[Clinical Observation on Haploid Hematopoietic Stem Cells Combined with Umbilical Cord Blood Double Transplantation for Aplastic Anaemia in Children].

OBJECTIVE: To explore the efficacy and safety of haploidentical hematopoietic stem cell transplantation combined with umbilical cord blood infusion for the treatment of aplastic anaemia in children. METHODS: Nine cases of children with aplastic anaemia treated with umbilical cord blood combined with haploidentical hematopoietic stem cell transplantation at the People's Hospital of Henan University of Chinese Medicine from January 1, 2021 to September 15, 2023 with a median age of 11(2-13) years and a median follow up of 18(7.5-21) months were included, and the clinical data were retrospectively analyzed. Hematopoiesis reconstitution, the incidence of graft-versus-host disease(GVHD), infections and survival of the patients were analyzed. RESULTS: All 9 children were successfully implanted. The median time to neutrophil and platelet implantation was 11.11±1.27 d and 12.44±3.36 d, respectively. One case developed acute gastrointestinal GVHD of degree I, which was improved after treatment, and the patient developed superficial gastritis and chronic gastrointestinal GVHD at a later stage, which is currently under clinical follow-up. Acute GVHD of II-IV degree was 0%. Hemorrhagic cystitis in 3 cases, CMV infection in 5 cases and bacterial and fungal infections in 5 cases improved with symptomatic treatment.All 9 children demonstrated complete donor chimerism within 1 month after transplantation, at two years of follow-up, all nine children survived without recurrence or development of grade II-IV GVHD, and there were no children with transplant-related deaths. CONCLUSION: Haploidentical hematopoietic stem cell transplantation combined with umbilical cord blood transfusion for aplastic anaemia in children has a low incidence and mild degree of GVHD, with significant efficacy, and can be used as a therapeutic option for children without an HLA full donor chimeric match.

Predicting novel salivary biomarkers for the detection of pancreatic cancer using biological feature-based classification.

AIM: The use of saliva as a diagnostic fluid enables non-invasive sampling and thus is a prospective sample for disease tests. This study fully utilized the information from the salivary transcriptome to characterize pancreatic cancer related genes and predict novel salivary biomarkers. METHODS: We calculated the enrichment scores of gene ontology (GO) and pathways annotated in Kyoto Encyclopedia of Genes and Genomes database (KEGG) for pancreatic cancer-related genes. Annotation of GO and KEGG pathway characterize the molecular features of genes. We employed Random Forest classification and incremental feature selection to identify the optimal features among them and predicted novel pancreatic cancer-related genes. RESULTS: A total of 2175 gene ontology and 79 KEGG pathway terms were identified as the optimal features to identify pancreatic cancer-related genes. A total of 516 novel genes were predicted using these features. We discovered 29 novel biomarkers based on the expression of these 516 genes in saliva. Using our new biomarkers, we achieved a higher accuracy (92%) for the detection of pancreatic cancer. Another independent expression dataset confirmed that these novel biomarkers performed better than the previously described markers alone. CONCLUSION: By analyzing the information of the salivary transcriptome, we predict pancreatic cancer-related genes and novel salivary gene markers for detection.

[A Study on Predicting Model of Organic Matter Contend Incorporating Soil Moisture Variation].

Soil organic matter (SOM) is one of the most important measuring indexes of soil fertility. How to predict SOM spatial distribution precisely has great significance to soil carbon storage estimation and precision agriculture development. Traditional measurement of SOM, although with higher accuracy, consumes a lot of labor resources and costs long-term monitoring period, therefore, it is hard to achieve dynamic monitor of SOM. Spectroscopy technique has been used in SOM and other soil physicochemical parameters quick measurement. However spatial inversion model accuracy of SOM based on remote sensing images is relatively lower than laboratory model accuracy due to the influence of soil moisture, roughness and so on. In recent years, most studies have not eliminated the effect of moisture. Since moisture has great influence on SOM spectra reflectance, this study introduced the temporal information combined with the spectral information in order to solve this problem. Soil moisture has differences in multi period remote sensing images, and the spectra reflectance is also different. Based on the combination of reflectance from of two periods remote sensing images, the spectral index was constructed to predict SOM in this study. MODIS images of study area acquired in this study area (Blacksoil zone) because of the advantage of high temporal resolution. Spectra reflectance of MODIS images were used to analyze the effect of moisture on soil spectral reflectance, and then the spectral prediction models of SOM were built based on the comprehensive impacts of SOM and soil moisture. The results shows that: (1) the accuracy of SOM prediction model based on single image was lower without consideration of moisture effect, The Root mean square error (RMSE) of SOM prediction model were 0.591, 0.522, 0.545, 0.553, and the determination coefficient (R(2)) were 0.505, 0.614, 0.562, 0.568, 0.645 respectively based on the day of year (DOY) 117, 119, 130, 140, 143 single image. (2) Model with multi temporal images (DOY119 and 143) which considered the effect of moisture and SOM showed better predictive ability. RMSE was 0.442 while R2 was 0.723. Therefore the accuracy and stability of the model were significantly improved, and it can be used to predict the spatial distribution of SOM in regional scale. This study provides important information for regional soil fertility evaluation, soil carbon storage estimation, and precision agriculture development.

[Analysis on Impact Factors of Thermal Polarized Radiation Characteristics of the Soil].

As an important content of the nature, soil has great influence on the formation of ecological system and human life. Therefore, the study of soil's polarized thermal radiation characteristics has great practical significance. There have been few reports about the study of the polarized radiation characteristics of the soil in 2π space. The results showed that the polarized brightness temperature performed nonlinearly as the change of detection angles between 0° to 80°. However, polarized brightness temperature increased greatly when the detection angle changed from 60° to 80°. It also changed under different azimuth angles. The polarized brightness temperature increased as the growth of the azimuth angles in the range of 0° to 240°, but its tendency was opposite in the range between 240° and 320°. The channels and polarized angles both influenced the polarized brightness temperature. Their amplitudes of fluctuation of their own curves were gentle and the temperatures of different agrotype were various. The order was Meadow Soil>Leached Chernozem>Chernozem>Aeolian Soil. These results provide significant foundation to the study about the basic theory of thermal infrared polarization remote sensing.

[Study on the Prediction of Cotton Yield within Field Scale with Time Series Hyperspectral Imagery].

Pixel-based processing method mainly extracts spectral information from hyperspectral remote sensing images, but site specific management zone (SSMZ) delineation and crop yield estimation with images need to take spatiotemporal heterogeneity into account. As the spatial resolution of remote sensing data increases, the so-called "salt-and-pepper" problem of pixel-based classification becomes more serious. The spatiotemporal heterogeneity of soil properties and crop biophysical parameters are mainly delineated with grid sampling and geostatistics interpolation, but the widely used method has some problems: time consuming and high cost. Satellite imageries are introduced to delineate SSMZ, but there are also problems needed to be resolved: (1) single date imagery is used to map SSMZ which is difficult to determine the optimal date for SSMZ delineation; (2) only few SSMZs were mapped, which limited application of site specific fertilizing and management; (3) pixel-based method for SSMZ delineation didn't concern the spatial relationship between pixels and site specific management does not implement at pixel level, but at SSMZ level. To improve the accuracy of crop yield estimation, a time-series of hyperspectral airborne images with high spatial resolution (1 m) of a cotton field, which is located in San Joaquin Valley, California US, were acquired and classified by using object-oriented segmentation, then yield predicting models were built, and the accuracy and stability of yield models were validated with determining coefficients R2 and the root mean square error (RMSE). Results are as follows: (1) object-oriented SSMZ delineating method combines spectral, spatial and temporal information, reduces noises in images and yield data, improves the accuracy of yield prediction; (2) for same SSMZ number, first derivative predicting model is more accurate; (3) for same spectral input, models with fewer SSMZs show higher accuracy, which is due to spatial errors of airborne images and yield data. The results will improve monitoring methods for crop growth and yield while accelerate the application of UAV remote sensing in precision agriculture.

[Effect of spectral resolution on black soil organic matter content predicting model based on laboratory reflectance].

Laboratory reflectance of Black soil samples was re-sampled with different spectral resolution, and the correlation between soil organic matter (OM) and reflectance, spectral variables was analyzed to study the effect of spectral resolution on black soil OM predicting model. The results are as follows: the spectral response range of black soil OM is between 445 and 1 380 nm, high OM content shades the spectral effect of other soil properties. The precision of black soil OM predicting models increases and decreases with spectral resolution, and the maximum accuracy is at 50 nm, which is wider than hyperspectral resolution, and narrower than the bandwidth of multispectral sensors; with the derivative of logarithmic reflectance reciprocal as input variables, the optimal black soil organic matter predicting model shows high accuracy, with R2 = 0.799 and RMSE = 0.439; the results can provide the academic and technical support for soil organic matter remote sensing reversing and quick instrument developing.

[Study on relationship between new characteristic parameters of spectral curve and chlorophyll content for rice].

It is of significance to monitor chlorophyll content with hyperspectral data for crop growth diagnosis in field. In the study, with the point of view that spectral curve shapes display "tall, low, fat and thin" morphological changes, we proposed some new characteristic parameters from spectral curve such as the ascensive or degressive velocities of segments composing peak or valley shapes in spectral curve, and angles formed by the lines fitting the segments of two sides of peak or valley curves, and used the normalized spectra to analyze correlation between these parameters and rice chlorophyll content. The result shows that (1) there is a good negative correlation between rice chlorophyll content and normalized reflectance spectra from 520-740 nm; (2) characteristic parameters from green peak region of spectral curve display better correlation with rice chlorophyll content, which makes it possible to utilize the parameters to monitor crop chlorophyll content, and will provide new ideas and methods for carrying out crop growth diagnosis with hyperspectral data.

Determinations of mifepristone and its metabolites and their pharmacokinetics in healthy female Chinese subjects.

The aim of this study is to establish an HPLC method for simultaneous determinations of mifepristone and its metabolites, mono-demethylated mifepristone, di-demethylated mifepristone and C-hydroxylated mifepristone in plasma and to evaluate the pharmacokinetic characteristics of mifepristone tablet. Twenty healthy female Chinese subjects were recruited and a series of blood samples were collected before and after 0.25, 0.5, 1.0, 1.5, 2.0, 4.0, 8.0, 12.0, 24.0, 48.0, 72.0 and 96.0 hours administration by a single oral dose of 75 mg mifepristone tablet. Mifepristone and its three metabolites were extracted from plasma using ethyl acetate and determined by high performance liquid chromatography. The main pharmacokinetic parameters of mifepristone and its metabolites, including Cmax, tmax, MRT, t(1/2), V, CL, AUC(0-96 h) and AUC(0-infinity), were calculated by Drug and Statistical Software Version 2.0. The simple, accurate and stable method allows the sensitive determinations of mifepristone and its metabolites in human plasma up to 4 days after oral administration of 75 mg mifepristone tablet and the clinical applications of their pharmacokinetic studies.

[Black soil organic matter predicting model based on field hyperspectral reflectance].

To develop soil organic matter (OM) quick measuring methods, deepen the application of remote sensing in agriculture, improve agricultural production and management way, and promote the development of quantitative remote sensing studies relating to terrestrial ecosystem, field hyperspectral reflectance in the visible/near infrared bands of black soil in Hailun city, northeast China, was collected and analyzed with spectral analysis methods to discover the spectral characteristics of field reflectance and its influencing factors, and the spectral indices were derived, then black soil organic matter predicting model based on the correlation between OM content and spectral indices was built. Root mean squared error (RMSE) was introduced to validate the predictability and precision of the models, and coefficient of the determination (R2) was used to evaluate stability of the models. The results are as follows: the main spectral region of remarkable differences between field black soil reflectance curves is less than 1 250 nm, especially less than 1 000 nm; OM is the main factor determining the curve shape of field black soil reflectance, anc there are single or double spectral wave troughs for different soil samples because of varying OM content at the spectral region less 1 100 nm; correlation between OM and differential coefficient of logarithmic reflectance reciprocal (DCLRR) is much more significant than that between OM and other reflectance or its transforms, and the maximum coefficient of correlation is at 1 260 nm; the predicting model for black soil OM content is built with DCLRR at 1 260 nm as independent varialble and OM as dependent variable, and the coefficients of determination R2 of the model is 0.71, RMSE is 0.42, so the model is quite good in stability and predictability, and can be used in fast testing of organic matter in black soil.

[Extracting black soil border in Heilongjiang province based on spectral angle match method].

As soils are generally covered by vegetation most time of a year, the spectral reflectance collected by remote sensing technique is from the mixture of soil and vegetation, so the classification precision based on remote sensing (RS) technique is unsatisfied. Under RS and geographic information systems (GIS) environment and with the help of buffer and overlay analysis methods, land use and soil maps were used to derive regions of interest (ROI) for RS supervised classification, which plus MODIS reflectance products were chosen to extract black soil border, with methods including spectral single match. The results showed that the black soil border in Heilongjiang province can be extracted with soil remote sensing method based on MODIS reflectance products, especially in the north part of black soil zone; the classification precision of spectral angel mapping method is the highest, but the classifying accuracy of other soils can not meet the need, because of vegetation covering and similar spectral characteristics; even for the same soil, black soil, the classifying accuracy has obvious spatial heterogeneity, in the north part of black soil zone in Heilongjiang province it is higher than in the south, which is because of spectral differences; as soil uncovering period in Northeastern China is relatively longer, high temporal resolution make MODIS images get the advantage over soil remote sensing classification; with the help of GIS, extracting ROIs by making the best of auxiliary data can improve the precision of soil classification; with the help of auxiliary information, such as topography and climate, the classification accuracy was enhanced significantly. As there are five main factors determining soil classes, much data of different types, such as DEM, terrain factors, climate (temperature, precipitation, etc.), parent material, vegetation map, and remote sensing images, were introduced to classify soils, so how to choose some of the data and quantify the weights of different data layers needs further study.

[Study on the main influencing factors of black soil spectral characteristics].

To develop soil properties quick measuring methods, promote the development of quantitatively remote sensing studies relating to terrestrial ecosystem, and deepen the application of remote sensing in agriculture, the hyperspectral reflectance of black soil in Songnen Plain, northeast China, was analyzed with spectral analysis methods (continuum removal, spectral angle match and spectral feature fitting) and statistic methods to discover the reflectance spectral characteristics and its influencing factors. The results are as follows: the soil parent material determines the basic characteristics of reflectance of the black soil, which is the mixture of montmorillonite and illite, and consistent with mineral analysis result. Organic matter is the main factor determining the curve shape of black soil reflectance in the region shorter than 1 000 nm, and indirectly influencing the reflectance in the region longer than 1 000 nm because of the correlation with soil moisture and mechanical composition. The varying process of soil reflectance with changing soil moisture can be quantitatively described with cubic equation, and moisture mainly changes the reflectance value but not the curve shape. Black soil reflectance is not influenced significantly by Fe, which is different from soils of south China. Roughness mainly impacts on the soil reflectance value but not the shape feature. The spectral feature of straw reflectance is remarkably different to that of black soil, and impacts on both the value and curve shape of black soil reflectance. Different soil tillage measurements result in different moisture holding ability and the amount of straw for different farm fields, and influence the reflectance further, with the order of soil reflectance from high to low is: no tillage, moldboard tillage, combination tillage, reduced tillage, and rotary tillage.

[Soil taxonomy on the basis of reflectance spectral characteristics].

Soil spectral reflectance is the comprehensive representation of soil physical and chemical parameters, and its study is the physical basis for soil remote sensing and provides a new way and standard for soil properties themselves' research. Soil room spectra significantly correlate with that derived from hyperspectral images. So the room spectra are very important for soil taxonomy and investigation. To seek for the feasibility of soil taxonomy on the basis of topsoil reflectance spectral characteristics, and provide the theory foundation for quick soil taxonomy based on remote sensing methods, the spectral reflectance in the visible and near infrared region (400-2 500 nm) of 248 soil samples (black soil, chernozem, meadow soil, blown soil, alluvial soil) collected from Nongan county, Jilin province was measured with a hyperspectral device in room, and the soil spectral characteristics were determined with continuum removal method, and soil spectral indices (spectral absorption area, depth and asymmetry) were computed, which were introduced into BP network models as external input variables. The models consist of three layers (input, output and hidden layer), the training function is "TRAINLM", learning function "LEARNGDM", and transferring function "TAN SIG". The results showed that: (1) There are some differences among different soils in their spectral characteristics, but with similar parental matrix and climate, the spectral differences of soils in Nongan county are not significant. So it's difficult to analyze soil spectral characteristics based on soil reflectance. (2) The curves after continuum removal strengthened soil spectral absorption characteristics, and simplified soil spectral analysis. The soil spectral curves in Nongan county mainly have five spectral absorption vales at 494, 658, 1 415, 1 913 and 2 206 nm, and the former two vales are caused by soil organic matter, Fe and mechanical composition, the latter three are due to soil moisture; the differences of the latter three vales among different soils are not apparent, and the significant differences are in the former two vales region. (3) Soil reflectance is sensitive to organic matter, soil moisture, Fe, mechanical composition, roughness, and so on. The sensitivity of soil spectral indices derived with continuum removing method is decreased. Then the models with these indices as input variables are more stable and general. As the input variables were external, the BP network model based on the former two vales' shape characteristics was better than that based on reflectance values or all five vales, the classifying accuracy of the main three soils (chernozem, meadow soil, blown soil) was bigger than 60%, and the model could be used for soil taxonomy. However, this work still needs further study, and to improve classifying accuracy, auxiliary data, such as topography, vegetation, and land use should be introduced.

[Black soil organic matter content prediction based on reflectance simulation models].

The hyperspectral reflectance characteristics of black soil in Heilongjiang province were analyzed quantitatively, and then the main characteristic controlling points of reflectance were determined and used to build soil reflectance prediction models; the relationship between organic matter content and reflectance and the coefficients of simulating models were studied, Black soil organic matter content spectral prediction models were built, and the feasibility of hyperspectral reflectance simulatiib method was discussed. The results are as follows (1) Organic matter content is the determining factor of black soil reflectance characteristics in the range less than 1000 nm. When the content is low, the covering effect of organic matter on the black soil parent matrix reflectance characteristics is very weak, there are two absorption vales at 500 and 640 rim; when the content reaches a certain content (about 5%), the reflectance characteristics of black soil parent matrix are totally covered by organic matter, and there is only one large absorption vale in the region caused by organic matter. (2) The spectral characteristic controlling points of black soil hyperspectral reflectance in the range of 450-930 om are located at 450, 500, 590, 660 and 930 nrn, and divide the black soil reflectance into four parts. (3) Simulation models (linear, quadratic) rightly describe the characteristics of black soil hyperspectral reflectance, and the linear piecewise model shows a better performance. (4) The organic matter content prediction models with the coefficients of reflectance simulation models as independent variables are more precise than that based on soil reflectance and its derivate, which indicates that the characteristic controlling points for reflectance simulation models are selected reasonably and representatively, and the simulation models partly solve the data redundancy problem of soil hyperspectral reflectance, and improve the precision of black soil organic matter content prediction models with remote sensing methods. Reflectance simulating method can be used for data simplification and compression, data redundancy removal, organic matter and other soil pararneters remote sensing studies.

[Comparison of methods for estimating soybean leaf area index].

Leaf area index (LAI) is an important biophysical parameter, and is the critical variable in many ecology models, productivity models and carbon circulation study. Based on the field experiment data, an evaluation of soybean LAI retrieval methods was conducted using NDVI (normalized difference vegetation index) and RVI (ratio vegetation index), principle component analysis (PCA) and neural network (NN) methods, and the estimate effects of three methods were compared. The results showed that the three methods have an ideal effect on the LAI estimation. R2 of validated model of vegetation indices, PCA, NN were 0.753 (NDVI), 0.758 (RVI), 0.883, 0.899. PCA and NN methods were better with higher precision, and PCA method was the best, as its RMSE (0.202) was slower than the two vegetation indices (RMSEs of NDVI and RVI were 0.594 and 0.616) and NN (RMSE was 0.413) method. While the LAI was small, vegetation indices were obvious for removing the noise from soil and atmospheric effect and obtained the good evaluation result. PCA showed better effect for all LAI. LAI affected the estimating result of NN method moderately. As for the NN method, modeled LAI value and measured LAI regression formula slope was the nearest to 1 with R2 of 0.949, which showed a great potential for LAI estimating. As a whole, PCA and NN methods were the prior selection for LAI estimation, which should be attributed to the application of hyperspectral information of many bands.