Effects of exogenous organic/inorganic nitrogen addition on carbon pool distribution and transformation in grassland soil.

AIMS: Increases in nitrogen (N) deposition may significantly affect the organic carbon (OC) cycle in soil. The inconsistent findings of the influence of added N on soil OC pools highlight the need of quantifying responses of the OC pool distribution to N addition. Moreover, the influence of N addition with a mixture of organic and inorganic N on OC pool distribution and stabilization in grassland soil remains unclear. METHODS: We carried out a five-year field experiment with adding N to examine the effects of different types of N addition on soil OC pool distribution and transformation in a meadow steppe in Inner Mongolia. We applied N in the ratios of inorganic N (IN) and organic N (ON) at 10:0 (N1), 7:3 (N2), 5:5 (N3), 3:7 (N4), 0:10 (N5), and 0:0 (CK), respectively. We measured OC content in bulk soil, particulate organic matter (POM), and mineral-associated organic matter (MAOM) fractions. Additionally, a short-term soil incubation was conducted to assess potential OC mineralization. RESULTS: Our study showed no significant effect on soil organic carbon content of different ratios of IN/ON addition. N addition reduced microbial biomass C/N ratio, the fraction of mineral-associated organic matter, cumulative CO2 emission, and microbial metabolic quotient. Compared with ON addition alone, IN addition alone showed a stronger effect on the C in different soil fractions and soil OC mineralization. The particulate organic matter (POM) fraction was more sensitive to N addition than the mineral-associated organic matter (MAOM) fraction. CONCLUSIONS: Our results suggest that the contribution of N in organic and inorganic forms affecting OC pool distribution with different turnover rates should be considered when assessing the effects of N addition types on soil OC processes in grassland.

AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network: A STARD compliant diagnosis research.

Leukemia diagnosis based on bone marrow cell morphology primarily relies on the manual microscopy of bone marrow smears. However, this method is greatly affected by subjective factors and tends to lead to misdiagnosis. This study proposes using bone marrow cell microscopy images and employs convolutional neural network (CNN) combined with transfer learning to establish an objective, rapid, and accurate method for classification and diagnosis of LKA (AML, ALL, and CML). We collected cell microscopy images of 104 bone marrow smears (including 18 healthy subjects, 53 AML patients, 23 ALL patients, and 18 CML patients). The perfect reflection algorithm and a self-adaptive filter algorithm were first used for preprocessing of bone marrow cell images collected from experiments. Subsequently, 3 CNN frameworks (Inception-V3, ResNet50, and DenseNet121) were used to construct classification models for the raw dataset and preprocessed dataset. Transfer learning was used to improve the prediction accuracy of the model. Results showed that the DenseNet121 model based on the preprocessed dataset provided the best classification results, with a prediction accuracy of 74.8%. The prediction accuracy of the DenseNet121 model that was obtained by transfer learning optimization was 95.3%, which was increased by 20.5%. In this model, the prediction accuracies of the normal groups, AML, ALL, and CML were 90%, 99%, 97%, and 95%, respectively. The results showed that the leukemic cell morphology classification and diagnosis based on CNN combined with transfer learning is feasible. Compared with conventional manual microscopy, this method is more rapid, accurate, and objective.

Quantitative analysis of glycated albumin in serum based on ATR-FTIR spectrum combined with SiPLS and SVM.

A rapid quantitative analysis model for determining the glycated albumin (GA) content based on Attenuated total reflectance (ATR)-Fourier transform infrared spectroscopy (FTIR) combining with linear SiPLS and nonlinear SVM has been developed. Firstly, the real GA content in human serum was determined by GA enzymatic method, meanwhile, the ATR-FTIR spectra of serum samples from the population of health examination were obtained. The spectral data of the whole spectra mid-infrared region (4000-600 cm-1) and GA's characteristic region (1800-800 cm-1) were used as the research object of quantitative analysis. Secondly, several preprocessing steps including first derivative, second derivative, variable standardization and spectral normalization, were performed. Lastly, quantitative analysis regression models were established by using SiPLS and SVM respectively. The SiPLS modeling results are as follows: root mean square error of cross validation (RMSECVT) = 0.523 g/L, calibration coefficient (RC) = 0.937, Root Mean Square Error of Prediction (RMSEPT) = 0.787 g/L, and prediction coefficient (RP) = 0.938. The SVM modeling results are as follows: RMSECVT = 0.0048 g/L, RC = 0.998, RMSEPT = 0.442 g/L, and Rp = 0.916. The results indicated that the model performance was improved significantly after preprocessing and optimization of characteristic regions. While modeling performance of nonlinear SVM was considerably better than that of linear SiPLS. Hence, the quantitative analysis model for GA in human serum based on ATR-FTIR combined with SiPLS and SVM is effective. And it does not need sample preprocessing while being characterized by simple operations and high time efficiency, providing a rapid and accurate method for GA content determination.

A simple quality assessment index for stereoscopic images based on 3D gradient magnitude.

We present a simple quality assessment index for stereoscopic images based on 3D gradient magnitude. To be more specific, we construct 3D volume from the stereoscopic images across different disparity spaces and calculate pointwise 3D gradient magnitude similarity (3D-GMS) along three horizontal, vertical, and viewpoint directions. Then, the quality score is obtained by averaging the 3D-GMS scores of all points in the 3D volume. Experimental results on four publicly available 3D image quality assessment databases demonstrate that, in comparison with the most related existing methods, the devised algorithm achieves high consistency alignment with subjective assessment.

[Effects of soil moisture before sowing and nitrogen fertilization on winter wheat yield and water use on Weibei Plain of Loess Plateau].

A five-year site-specific experiment was carried out on the Weibei Plain of Loess Plateau in Shaanxi Province of northwestern China to study the effects of soil moisture regime before sowing (SMBS) and nitrogen fertilization on the grain yield and water use of winter wheat. On the basis of applying 100 kg x hm(-2) of P2O5, five nitrogen fertilization levels (0, 80, 160, 240, and 320 kg N x hm(-2)) were installed, and took the precipitation in the five years into consideration. In the study area, there was a linear correlation between the precipitation in summer (from July to September) and the SMBS, with an increment of 0.6 mm SMBS per 1 mm precipitation. For a stable or high wheat yield, the SMBS should be kept around 550 mm, and the precipitation in summer should be around 370-390 mm. In the years with adequate precipitation (> 386 mm) in summer, the SMBS in present winter wheat growth season less decreased by the increase of the nitrogen fertilization rate in previous growth season. However, in the years with less precipitation in summer (< 350 mm), the SMBS in present winter wheat growth season decreased significantly by 9-17 mm when the nitrogen fertilization rate in previous growth season was increased by each 100 kg N x hm(-2). In addition to SMBS, adequate precipitation in key growth stages was another important factor to ensure the wheat yield in dryland area because 1 mm SMBS could produce 10.6-11.4 kg x hm(-2) of wheat grain, and 1 mm precipitation occurred in the key growth stages could lead to more grain yield as high as 30.6-33.1 kg x hm(-2). Variation analysis showed that nitrogen fertilization rate affected the utilization degree of SMBS by winter wheat, while SMBS controlled the allocation and transportation of dry matter from vegetative parts to grain.