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Regression analysis is an essential tool for modeling and analyzing data, which can be utilized in various areas for predictive analysis and discovering relationships between variables. However, guidelines such as the model's features, dataset selection, and method settings for using regression models to explore air pollution status in a region are not detailed. This paper applied regression analysis based on air quality data in Beijing from 2017 to 2021, to study the characteristics of regression models, provide research guidance, and update the air pollution research data based on the dataset. This paper drew the latest conclusions: (1) PM2.5and NO2are positively correlated on the test set from these 5 years, yielding a correlation coefficient of 0.7036 by using linear regression. The respective coefficient of determination on small-scale test sets for 2017, 2019, and 2021 is much lower than those derived from a 5-year dataset. Single-year dataset is not befitting for linear regression analysis. (2) The polynomial regression’s coefficient of determination on the training set is higher than that of the linear regression model, which is more proper for regression analysis on a 1-year dataset. (3) PM2.5and NO2concentrations are strongly positively correlated with whether the air is polluted or not, and the correlation coefficient on the test set from these 5 years is 0.9697. The accuracy of logistic regression in classifying air pollution status based on these two pollutants’ concentrations reaches 0.9430. Besides, this paper proposed some appropriate parameter settings for the logistic regression method provided by Python third-party library sklearn. Specifically, L2-type regularization is better optimized for the 2017–2021 dataset. L1-type regularization works better when applying a 1-year dataset. A boost in the inverse of the regularization strength to 1.8 will optimize the regularization.

Background:The abnormal expression of genes in serum may be associated with early diagnosis of patients with malignant tumors. This study was designed to screen for significantly differentially expressed genes (DEGs) that may be associated with gastric cancer using bioinformatic methods.Methods:RNA-seq data from gastric cancers were downloaded from the TCGA and GEO databases, and 1903 secretory genes were downloaded from the HPA database. The diagnostic secretory RNAs of gastric cancer were screened using least absolute shrinkage and selection operator regression analysis. Univariate Cox regression analysis was used to evaluate the prognostic significance of the results. Biological functions were performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Then, 640 cases of gastric cancer and paracancerous tissues were collected, and immunohistochemistry (IHC) was used to detect the expression of COL4A1.Results:In total, 25 upregulated differentially expressed genes (DEGs) were identified, which were secreted mainly in the blood and cell matrices. Six secretory genes (OLFM4, CEMIP, APOC1, CST1, COL4A1, and CD55) with diagnostic significance were identified, and the enrichment scores of these six genes were significantly associated with tumor stage. In addition, we found that increased COL4A1expression might be associated with a poor prognosis in patients with gastric cancer. Based on GO and KEGG analyses, we found COL4A1-related DEGs were mainly enriched in connective tissue development, collagen fibrous tissue-related processes, extracellular structure, extracellular matrix (ECM) tissue, and related to the ECM receptor-related pathway, focal adhesion, and PI3K-Akt signaling pathway. Moreover, the results of immunohistochemical analyses showed that the COL4A1protein level in gastric cancers was also higher than in the matched paracancerous tissues.Conclusions:In this study, we found six upregulated secretory genes, including OLFM4, CEMIP, APOC1, CST1, COL4A1, and CD55which we hypothesized to be significant DEGs for the diagnosis of gastric cancer. Our data also suggest that COL4A1may play an important role in the diagnosis and prognosis of gastric cancer.

In crop growth management, phenotypic traits are an important basis for judging growth status. Manual measurements are labor-intensive, unstable and time-consuming. We propose an image processing pipeline to estimate multiple lettuce traits (fresh weight, dry weight, plant height, diameter, leaf area) based on the lightweight DeepLabV3 + network. Accurate and rapid segmentation of crops from backgrounds is the basis for phenotypic research. First, we propose to combine DeepLabV3 + and MobilenetV2 to realize a high-precision and fast segmentation of lettuce in complex backgrounds and illuminations. Based on the segmentation results, we extracted the morphological factors and vegetation indices. Random forest (RF), partial least squares regression (PLSR) and support vector machine were applied to predict the multiple lettuce traits and compared for optimal model selection. Results showed that DeepLabv3 + (with Mobilenetv2) has the best segmentation performance with pixel accuracy of 97.520% and 99.821%, mIoU of 88.661% and 98.517%, and segmentation speeds with 0.094 and 0.049 ms per image in dataset D3 and dataset D4. PLSR had the highest accuracy in predicting fresh weight, dry weight, diameter and leaf area, with R2of 0.898, 0.899, 0.931 and 0.904, respectively. RF yielded the highest accuracy in predicting plant height, with R2of 0.858. We proposed method for estimating phenotypic characteristics of lettuce based on deep learning has excellent performance and important application value for lettuce growth monitoring and yield estimation.

ABSTRACTLittle is known about the regulatory effect of microbiota on the proliferation and regeneration of ISCs. Here, we found that L. reuteristimulated the proliferation of intestinal epithelia by increasing the expression of R-spondins and thus activating the Wnt/β-catenin pathway. The proliferation-stimulating effect of Lactobacilluson repair is further enhanced under TNF -induced intestinal mucosal damage, and the number of Lgr5+cells is maintained. Moreover, compared to the effects of C. rodentiumon the induction of intestinal inflammation and crypt hyperplasia in mice, L. reuteriprotected the intestinal mucosal barrier integrity by moderately modulating the Wnt/β-catenin signaling pathway to avoid overactivation. L. reuterihad the ability to maintain the number of Lgr5+cells and stimulate intestinal epithelial proliferation to repair epithelial damage and reduce proinflammatory cytokine secretion in the intestine and the LPS concentration in serum. Moreover, activation of the Wnt/β-catenin pathway also induced differentiation toward Paneth cells and increased antimicrobial peptide expression to inhibit C. rodentiumcolonization. The protective effect of Lactobacillusagainst C. rodentiuminfection disappeared upon application of the Wnt antagonist Wnt-C59 in both mice and intestinal organoids. This study demonstrates that Lactobacillusis effective at maintaining intestinal epithelial regeneration and homeostasis as well as at repairing intestinal damage after pathological injury and is thus a promising alternative therapeutic method for intestinal inflammation.

•Tomato chlorophyll a content grown in aeroponics system had the top advantages.•Both tomato photosynthesis and stomatal conductance maximized at the development stage.•There were no significant differences among nutrient delivery systems in the per fruit fresh mass.

Root is one of the most important organs for plants to obtain water and nutrients so that its morphological research is critique for identifying plant growth conditions. Aiming at breakthrough of barriers and obtaining accurate root phenotype data based on the original plant root image, a method of Arabidopsis thaliana root image restoration based on GAN (generative adversarial network) was proposed in this paper. Firstly, a second generation Kinect camera is used to capture the matched data set for training the GAN, which includes high-resolution images of some objects and their matched fuzzy and distort images, and high-resolution images of Arabidopsis’ roots and their images in the biogel. Secondly, a GAN with attention mechanism is constructed and trained. The network mainly consists of two parts: the generator and the discriminator with attention mechanism. It is multi-layer convolution network, except that the generator adopts a de-convolution structure to carry out the super-resolution reconstruction. The generator is responsible for converting a fuzzy image into high-resolution image, and the discriminator is used to distinguish whether the inputted image is derived from the prepared dataset or generated by the generator. With the progress of network training, the generator is getting better and better at generating images, the same is true for the effect of the discriminator discriminating the image, that is, the better mapping relationship between the blurred or partially missing image and the high resolution complete image is established. Finally, import the root image of the Arabidopsis planted in the biogel into the trained network and the repaired and restored root image can be obtained. Compared with the original image, the restored one has more accurate details and accordingly more accurate root morphology parameters are computed. The experiment results showed that the proposed method can be used to achieve the super-resolution reconstruction and complete the incomplete or blur Arabidopsis root images.

This paper explores the recent development and innovative uses of augmented reality (AR) in formal and informal education. Our research categorizes current AR technologies, introduces a review of relevant literature, and presents case studies illustrating AR implementation utilizing different pedagogical approaches. Based on current trends, the educational potential of AR tools and systems is discussed and factors impacting large-scale use in teaching and training are presented.

With the fast development of online and mobile technologies, individualized or personalized learning is becoming increasingly important. Online courses especially Massive Open Online Courses (MOOCs) often have students from many countries, with different prior knowledge, expectations, and skills. They in particular could benefit from learning materials or learning systems that are customized to meet their needs. On this note, this paper suggests a personalized recommendation system for learners in online courses. The system recommends learning resources such as relevant courses to learners enrolled in formal online courses, by using a combination of association rules, content filtering, and collaborative filtering. Pilot testing of this system in the Shanghai Lifelong Learning Network, a platform for free and open education, indicates that this recommendation system can improve the utilization rate of educational resources and also promote the learning autonomy and efficiency of students.

Neighborhood impacts on decisions about out-migration, though less explored and understood than individual- and household-level impacts, can be significant; the integration of these impacts in decision-making analyses may reveal mechanisms undetectable otherwise. However, detecting these impacts can be difficult, especially when prior theorization is lacking. In this paper, we compare three methods of measuring and reducing neighborhood impacts: multilevel modeling, eigenvector spatial filtering (ESF) based on Euclidean distance, and ESF based on topological distance. The second ESF method, in particular, is developed to accommodate the elevation profile of our study site at the Fanjingshan National Nature Reserve of Guizhou Province, China. Our previous work identified a suite of socioeconomic factors at individual and household levels that influence out-migration decisions, to which we apply the aforementioned methods to identify and control for neighborhood impacts. While the non-spatial and multilevel models generated nearly identical results, the results from the ESF models present several considerable differences. The Moran's I statistics for each non-binary variable show that spatial autocorrelation is present in some variables. Among the spatially autocorrelated variables, there are different degrees of change in significance levels when compared to those in the non-spatial model. Although most changes detected are small, we identify an additional significant variable—in our case area farmed—that was not observed before we apply the ESF. Changes in the significance levels of several other independent variables are also more significant after we applied the topological distance definitions. Methodologically, the new results suggest using the topological ESF approach may allow other studies to take into account spatial autocorrelation, especially in more rural areas where elevation differences are significant.

Phase shifter is one of the key devices in microwave photonics. We report a silicon microring resonator with coupling modulation to realize microwave phase shift. With coupling tuning of the Mach-Zehnder interferometer (MZI) coupler to change the resonator from under-coupling to over-coupling, the device can realize a π phase shift on the incoming microwave signal with a frequency up to 25 GHz. The device can also realize 2.5π continuous phase tuning by manipulating the three DC bias voltages applied on the MZI coupler.

Since the industrial revolution, anthropogenic activities, such as fossil fuel use and deforestation, have caused a dramatic increase in the atmospheric CO2concentration. To understand how the growth and development in cereal crops may respond to elevated CO2, it is necessary to determine if the leaves of crops grown in a closed artificial ecosystem have a fully developed photosynthetic apparatus and whether or not photosynthesis in these leaves is more responsive to an elevated CO2concentration. To address this issue, we evaluated the response of the photosynthetic characteristics, antioxidant capacity, and water use efficiency of wheat (Triticum aestivumL.) under four CO2concentrations (500, 1000, 3000, and 5000 ppm) for 3 d in Lunar Palace‐1, which is the first bioregenerative life support system developed in China. The results showed that wheat cultivated at 1000 ppm from vegetative growth to maturity was characterized by more appropriate relative water content, membrane stability index, photosynthetic rate, chlorophyll concentration, and antioxidant capacity, which was more beneficial to growth and development in a closed artificial environment. There were significant effects with increased CO2concentration on the effective quantum yield of PSII and photosynthetic electron transport of wheat plants. Furthermore, elevated CO2controlled the transpiration rate, which enhanced water use efficiency. During ripening, wheat aging may be accelerated by elevated CO2, which promotes grain growth and maturing.

Yellow mealworm (Tenebrio molitorL.) is one of the animal candidates for space bioregenerative life support systems. In this study, T. molitorwas involved in a 105-day multi-crew closed integrative BLSS experiment for a tentative rearing study. The results showed that the overall bioconversion rate (ratio of T. molitorgained to the total feed consumed) of T. molitorreared in the closed system was 8.13%, while 78.43% of the feed was excreted as frass. T. molitorreared in the closed system had a good nutritional composition. The eight essential amino acids (EAAs) in T. molitorlarvae accounted for 41.30% of its total amino acids, and most EAA contents were higher than the suggested amino acid pattern recommended by the FAO/WHO. T. molitorsample obtained in this work was high in polyunsaturated fatty acids, and low in saturated fatty acids, indicating that the composition of fatty acids was beneficial to human health. In the open environment outside the experimental system, we simultaneously reared three parallel groups of larval T. molitorusing the same feeding regime and temperature condition. Compared with T. molitorreared in the open environment, larvae reared in the closed system grew slower. With the course of time t, the growth rate of T. molitorin the open environment was 0.839e0.017ttimes that of larvae in the closed system. This paper can provide data for future design and improvement of BLSS containing a T. molitorrearing unit.

The gradual development of intelligent learning (iLearning) systems has prompted the changes of teaching and learning. This paper presents the architecture of an intelligent learning (iLearning) system built upon the recursive iLearning model and the key technologies associated with this model. Based on this model and the technical structure of a cloud-based intelligent system, the authors developed an exemplary iLearning system-”Mobile Class”, accessible from the Shanghai Lifelong Learning Network, an online platform for the continuing education of Shanghai residents. This cloud-based intelligent learning (C-iLearning) system can adopt the cloud management model and synchronize users' learning process in the clouds, so as to support users' continuous learning with different devices. Formal testing with target users revealed the effectiveness of this system in supporting anytime anywhere learning.

The data gathered provided valuable insights into the attitudes of teachers in relation to distance education. Most of the participants of the survey felt that with adequate training, technology could be a valuable tool in education. The confidence of the respondents in their own computer skills was rather high, although it was apparent that this confidence did not cover the application of their computer skills to the development of online instruction. None of the respondents currently use a course Web site, and most had little interest in doing so. There was also a strong feeling that there was little support from administration, which made the incentives for providing online instruction low. The survey also raised additional questions such as what type of incentives would be required to make distance education a desirable option for a teacher, or whether they would be interested in receiving training on preparing distance course materials. Further research into possible correlation between the subject matter taught and feelings towards online instruction would be beneficial.

We demonstrate a high-speed silicon carrier-depletion Michelson interferometric (MI) modulator with a low on-chip insertion loss of 3 dB. The modulator features a compact size of <1 mm^2 and a static high extinction ratio of >30 dB. The V_π·L_π of the MI modulator is 0.95–1.26 V·cm under a reverse bias of −1 to −8 V, indicating a high modulation efficiency. Experimental results show that a 4-level pulse amplitude modulation up to 20 Gbaud is achieved with a bit error rate of 6×10^−3, and a 30 Gb/s binary phase-shift-keying modulation is realized with an error vector magnitude of 25.8%.