fengyun-3C  and FY-3D. It is a microwave radiometer that integrates humidity and temperature sounding to detect atmospheric humidity and temperature profiles simultaneously. In this paper, a study on the applications of DNN in three retrieval schemes for temperature and humidity profiles using MWHTS measurements is recounted. The retrieval results are analyzed and compared with those of SNN, with the aim of exploring the potential of DNN in retrieving atmospheric parameters by using passive microwave observations. This paper is organized as follows: As a total power microwave radiometer, MWHTS is an important payload onboard FY-3C and FY-3D satellites, which were launched in September 2013 and November 2017, respectively, and both payloads are operating normally in orbit and have accumulated rich atmospheric sounding data. MWHTS performs the cross-track scanning along the orbit with the angle of ±53.35° from the nadir to inspect 98 nominal fields of view (FOVs) in each scan line, which is corresponding to the scanning of the swath of 2645 km in 2.667 s. MWHTS has eight temperature sounding channels with frequencies near the 118.75 GHz oxygen absorption line for sounding temperature from the surface to the upper atmosphere and five humidity sounding channels with frequencies around the 183.31 GHz water vapor absorption line for sounding water vapor and precipitation from the surface to about 300 hPa, and two window channels with frequencies at 89.0 and 150.0 GHz, respectively, for providing information about the surface parameters . Table 1http://satellite.nsmc.org.cn accessed on 15 November 2019)—the quality of MWHTS observations at ECMWF has been evaluated, and the detailed description of the evaluated results can see Lawrence et al. [http://apps.ecmwf.int/datasets accessed on 15 November 2019). Many global atmospheric sounding measurements are assimilated with the 12-h analysis window by using four-dimensional variational analysis to produce ERA-Interim [The datasets used in this study included the following: (1) Level 1b brightness temperatures of MWHTS onboard FY-3D from the National Satellite Meteorological Center (NSMC) -Interim ,30. In tIn the three retrieval schemes used in this study, it was necessary to calculate the simulations of MWHTS, especially for the retrieval scheme based on the physical retrieval algorithm, where the radiative transfer model is an essential part. In this study, the fast radiative transfer model RTTOV , Version 11.3, developed by ECMWF, was used to calculate MWHTS simulated brightness temperatures. RTTOV can calculate simulated brightness temperatures for a wide range of satellite-based microwave radiometers, with an accuracy that can meet the application requirements . The emiAccording to the purpose of this paper, for applying the data used in this study in the three retrieval schemes, the data-preprocessing process was as follows. First, building the collocated dataset. As for the criteria of the collocation between the MWHTS observed brightness temperatures and the atmospheric parameter dataset, the time and absolute distance in latitude and longitude are less than 0.5 h and 0.1°, respectively. Second, calculating the simulated brightness temperatures of MWHTS in the collocated dataset in which the profile parameters and the surface parameters are input to RTTOV to calculate the simulations. Then, after filtering out the scattering-affected data in the collocated dataset, we took the vertical integral cloud liquid water of 0.05 mm as the threshold of precipitation. If the integral cloud liquid water in one group of collocated dataset exceeds 0.05 mm, then that group’s data are considered as scattering-affected data and are removed. Finally, 80% of the collocated dataset after filtering out the scattering-affected data containing the observed brightness temperatures, the simulated brightness temperatures, and the atmospheric parameters is randomly selected as the analysis dataset with 848129 collocated samples, and the remaining 20% are set as the testing dataset with 212033 collocated samples. The overall data preprocessing procedures are summarized in In 2006, deep learning was introduced in the research of Hinton and his students, which initiated the research wave of multilayer neural networks, and many scholars began to study the applications of DNN in various fields . In shorThe purpose of DNN, as a type of neural network, is to build a statistical model between input and output samples through supervised learning in a training dataset, and to make predictions on new input samples from the validation dataset when they are fed into the established statistical model. In this study, the application of DNN to the retrieval of atmospheric temperature and humidity profiles using MWHTS observations involved two main aspects: the building of the DNN-based observation-bias-correction model and the building of the DNN-based retrieval model for temperature and humidity profile.For MWHTS-observation-bias correction, DNN was used to build a statistical model between MWHTS-observed brightness temperatures and MWHTS observation biases for predicting the observation biases, and the observation biases are defined as follows:For the retrieval of atmospheric temperature and humidity profiles, both MWHTS observed brightness temperature and MWHTS simulated brightness temperature can be used to build the retrieval model. The DNN-based retrieval model using the observed brightness temperature is built as follows. The training dataset of DNN is established, i.e., MWHTS observed brightness temperatures in the analysis dataset are set as the input samples, and the atmospheric temperature and humidity profiles in the analysis dataset are set as the output samples. Then, the four-layer network structure  of DNN is built, and DNN is trained by using the training dataset. Thus, the DNN-based retrieval model, using the observations, is built. If DNN is trained with the simulated brightness temperature instead of the observed brightness temperature in the training dataset, the DNN-based retrieval model using the simulations is obtained. Finally, the observed brightness temperatures from the testing dataset are fed into the DNN-based retrieval model using the observation or the corrected brightness temperature from the testing dataset are fed into the DNN-based retrieval model, using the simulations to obtain the retrievals of the temperature and humidity profiles, and the temperature and humidity profiles in the testing dataset are used to verify the retrieval of the temperature and humidity profiles. Further details of the training and testing of the DNN-based retrieval models are also contained in S; and T represents the matrix transpose. By minimizing the cost function, ξ, the optimal solution is obtained as follows:K is the tangent linear function of f at point S, n is the iteration index, and S1 is the initial state variable.The 1DVAR algorithm is generally labeled under the general term of physical retrieval that inputs the initial values of atmospheric parameters to the radiative transfer model and adjusts the initial values through an iterative process with the aim of fitting the simulations from the radiative transfer model to the observations from the satellite. The 1DVAR algorithm mainly includes two parts: one is the radiative transfer model for the simulations of brightness temperature; the other is the minimization of the cost function. Assuming that the errors in the observations are neither biased nor correlated, Gaussian distribution, the optimal estimate of the atmospheric state variable, function :(3)ξ=12SaS, and the initial state variable, In this study, the parameters of the 1DVAR for retrieving atmospheric temperature and humidity profiles using MWHTS observations were specifically set as follows. The averages of the temperature and humidity profiles in the analysis dataset were taken as both the background state variable, ferences ,37,38. TIn this study, three commonly used retrieval schemes were designed for retrieving atmospheric temperature and humidity profiles by using MWHTS brightness temperatures to study the effect of DNN on the retrieval accuracy of temperature and humidity profiles in different retrieval schemes. In order to compare the performance of DNN in retrieving temperature and humidity profiles, studies of SNN in the three retrieval schemes were carried out. The three retrieval schemes for retrieving atmospheric temperature and humidity profiles using MWHTS brightness temperatures were specifically designed as follows.The first retrieval scheme: The 1DVAR retrieval retrieved the atmospheric temperature and humidity profiles based on the MWHTS 1DVAR retrieval system. First, according to the description in The second retrieval scheme: The NN-based retrieval using the observations retrieved the atmospheric temperature and humidity profiles by the NN-based retrieval model using the observations. The DNN-based retrieval model using the observations and the SNN-based retrieval model using the observations were established, respectively, as described in The third retrieval scheme: The NN-based retrieval using the simulations retrieved the atmospheric temperature and humidity profiles by the NN-based retrieval model using the simulation. The DNN-based retrieval model using the simulations and the SNN-based retrieval model using the simulations were established, respectively, as described in In this study, the DNN and the SNN design used in the three retrieval schemes both produced the best results in terms of reproducing the observations bias and the atmospheric temperature and humidity profiles. The input layers of DNN and SNN used in the observation-bias correction have 15 neurons, which receive the observations of 15 channels of MWHTS, and the output layers have 15 neurons, which output the observation biases corresponding to the observations in the input layers. In the retrievals of the temperature and humidity profiles, the input layers of DNN and SNN also have 15 neurons, which receive the simulations or the observations of MWHTS; the output layers have 74 neurons, which output the temperature and humidity profiles.For the hidden layers of DNN and SNN applied in the three retrieval schemes, the configurations of neurons, layers, and activation function were determined by extensive testing . In the extensive testing, the trained DNN and SNN for the observation-bias correction were evaluated by the Root Mean Square Error (RMSE) between the predictions of the observation bias and the observation biases in the testing dataset, and the trained DNN and SNN for the retrieval of the temperature and humidity profiles were evaluated by the RMSE between the retrievals and the temperature and humidity profiles in the testing dataset.In the three retrieval systems, one hidden layer for SNN and two hidden layers for DNN can ensure adequate training of the model in the extensive testing. Then the Rectified Linear Unit (ReLU) was selected as the activation function in this study because it can overcome the problems of saturation and vanishing gradients . MoreoveThis section presents the experimental results of DNN and SNN models used to correct the MWHTS observation bias, as well as the retrieval results of atmospheric temperature and humidity profiles when DNN and SNN models are applied to the three retrieval schemes described in According to the experimental design in As can be seen in As can be seen from This section presents the retrieval results of the temperature and humidity profiles of the three retrieval schemes as designed in According to the experimental design of the first retrieval scheme, the MWHTS observed brightness temperature in the testing dataset, the DNN-based corrected brightness temperature, and the SNN-based corrected brightness temperature obtained in As can be seen from According to the experimental design of the second retrieval scheme, the observed brightness temperature in the testing dataset is input to the DNN-based and SNN-based retrieval models using the observations, respectively. The output results are the retrieval results of temperature and humidity profiles, and the retrieval accuracies are verified as shown in From According to the experimental design of the third retrieval scheme, the DNN-based corrected brightness temperature and the SNN-based corrected brightness temperature obtained in the first retrieval scheme are input to the DNN-based and SNN-based retrieval models using the simulations, respectively, to obtain the retrieval results of the atmospheric temperature and humidity profiles, and the comparison of the retrieval accuracies is shown in From From The stability test of the algorithm is required when the neural network is applied to retrieve the atmospheric parameters using passive microwave measurements. The weights and biases are set randomly at the start of the training of the neural network. The performance of a well-constructed neural network for atmospheric parameter retrieval is not affected by the different weights and biases used at the start of training. Therefore, the stability testing experiments of DNN and SNN in the three retrieval schemes are carried out, simultaneously. Taking the 1DVAR retrieval and the NN-based retrieval using the observations as examples, one must retrain the DNN and SNN models with randomly initialized weights and biased for three separate times, respectively. Then they are applied to the same MWHTS observations as those of the 1DVAR retrieval and the NN-based retrieval using the observations. The differences in the RMSE of bias correction between the three bias corrections and the bias correction in For the bias-correction results of SNN and DNN for MWHTS observation bias, it can be seen from In conclusion, both SNN and DNN can be successfully applied to the 1DVAR retrieval and the NN-based retrieval using the observations, and DNN shows better performance than SNN in both MWHTS-observation-bias correction and the retrieval of temperature and humidity profiles using MWHTS observations. However, in the NN-based retrieval using the observations, the application of SNN fails, while DNN achieves a successful retrieval based on MWHTS simulations due to its stronger generalization ability. For the three retrieval schemes, although the 1DVAR retrieval and the NN-based retrieval using the observations obtain comparable retrieval accuracies of atmospheric temperature and humidity profiles, they have their advantages and disadvantages. Although the 1DVAR algorithm does not require a large amount of historical data for learning the sample features, the parameters of the physical retrieval system need to be set before retrieving to ensure a high retrieval accuracy, which is computationally intensive and expensive. In the NN-based retrieval using the observations, although the retrieval operation is simple, it is necessary to establish representative sample data for the training of NNs to ensure high retrieval accuracy when applied to new observed brightness temperature. For the NN-based retrieval using the simulations, although the retrieval accuracy is slightly poorer compared with the former two retrieval schemes, it has the advantage that the data-matching errors between satellite observations and atmospheric parameters can be disregarded because the atmospheric parameters can be directly used to generate simulated brightness temperatures, which can satisfy the demand of the neural network for a large number of data samples, and it is easy to establish a representative sample dataset.In this paper, the application of DNN in the retrieval of atmospheric temperature and humidity profiles from FY-3D/MWHTS was investigated, mainly involving the application of DNN in MWHTS-observation-bias correction and in retrieving atmospheric temperature and humidity profiles using MWHTS observed brightness temperature or MWHTS simulated brightness temperature. In order to verify the performance of DNN applied in retrieving, a study on the application of SNN in the same situation was also carried out. It is found that, in the three retrieval schemes of atmospheric temperature and humidity profiles that use MWHTS measurements, DNN shows superior performance and better stability than SNN in both MWHTS-observation-bias correction and retrieval of atmospheric temperature and humidity profiles. Therefore, the results indicate that DNN and other deep learning algorithms have great potential in the applications of microwave remote sensing.It is noted that the retrieval experiments of temperature and humidity profiles in this study were conducted under all-weather conditions and do not classify the MWHTS observations for different atmospheric states, such as clear sky, cloudy, and rainy. In practical applications, higher retrieval accuracy can be obtained if the brightness temperature classification according to different atmospheric states is realized based on the brightness temperature characteristics, and then the atmospheric temperature and humidity profile is retrieved under different atmospheric states. However, it is difficult to classify satellite observed brightness temperature under different atmospheric states based on the characteristics of satellite observations at present. The study of data classification of satellite microwave observation under different atmospheric states by deep learning algorithms to further improve the retrieval accuracy of atmospheric temperature and humidity profiles using satellite passive microwave observations is the focus of future work.


The rGO is flat and smooth with a sheet-like morphology while the N-rGO exhibits folded morphology. This type of folding of the surface morphology can increase the gas sensitivity. The N-rGO and the rGO sensors showed n-type and p-type semiconducting behaviors in ambient conditions, respectively, and were responsive to low concentrations of NO gases (< 1000 ppb) at room temperature. The gas-sensing results showed that the N-rGO sensors could detect NO gas at concentrations as low as 400 ppb. The sensitivity of the N-rGO sensor to 1000 ppb NO (1.7) is much better than that of the rGO sensor (0.012). Compared with pure rGO, N-rGO exhibited a higher sensitivity and excellent reproducibility.In this study, we use nitrogen-doped to improving the gas-sensing properties of reduced graphene oxide. Graphene oxide was prepared according to a modified Hummers’ method and then nitrogen-doped reduced graphene oxide (N-rGO) was synthesized by a hydrothermal method using graphene oxide and NH The development of gas sensors is a highly critical research area that involves issues related to safety, health, and environmental risks4. Typical gas sensors use metal oxide materials, such as zinc oxide and tin oxide6. Metal oxide-based gas sensors operate at high temperatures (200–400 °C), which results in excessive consumption of energy and limits their long-term stability. Thus, it is important to develop new gas sensor concepts that can overcome these drawbacks.Gas sensors are very important for a wide range of applications, including detection of harmful chemical vapors, industrial process control, and chemical processing plants9. However, these sensors show low sensitivity and long response time, or poor reproducibility, depending on the assembly process and the purity. Graphene-based materials are useful for the fabrication of high-performance sensors, especially for detecting gases at low operating temperatures. Graphene exhibits two excellent properties for gas detection: (1) High carrier mobility  and high surface area (2630 m2/g), facilitating detection of resistance change during the process10 and (2) Low electrical resistivity (1.0 × 10–6 Ω cm) for detecting resistance change after adsorption or desorption and increase or decrease in target gas concentration11. However, pure graphene is intrinsically inert, while graphene oxide (GO) has multitudinous oxygen functional groups and is too insulating for fabrication of resistance-based sensors. Recently, several graphene-based composites have been reported for gas sensors. For example, Ganhua et al.12 prepared graphene directly from GO through one-step heating , which was responsive to low-concentration NO2 and NH3 gases in air at room temperature with excellent sensitivity. Jianwei et al.13 synthesized graphene/Pt with sensitivities of 14% (7%), 8% (5%), and 10% (8%), for 1000 ppm H2, NH3, and NO gases, respectively with (without) Pt nanoparticles at room temperature. Vardan et al.14 prepared a hybrid structure of reduced graphene oxide (rGO) and ZnO nanostructures exhibiting 40–50% better response to NO2 and H2 compared to pure ZnO sensors at an operating temperature of 200 °C. Hao et al.15 synthesized rGO/SnO2/Au hybrid nanomaterials through a one-step hydrothermal process, exhibiting a much better response/recovery time to 5 ppm NO2 compared with pure rGO and rGO/SnO2 at an optimal operating temperature of 50 °C. Sen et al.16 synthesized rGO/ZnO/Au hybrids through the wet chemical method with high sensitivity and fast response for detection of NO2 at an operating temperature of 80 °C. The improved sensor performance reported in the above studies is attributed to the incorporation of graphene/RGO-based nanocrystals/nanoparticles. However, their synthesis methods are complicated, requiring a high operating temperature and incorporation of precious metal or metal-oxide, and the sensor has a low response to low concentrations of gases. Therefore, reducing operating temperature and cost, and developing an environment-friendly strategy is very important to extend the applications of graphene-based gas sensors.In recent years, carbon-based nanomaterials, such as carbon nanotubes and graphene, have been used for gas detection at room temperature. Various gases have been tested using carbon nanotubes gas sensorsWe prepared nitrogen doped rGO gas sensors with fast response and high sensitivity. Nitrogen-doped rGO (N-rGO) nanosheets were prepared directly from GO through the hydrothermal treatment in an ammonia solution. In the gas sensing process, N-rGO and rGO exhibited n-type and p-type semiconductor behaviors, respectively. We assessed the sensing characteristics of N-rGO at various concentrations of NO gas at room temperature.All chemicals were of analytic grade and used without further purification. Graphite powder  was used to prepare GO. Sodium hydroxide  and an ammonia solution  were used to prepare rGO and N-rGO, respectively. The NO calibration gas cylinders (1000 ppb) used for the sensing studies were obtained from Air Products San Fu Co. Ltd., Taiwan.17 is used to prepared graphene oxide powders. The mixture of 2 g of graphite powder and 0.6 g of NaNO3 is mixed in 20 mL of concentrated H2SO4 solution under 30-min stirring in an ice-bath surrounding. Then, 10 g of KMnO4 was added slowly in the mixture solution which was stirred at 35 °C for 3 h. Follow, the mixture solution with 180 mL DI water was heated at 98 °C for 2 h. In order to eliminate the excess of KMnO4, 20 mL of 30% H2O2 was dropped slowly to the solution. Finally, the mixture solution was centrifuged and washed with 5% HCl and DI water to remove the metal ions and control the pH value to 7. The resulting solid was dried in a vacuum oven under a pressure of 101.32 Pa at 70 °C for 12 h.The modified Hummers’ methodHydrothermal method is used to synthesize N-doped graphene from the raw material of graphene oxide. Dispersion of GO (0.05 g) in 50 mL of DI water is obtained by ultra-sonication for 2 h. Then, 20 mL of ammonia solution (25 wt.% in water) was added in dispersed solution. The Teflon-lined autoclave which contain the mixture solution is placed in drying oven at 200 °C for 24 h. The reacted solution from the autoclave which is cooled naturally to room temperature are centrifuged and washed several times with distilled water and ethanol after. The remaining mixtures are drying in a vacuum oven at 80 °C for 24 h. For fabrication of rGO powders, the NaOH solution is conducted in the same process instead of ammonia solution.The optical properties were measured using a Shimadzu UV 3600 spectrophotometer at wavelengths in the range of 200–800 nm. The morphology and the dispersion level of rGO and N-rGO were analyzed by transmission electron microscopy , field-emission scanning electron microscopy , and scanning electron microscopy . Raman spectra were recorded on an Andor DU401-BV multichannel confocal microspectrometer with 632-nm laser excitation. X-ray photoelectron spectroscopy  was performed using monochromated Al Kα radiation (1486.8 eV). All these measurements were carried out at room temperature.2O3 substrate. Figure The sensor was fabricated using a three-layer configuration: sensing materials, Ag–Pd electrodes, and Al19.Figure 2 (C=C), sp3 (C–H), C–O , and C=O (carboxylic) bonds21. The intensity of the sp2 peak increases and that of the oxygen-related peaks decreases, further confirming the reduction of GO. Figure 2 (C=C), Csp2-N, and Csp3-N, originating from the doping of N22. As a result of the relatively low electronegativity of N (than oxygen), this peak appears at a lower binding energy relative to the C–O and C=O peaks. The N 1s spectra in Fig. 24. The presence of these peaks show that the doping of N atoms into an sp2-bonded network of carbon results in three different N functionalities  gra2 C=C bonds and n-π* transition of the C=O bonds, respectively27. When GO is reduced to graphene, a typical absorption peak is observed at 234 nm, which can be ascribed to the excitation of the π-plasmon of the graphitic structure28. The N doping introduces a new UV absorption peak at 268 nm, corresponding to the electron transitions from C=N29. Meanwhile, when GO is reduced to rGO and N-rGO, the shoulder peak at approximately 305 nm disappears as a result of the decrease in the concentration of carboxyl groups30.Figure −1 and the G-band (in-plane vibrations of sp2 carbon network) is observed at approximately 1613 cm−131. The intensity ratio of the D-band to the G-band (ID/IG) is used to determine defect lattices and degree of disorder in graphene32. The ID/IG ratio increased from 1.15 for GO to 1.24 for rGO. This is a clear indication of the effective reduction of GO. A higher ratio (ID/IG = 1.28) for N-rGO indicates the presence of more defects caused by N doping.Figure 3-hybridized carbon atoms33. After the NaOH treatment, the resistance of rGO slightly decreases, further confirming the reduction of GO. The resistance of N-rGO significantly decreases, indicating improved sensitivity.Figure 0) in 1000-ppb NO at room temperature. Initially, the sensor was exposed to clean air to determine the base resistance; then, NO gas was injected into the sensing chamber for 2.5 min to register the sensing signal; finally, the NO gas supply was closed and air flow was simultaneously allowed for 2.5 min to repeat the cycling of the sensor. In these experiments, there was no evidence of any deterioration in the sensitivity. Figure 34. This is because N-rGO is very sensitive to this gas. As the N-rGO nanostructures are exposed to air ambient, the electrons introduced by the oxygen vacancies in the N-rGO nanostructures are adsorbed by the oxygen molecules from the air ambient which compensate the surface oxygen vacancy sites. These surface adsorbed oxygen molecules are transformed into oxygen ions such as O2−, O2− and O− by capturing the electrons from the conduction band of N-rGO. Hence, the electron depletion layer is extended on the surface of the N-rGO nanostructures that increases the electrical resistance of the nanostructures35.Figure −, O2− ions by capturing electrons from the conduction band which results in increase in resistance (Eq. 1). When the N-rGO is exposed to the atmosphere of NO, it captures the electrons due to its higher electrophonic property leading to the formation of adsorbed NO− (ads) which results in increase in resistance (Eq. 2). However, desorption of NO− can take place and the adsorbed NO− (ads) reacts with adsorbed oxygen  which results in further increase in resistance (Eq. 3). Then, when the air was let in, NO can readily dissociate into O− and some bound electrons were released36. Figure 37 and chemically38 reduced GO when exposed to the ambient environment. The absorbed oxygen on the rGO surface attracts electrons from graphene, generating additional holes as carriers. This process increases the hole concentration and contributes to the p-type semiconducting behavior. When the rGO (p-type) sample is exposed to the NO gas, a chemical reaction takes place. This chemical reaction will then cause a change in the material properties, for example, electrical conductivity or resistance. The NO gas is adsorbed on the surface of the N-rGO material according to Eqs. , 2), an, an2), a2) gas excess NO reacts with oxygen to form NO239. Therefore, used the N2 represent the state the ambient. Figure 2. This means that the surface topography is related to the response time. We calculated the sensitivity (S), which is one of the most important characteristics of a gas sensor41. Figure Various concentrations of NO gas, ranging from 400 to 1000 ppb, were fed into the chamber to determine the sensing performance of the optimal sensor. In ambient environments, the oxygen , ethanol (EtOH), methanol (MeOH), as summarized in Fig. Selective detection of target gases remains a challenge in the applications of gas sensors. To identify their selectivity by exposing them to saturation organic vapors like NHThe long-term cyclic stability of the rGO and N-rGO sensor was measured in Fig. 2 hybridized carbon atoms and sp3 hybridized carbon atoms. These constitute the graphite structure and the structural defects, and form chemical bonds with oxygen-containing groups, respectively. The absorption energy of sp3 is larger , resulting in slower adsorption and desorption43. Figure 2 carbon network, in which N has a higher electronegativity (x = 3.04) than C (x = 2.55), thus further affecting the physical and the chemical performance; (iii) The doped N atoms exist in three different states, namely pyridinic-N, graphitic-N, and pyrrolic-N. The enhanced catalytic activity is usually attributed to the increased active sites of pyridinic-N and/or pyrrolic-N. Pyridinic N refers to N atoms at the edges of graphene planes, where each N atom is bonded to two carbon atoms and donates one p-electron to the aromatic π system45. Pyrrolic N atoms are incorporated into five-membered heterocyclic rings, which are bonded to two carbon atoms and contribute two p-electrons to the π system48. Pyridinic-N modifies the band structure of carbon, raising the density of π states near the Fermi level and lowering the work function.the relative electronegativity of graphitic N atoms reduces the electron density on the adjacent C nuclei, which helps electrons transfer from the adjacent C to N atoms, and N backdonates electrons to adjacent C pz orbitals. The donation and backdonation processes not only facilitate O2 dissociation on the adjacent C atoms, but also help forming a strong chemical bond between O and C49.The gas sensing mechanism of N-rGO can be explained using a surface-controlled model, which includes absorption, electronic desorption, and transfer. The unsatisfactory performance of the rGO sensor results from its constituent carbon atoms. Graphene has two types of carbon atoms, namely spReduced graphene oxide and N-rGO nanosheets were evaluated for their NO gas sensing properties at room temperature. The hydrothermal treatment converted rGO to n-type N-rGO, as confirmed by UV and XPS analyses. The gas-sensing results showed that the N-rGO sensors could detect NO gas at concentrations as low as 400 ppb. The sensitivity of the N-rGO sensor to 1000 ppb NO (1.7) is much better than that of the rGO sensor (0.012). The results of the present study indicate that N-doping can significantly enhance the NO sensing properties of graphene-based sensing materials at room temperature, suggesting their excellent potential for use as gas sensors.


Smoothened from the mesoderm impaired the development of the venous pole of the heart and resulted in hypoplasia of the atrium/inflow tract (IFT) and a low heart rate. The blockage of Smoothened led to reduced expression of genes critical for sinoatrial node (SAN) development in the IFT. In a cardiac cell culture model, we identified a Gli2–Tbx5–Hcn4 pathway that controls SAN development. In the mutant embryos, the endocardial-to-mesenchymal transition (EndMT) in the atrioventricular cushion failed, and Bmp signalling was downregulated. The addition of Bmp2 rescued the EndMT in mutant explant cultures. Furthermore, we analysed +Gli2 scRNAseq and −/−Tbx5 RNAseq data and explored the potential genes downstream of hedgehog signalling in posterior second heart field derivatives. In conclusion, our study reveals that Smoothened-mediated hedgehog signalling controls posterior cardiac progenitor commitment, which suggests that the mutation of Smoothened might be involved in the aetiology of congenital heart diseases related to the cardiac conduction system and heart valves.Smoothened is a key receptor of the hedgehog pathway, but the roles of Smoothened in cardiac development remain incompletely understood. In this study, we found that the conditional knockout of  Cardiac progenitor formation and differentiation are essential for heart development. During early gastrulation, a subset of mesodermal cells leaves the primitive streak and is destined to a cardiac fate . Later d−/−Smo mutant embryos fail to turn and are arrested at approximately E9.0 with a linear heart tube . M. MGli2 ilaxation . A scRNAt hearts g. qRT-PCt hearts h. Type 2illation ,42. RyR2 mutants h.Gli2+ cardiac progenitors.Thus, we identified the Gli2–Tbx5–Hcn4 and Gli2–Bmp2 axes, which control SAN development and AV cushion formation, respectively, and we also analysed and validated the genes related to the cardiac contraction in . 4+ cells within the AV canal myocardium.We have demonstrated that Hh signalling is required for the developmental potential of the cardiac progenitors and their differentiation towards pacemaker cells within the SAN and Bmp2. 4.1Smo in the mesoderm reduces the de novo expression of Tbx5 in the pSHF. Wnt2, a downstream target of Tbx5 [Smo mKO mutants. However, the loss of Smo did not affect the expression of Hcn4 and Isl1 at the approximately E7.5–E8.0 stage. These results demonstrated that Hh signalling is required for the developmental potentials but not the formation of the cardiac progenitors in the pSHF. It is reported that Tbx5 acts upstream and parallel to Hh signalling in the SHF [Tbx5+/− mutants. Our data along with that obtained in the previous study suggests that the regulation of the Hh pathway by Tbx5 might constitute a feedback pathway.In this study, we found that the inactivation of  of Tbx5 , was als the SHF . The pre the SHF  showed t. 4.2Hcn4, Tbx3, Shox2, Isl1 and Tbx18 are enriched in the SAN [A differential expression analysis of RNAseq data revealed that  the SAN ,44. The  the SAN ,45. The  the SAN .Hcn4, Tbx3, Shox2 and Isl1. Furthermore, functional assay revealed that the heart rates are significantly decreased in Smo mutant heart. By contrast, the expression of Tbx18 was not affected in the IFT of Smo mKO mutant heart. These results suggest that Hh signalling controls pacemaker progenitor cell commitment.Tbx5 and Hcn4 are required for the specification and maturation of pacemaker progenitor cells, respectively . Tbx3, SSmo mKO mutant hearts at E8.25–E8.5 . By E9.5, a marked reduction of proliferation was found in the mutant hearts  . It has been reported that Tbx5 controls the expression of cell-cycle progression genes [In addition, we measured cell proliferation at different embryonic stages. The proliferation of cardiac progenitor cells displayed a decreasing trend in the . 4.3Bmp2 expression, which is required for activation of the Bmp pathway and initiation of the EndMT. Moreover, the administration of Bmp2 to the Smo mKO mutant AV cushion rescued the transition defect, which suggested that Bmp2 is both necessary and sufficient for induction of the EndMT by Hh signalling.Multiple signalling pathways are involved in the EndMT of the endocardium, and these pathways include Bmp/Tgfβ, Notch, Vegf and calcineurin/NFAT . In this−/−Shh hearts [Shh and Ihh compound mutants arrest shortly after gastrulation and phenocopy Smo mutants. Shh and Ihh compound mutants or Smo mutants exhibit a more severe phenotype than −/−Shh mutants [Shh and Ihh are both required for Smo-mediated EndMT during heart development.It has been reported that the specification of the AV cushion and the initiation of the EndMT proceed normally in − hearts . Shh is − hearts . Shh and mutants . We spec. 4.4Tbx5 in the pSHF was reduced in the Smo mKO mutants, its expression in the anterior heart field was less affected. These results suggest that the regulation of Tbx5 by Hh signalling is context-dependent. We noted that the expression levels of Hcn4 in the heart field were comparable between the control and Smo mKO mutant embryos at E8.0. As the embryo develops, the expression of Hcn4 was decreased in the IFT and common atrium of the mutant hearts, which suggested Tbx5 is required for the maintenance of Hcn4 expression.We identified the Gli2Tbx5–Hcn4 axis which is essential for pacemaker progenitor cell differentiation and cardiac conduction. Although the expression of Bmp2 expression. The results indicate that Gli2 is required in cardiac progenitors for Bmp2+ cell lineage determination.The cardiac progenitor cells in the pSHF contribute to the AV canal myocardium, atrium and IFT. The specific expression of Bmp2 in the AV canal myocardium induces the EndMT by lateral induction. We demonstrated that the overexpression of Gli2 enhanced Gli1-lacZ, Gli2-lacZ and Gli3 mRNA are dominantly expressed in the lateral plate mesoderm (pSHF) and contribute to atrium/IFT development [−/−Gli2;−/−Gli3 double knockout embryos, the expression of Tbx5 is reduced in the lateral plate mesoderm [Smo mKO mutants. These results indicate that Gli2 and Gli3 redundantly regulate the expression of Tbx5 (d). In future study it would be interesting to explore whether Bmp2 expression is downregulated in the AV canal myocardium of the Gli2 and Gli3 compound mutant embryos. Gli1 and Gli2 exhibit similar expression patterns during cardiogenesis. zfd/zfdGli1 and lz/lzGli1 are viable with no obvious defects [Smo mKO mutants (a–d), and the overexpression of Gli2 in P19CL6 cells increased the expression of Gli1 (c), which is consistent with the in vivo results [Tbx5 and Bmp2 in the cell model (c). Thus, the in vivo and in vitro results indicate that Gli1 might not be essential for the formation of the SAN and AV cushion. In this study, we also found that Hh signalling controls other core transcriptional factors required for SAN node development. Single-cell ChIP-seq would be a powerful tool for dissecting the regulatory mechanism in the future.At E8.5 and E9.5. elopment . In Gli2mesoderm , which i of Tbx5 d. In fut defects ,50. Both mutants a–d, and  of Gli1 c, which  results . Unlike ll model c. Thus, Isl1, Tbx5, Hcn4, Mef2c, Fgf8, Wnt2, Osr1 and Hoxb1 were expressed in +Gli2 cells, which suggested that Hh responding cells contribute to both the aSHF and pSHF. Moreover, we predicted the potential downstream genes in the pSHF regulated by Gli2. Among these target candidates, Hcn4 was validated by a gain-of-function assay. The qRT-PCR results demonstrated that the expression of Mybpc3, a potential downstream target gene, was reduced in the IFT of the Smo mkO heart. +Gli2, +Tbx5, +Shox2 triple-positive cells were identified in E8.5 cardiomyocytes. We found that the expression of Shox2 was reduced in −/−Tbx5 and Smo mKO pSHF cardiomyocytes. The data suggest that Gli2 might regulate the expression of Shox2 via Tbx5.We analysed scRNAseq data from the E8.5 mouse embryonic heart . Isl1, TIn summary, our data demonstrate that Hh signalling in the pSHF controls the activity of Gli2 to regulate the development of the SAN and the formation of the AV cushion.


To analyze community-level vulnerability, we used the Canadian Index of Multiple Deprivation (CIMD), which has four dimensions of social vulnerability. We found that three of the four CIMD dimensions were significantly different between surrendering and adopting communities , Economic Dependency, but not Residential Instability). For further investigation, we also grouped our analysis by intake groups  and found multiple relationships for which the incoming and outgoing CIMD quintiles were different. For example, for both puppies and kittens, the median outgoing SV quintile ranks were statistically significantly lower (less vulnerable) than incoming quintile ranks, with the effect size being moderate , supporting the concern of the flow of certain animals from more vulnerable to less vulnerable communities. The results of this research provide a basis for understanding potential inequities in the use of shelter services to surrender or adopt an animal. Furthermore, these methods allow animal shelters to assess community needs and create interventions to reduce intake and increase adoption of animals. Finally, these data provide further support that animal sheltering is best considered from a One Welfare perspective.There is increasing awareness among animal shelter professionals regarding the role of shelters in perpetuating inequities in pet ownership, although the relationship between owner vulnerabilities and animal shelter services is largely understudied. Currently, there is no literature comparing the sociodemographic conditions of communities where surrendered animals originate and communities where they are adopted. The present study compared the “flow” of surrendered animals between originating communities (incoming) and communities where they were adopted (outgoing;  Animal sheltering organizations are aware of the relationship between human vulnerability and the use of animal shelter services, and express interest in providing more initiatives that target community-level issues , 2. ManyUnfortunately, access to continued pet companionship may not be equal in all groups of people. Pet ownership is more likely in certain demographics such as high-income earners, home owners, and rural residents –10. ManyCurrently, there is little research investigating the connections between social vulnerability and animal outcomes. In addition to larger societal inequities, one concern within the sheltering field is that animal shelter procedures themselves may be contributing to further inequities , 19. PotDespite substantial industry interest in providing more equitable services in animal shelters, research on these topics is lacking. To the best of the authors' knowledge, no study has yet compared vulnerability of communities that use intake services  compared to those that use outgoing services  by connecting the movement of individual animals from intake to adoption. In our previous work, we assessed only the vulnerability of communities surrendering animals. The present study continues this work, with an added layer of assessing community-level vulnerability at adoption. Understanding the “flow” of animals to and from communities of differing vulnerability levels can help animal shelters better understand potential imbalances in the use of these shelter services. Thus, the objective of this study was to understand whether surrendered animals are adopted to communities with the same or different vulnerability levels in British Columbia, Canada.This study protocol was reviewed and approved by the University of British Columbia's Research Ethics Board (H20-02704). Permission for data usage was granted by the BC SPCA. The complete dataset can be found in the This study utilized the CIMD, which is a publicly available measure of social well-being that uses Canadian census data to describe specific dimensions of vulnerability in a small dissemination area . AlthouOutgoing animal data were collected from 36 animal shelter locations of the BC SPCA for all animals adopted between January 1, 2016 and December 31, 2019. The BC SPCA is a non-profit organization that operates facilities and programs to improve the lives of animals in B.C. including enforcing provincial animal protection legislation, engaging in advocacy and humane education programs, providing support in the form of pet food banks to the community, and operating 36 animal shelters, 5 animal hospitals/clinics, and a wildlife rehabilitation center . In 2020The data collected for this project are similar to those utilized in our previous work , althougn = 1,705). Observations were also excluded if either the incoming or outgoing address was non-codable in a geographic information system software Quantum Geographic Information System . The cleaned, geocoded dataset included 21,270 observations .The data were cleaned and analyzed using RStudio version 1.4.1106 . The rawr = 0.38, p < 0.001) and RI and SV .Although the CIMD used factor analysis to create four independent factors, we verified the independence of the CIMD factors in our dataset with the incoming CIMD scores by using Spearman rank correlations on each possible pair. We found that there were only two relationships that had a weakly positive correlation: RI and EC . The distribution of animals incoming and outgoing to each EC quintile is shown in Across all intake groups of animals, the median outgoing EC ranks were higher (more vulnerable) than incoming quintile ranks, with the effect size being small . The distribution of animals incoming and outgoing to each SV quintile is shown in The median outgoing SV ranks were higher than incoming quintile ranks across all intake groups of animals, with the effect size being small . For both puppies and kittens, the majority of the incoming animals were surrendered from situationally vulnerable communities with SV scores in the 4th or 5th quintile , which is shown in The results for the Wilcoxon test performed separately by species can be found in For further exploration, we calculated the change in CIMD quintile by subtracting the outgoing CIMD quintile from the incoming CIMD quintile. The distribution of change is displayed for puppies and kittens in p < 2.20e-16, r = 0.11).The results showing the distribution of ED quintiles is shown in r = 0.12, kitten r = 0.19; p < 2.20e-16). Both intake groups had the majority of incoming animals in ED quintiles of 4 or 5 . Full plots showing the distribution of ED quintiles for puppies and kittens can be found in the When the analysis was done separately by intake group, only puppies and kittens showed a statistically significant difference with small effect size . For small animals, the median outgoing RI quintile ranks were lower than that of the incoming ranks. The majority of incoming small animals were surrendered from communities in the 4th and 5th RI quintile , whereas the small animals were adopted out to communities in a relatively balanced manner . The plot showing the distribution of small animals can be found in the The results of the Wilcoxon test for RI by intake groups are shown in In our previous work, we found that the risk of owner surrender for various reasons, of various species/breeds, and of various health statuses was predicted by the different CIMD factors, showing that community vulnerability affects animal surrender . In our A larger proportion of animals both originated from and were adopted to communities of low EC, indicating less presence of racialized and immigrant populations in both surrendering and adoptive communities overall. However, there was still a large difference between incoming and outgoing EC quintiles; a further disproportionate number of incoming animals were surrendered from areas with low EC, even lower than areas into which animals were subsequently adopted. In Canada, the phrase “visible minority” refers to those who are non-White . The phrAs we previously found, puppies and kittens were less likely to be surrendered from areas with high EC; this result is expected as the metropolitan areas of British Columbia are largely comprised of communities with high EC . Indeed,Across all animal intake types, the EC quintiles of adopter communities were imbalanced, with a large number of animals being adopted to communities with low EC. One explanation may be that the imbalance stems from differences in the overall likelihood of companion animal ownership between rural and urban communities. In Ontario, Canada, those who live in rural areas or on properties that were greater than one acre were more likely to own pets . Owning The procedures of the sheltering system itself may pose barriers to adoption. The criteria that are outlined in restrictive pet adoption processes  are likely to disproportionately affect racialized populations , althougQuestions that remain on less restrictive adoption applications, although not directly discriminatory, may still be subject to differences between cultures. For example, adoption applications may ask questions regarding caretaking behaviors, such as where an animal will spend time and sleep . ContempPrevious research and industry statements have noted that racialized populations are underrepresented in animal welfare professions –63. ThisOur study found that puppies and kittens were disproportionately surrendered from communities with high situational vulnerability . In our previous study, companion animals from areas of high SV were at an increased risk of being surrendered due to the owner having “too many” animals, surrendering animals that were intact upon intake, and surrendering puppies and kittens . Indeed,The difference between incoming and outgoing ED quintiles was largely driven by puppies and kittens, where most animals were surrendered from high ED communities. The ED factor also indicates that the community has a high proportion of unemployed individuals, which includes those who are collecting a pension, those who are too young to participate in the workforce, and those who are receiving income assistance. As previously mentioned, cost is a significant barrier to accessing veterinary care , 71. AltAlthough the ED dimension indicates non-employment or income from non-employment sources, it also describes the presence of two specific populations—seniors (>65) and children (<15). There are possible challenges that arise from pet ownership for seniors. In senior care homes, pet ownership may be discouraged due to risk of zoonotic disease and extra workload . Both doIn our study, ED quintiles of adopted animals were relatively equal in distribution, which may indicate that the conversation-based adoption procedures do not discriminate based on family composition or employment status. Questions related to family composition are anecdotally important to animal shelter staff when screening potential adopters. Some animal shelters require a minimum age requirement for children in a home, typically around 4 or 5 years old . Howeverowner-related reason for surrender of adult cats was lack of pet-inclusive housing , analyses were limited because many incoming community animals only have general finder locations  rather than exact addresses, and therefore could not be geocoded. However, inclusion of other incoming animals may have impacted results. For example, in India, a study found that communities with low socioeconomic status had a higher mean number of free-ranging dogs (57.4) per neighborhood compared to communities with middle (39.8) and high (17.0) socioeconomic statuses . SpencerThe data used in this study were only for animals that were both surrendered from and adopted to communities within the province. Transferring animals between shelters (or states in the United States) is a common method to improve animal outcomes, as ownership of pets and adopter preferences vary by region , 110. TrOur study may suggest support for the practice of open adoptions as we found only limited evidence of inequity at adoption, albeit experimental data are needed. In 2014, the BC SPCA implemented open adoption practices that may have contributed to the balanced distribution of adopting communities for three of the CIMD factors. The CIMD factor which did show unequal distribution (EC) may be the least impacted by open adoption practices, which are more focused on removing barriers related to factors such as housing, income, and prior pet experiences . Open adIn many areas, shelters emphasize adoption of animals in ways typically believed to reduce risk of re-relinquishment , which oOur data showed that, from 2016 to 2019, there were multiple differences in the vulnerabilities between owners' surrendering communities and adopters' communities. However, the imbalance in CIMD quintiles was mainly due to disproportionate surrender of animals from more vulnerable communities, with a notable exception for Ethnocultural Composition. The results add to previous work on social vulnerability and animal shelter services by including comparisons to outgoing communities of animals in order to identify possible barriers or discrimination. Although barriers to adoption were another potential source of inequity, our study locations did not show evidence of unevenly distributed adoption of animals based on most sociodemographic factors. Our findings may be due to open adoption policies enacted specifically to reduce adoption barriers prior to the study period, although this was not experimentally addressed. There was uneven adoption of animals based on Ethnocultural Composition, with a higher proportion of animals being adopted out to low vulnerability communities, which could imply direct or indirect discrimination based on race/ethnicity or culture. However, further research is needed to understand whether the uneven adoption distribution is driven by lack of access to animal shelters, an unwelcoming environment of the shelter, or other factors. As this work is also location-specific, animal shelters and rescues should investigate these differences in their own communities. Identifying CIMD dimensions which are different between incoming and outgoing communities does not necessarily imply a causal association, as the nature of systemic issues of vulnerability is complex. However, the results of this study can be used to help animal shelters reflect on practices related to owner surrender and adoption. Furthermore, the results can help inform interventions to reduce shelter intake and maintain the human-animal bond.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by University of British Columbia Behavioural Research Ethics Board. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.LL, EG, and AP contributed to the conception of the study and the subsequent study design. LL acquired the database, organized and visualized the data, and wrote the first draft of the manuscript. LL and AP performed the statistical analysis. All authors contributed to the manuscript, further revisions, and approved the final version of the manuscript for submission.This research was funded by the Social Sciences and Humanities Research Council (SSHRC) through the Canada Graduate Scholarships—Master's program.Data were obtained from the BC SPCA, who is the employer of one of the authors EG. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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