Research on the Spatiotemporal Characteristics and Concentration Prediction Model of PM2.5 during Winter in Jiangbei New District, Nanjing, China

Accurate prediction of PM2.5 concentration is one of the key tasks of air pollution assessment, early warning, and treatment. In this paper, four monitoring sites were arranged in Jiangbei New District of Nanjing City, China. The environmental parameters such as PM2.5/PM10 concentration, temperature, and humidity were monitored from January to February 2020. A gated recurrent unit (GRU) network based on the PM2.5 concentration prediction model was established to predict PM2.5 concentration. The mean relative error (MRE), root mean square error (RMSE), and Pearson correlation coefficient were selected as the evaluation criteria for the accuracy of the GRU model. The data set was divided into a training set, a test set and a validation set at a ratio of 7:2:1, and the GRU model was used to predict the hourly value of PM2.5 concentration in the next week. The prediction results show that the Pearson correlation coefficients between the predicted values and the monitored values of the four monitoring sites have reached more than 0.9, reflecting a strong correlation. The relative average errors are around 10%. The GRU model prediction of NJAU (Nanjing Agricultural University)-Pukou Campus Site is the most accurate, and the correlation coefficient, MRE, and RMSE are 0.970, 7.85%, and 9.6049, respectively, reflecting the good prediction performance of the model. Therefore, this research supports the prediction of air quality in different cities and regions, so people can take protective measures in advance and reduce the damage caused by air pollution to human bodies

Research on the Spatiotemporal Characteristics and Concentration Prediction Model of PM_2.5 during Winter in Jiangbei New District, Nanjing, China

Accurate prediction of PM2.5 concentration is one of the key tasks of air pollution assessment, early warning, and treatment. In this paper, four monitoring sites were arranged in Jiangbei New District of Nanjing City, China. The environmental parameters such as PM2.5/PM10 concentration, temperature, and humidity were monitored from January to February 2020. A gated recurrent unit (GRU) network based on the PM2.5 concentration prediction model was established to predict PM2.5 concentration. The mean relative error (MRE), root mean square error (RMSE), and Pearson correlation coefficient were selected as the evaluation criteria for the accuracy of the GRU model. The data set was divided into a training set, a test set and a validation set at a ratio of 7:2:1, and the GRU model was used to predict the hourly value of PM2.5 concentration in the next week. The prediction results show that the Pearson correlation coefficients between the predicted values and the monitored values of the four monitoring sites have reached more than 0.9, reflecting a strong correlation. The relative average errors are around 10%. The GRU model prediction of NJAU (Nanjing Agricultural University)-Pukou Campus Site is the most accurate, and the correlation coefficient, MRE, and RMSE are 0.970, 7.85%, and 9.6049, respectively, reflecting the good prediction performance of the model. Therefore, this research supports the prediction of air quality in different cities and regions, so people can take protective measures in advance and reduce the damage caused by air pollution to human bodies

Division of Cow Production Groups Based on SOLOv2 and Improved CNN-LSTM

Udder conformation traits interact with cow milk yield, and it is essential to study the udder characteristics at different levels of production to predict milk yield for managing cows on farms. This study aims to develop an effective method based on instance segmentation and an improved neural network to divide cow production groups according to udders of high- and low-yielding cows. Firstly, the SOLOv2 (Segmenting Objects by LOcations) method was utilized to finely segment the cow udders. Secondly, feature extraction and data processing were conducted to define several cow udder features. Finally, the improved CNN-LSTM (Convolution Neural Network-Long Short-Term Memory) neural network was adopted to classify high- and low-yielding udders. The research compared the improved CNN-LSTM model and the other five classifiers, and the results show that CNN-LSTM achieved an overall accuracy of 96.44%. The proposed method indicates that the SOLOv2 and CNN-LSTM methods combined with analysis of udder traits have the potential for assigning cows to different production groups.Applied Science, Faculty ofAlumniNon UBCEngineering, School of (Okanagan)ReviewedFacultyResearcherOthe

Research into Heat Stress Behavior Recognition and Evaluation Index for Yellow-Feathered Broilers, Based on Improved Cascade Region-Based Convolutional Neural Network

The heat stress response of broilers will adversely affect the large-scale and welfare of the breeding of broilers. In order to detect the heat stress state of broilers in time, make reasonable adjustments, and reduce losses, this paper proposed an improved Cascade R-CNN (Region-based Convolutional Neural Networks) model based on visual technology to identify the behavior of yellow-feathered broilers. The improvement of the model solved the problem of the behavior recognition not being accurate enough when broilers were gathered. The influence of different iterations on the model recognition effect was compared, and the optimal model was selected. The final average accuracy reached 88.4%. The behavioral image data with temperature and humidity data were combined, and the heat stress evaluation model was optimized using the PLSR (partial least squares regression) method. The behavior recognition results and optimization equations were verified, and the test accuracy reached 85.8%. This proves the feasibility of the heat stress evaluation optimization equation, which can be used for reasonably regulating the broiler chamber

Catalytic Oxidation of Toluene over Fe-Rich Palygorskite Supported Manganese Oxide: Characterization and Performance

A series of Fe–rich palygorskite supported manganese oxide (X%Mn–Pal) catalysts were prepared by co-precipitation method and used as catalysts for toluene oxidation. The components and structure of the as-prepared catalysts were characterized by XRD, Raman, TEM, XPS, and in situ DRIFTS. The results showed that the 15%Mn–Pal catalyst exhibited the highest catalytic activity (T90 = 227 °C) and excellent cycling stability for the oxidation of toluene compared with other catalysts. The characterization results indicated that remarkable activity of the 15%Mn–Pal catalyst for toluene oxidation should be ascribed to the abundant surface oxygen vacancies. In situ DRIFTS results elucidated that benzoate was the main intermediate, which can be further oxidized into H2O and CO2. The objectives of this study are to (i) investigate the synergistic effect between Fe and Mn for toluene oxidation, (ii) develop an efficient catalyst for toluene abatement with high activity and low–cost, and (iii) promote the application of natural Fe–rich palygorskite in the control of VOCs

Catalytic Oxidation of Toluene over Fe-Rich Palygorskite Supported Manganese Oxide: Characterization and Performance

A series of Fe–rich palygorskite supported manganese oxide (X%Mn–Pal) catalysts were prepared by co-precipitation method and used as catalysts for toluene oxidation. The components and structure of the as-prepared catalysts were characterized by XRD, Raman, TEM, XPS, and in situ DRIFTS. The results showed that the 15%Mn–Pal catalyst exhibited the highest catalytic activity (T90 = 227 °C) and excellent cycling stability for the oxidation of toluene compared with other catalysts. The characterization results indicated that remarkable activity of the 15%Mn–Pal catalyst for toluene oxidation should be ascribed to the abundant surface oxygen vacancies. In situ DRIFTS results elucidated that benzoate was the main intermediate, which can be further oxidized into H2O and CO2. The objectives of this study are to (i) investigate the synergistic effect between Fe and Mn for toluene oxidation, (ii) develop an efficient catalyst for toluene abatement with high activity and low–cost, and (iii) promote the application of natural Fe–rich palygorskite in the control of VOCs

Reconstructing Lineage Hierarchies of Mouse Uterus Epithelial Development Using Single-Cell Analysis

The endometrial layer comprises luminal and glandular epithelia that both develop from the same simple layer of fetal uterine epithelium. Mechanisms of uterine epithelial progenitor self-renewal and differentiation are unclear. This study aims to systematically analyze the molecular and cellular mechanisms of uterine epithelial development by single-cell analysis. An integrated set of single-cell transcriptomic data of uterine epithelial progenitors and their differentiated progenies is provided. Additionally the unique molecular signatures of these cells, characterized by sequential upregulation of specific epigenetic and metabolic activities, and activation of unique signaling pathways and transcription factors, were also investigated. Finally a unique subpopulation of early progenitor, as well as differentiated luminal and glandular lineages, were identified. A complex cellular hierarchy of uterine epithelial development was thus delineated. Our study therefore systematically decoded molecular markers and a cellular program of uterine epithelial development that sheds light on uterine developmental biology

Gefitinib for Epidermal Growth Factor Receptor Activated Osteoarthritis Subpopulation Treatment

Osteoarthritis (OA) is a leading cause of physical disability among aging populations, with no available drugs able to efficiently restore the balance between cartilage matrix synthesis and degradation. Also, OA has not been accurately classified into subpopulations, hindering the development toward personalized precision medicine.In the present study, we identified a subpopulation of OA patients displaying high activation level of epidermal growth factor receptor (EGFR). With Col2a1-creERT2; Egfrf/f mice, it was found that the activation of EGFR, indicated by EGFR phosphorylation (pEGFR), led to the destruction of joints. Excitingly, EGFR inhibition prohibited cartilage matrix degeneration and promoted cartilage regeneration. The Food and Drug Administration (FDA)-approved drug gefitinib could efficiently inhibit EGFR functions in OA joints and restore cartilage structure and function in the mouse model as well as the clinical case report.Overall, our findings suggested the concept of the EGFR activated OA subpopulation and illustrated the mechanism of EGFR signaling in regulating cartilage homeostasis. Gefitinib could be a promising disease-modifying drug for this OA subpopulation treatment. Keywords: Osteoarthritis, Disease subpopulation, Epidermal growth factor receptor, Gefitini

Targeting downstream subcellular YAP activity as a function of matrix stiffness with Verteporfin-encapsulated chitosan microsphere attenuates osteoarthritis

Changes in the stiffness of chondrocyte extracellular matrix (ECM) are involved in the pathological progression of osteoarthritis (OA). However, the downstream responses of cartilage ECM stiffness are still unclear. YAP (Yes-associated protein) has been extensively studied as a mechanotransducer, we thus hypothesized that by targeting the downstream molecule activity of ECM stiffness could maintain chondrocyte phenotype and prevent cartilage degeneration in OA. Here, we showed that human cartilage matrix stiffened during pathological progression of OA, and the chondrocyte YAP activity was associated with ECM stiffness. We then mimicked the physiological and pathological stiffness of human cartilage by using PDMS-based substrates, and found that YAP was activated in chondrocytes seeded on stiff substrate, gradually losing their phenotype. In addition, it was observed that YAP was also significantly activated in mice OA development, and conditional knockout (cKO) of YAP in mice preserved collagen II expression and protected cartilage from degeneration in the OA model. Furthermore, intra-articular injection of YAP-selective inhibitor, Verteporfin, significantly maintained cartilage homeostasis in mice OA model. This study indicates that the application of mechanotransducer-targeted drugs could be a potential therapeutic approach for cartilage repair in OA.</p