miR-22 represses osteoblast viability with ESR1 presenting a direct target and indirectly inactivating p38 MAPK/JNK signaling.

BACKGROUND: MicroRNAs (miRs) hold critical implications in the modulation of osteogenesis. This work was designed to unravel the underlying regulatory mechanism of miR-22 during osteoblast differentiation. METHODS: Synthetic miR-22 mimics or inhibitors were transfected into preosteoblast MC3T3-E1 cells to regulate miR-22 expression. MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] and flow cytometry analyses were employed to assess cell proliferation and apoptosis. A quantitative real-time polymerase chain reaction and western blot assays were applied to measure mRNA and protein expression. Alkaline phosphatase activity and alizarin red staining were tested to further analyze cell differentiation. In silico analysis and luciferase reporter assays were utilized to identify the direct binding between miR-22 and its potential target. RESULTS: MTT and flow cytometry analyses showed that miR-22 repressed MC3T3-E1 cell viability and promoted cell apoptosis. By detecting osteogenic-specific molecule expression, alkaline phosphatase activity and alizarin red staining, miR-22 was observed to suppress osteogenic differentiation of MC3T3-E1 cells. In silico analysis and luciferase reporter assays confirmed that ESR1 is a direct target gene of miR-22. In addition, miR-22 expression affected the phosphorylation of p38 mitogen-activated protein kinase and Jun N-terminal kinase expression in MC3T3-E1 cells. CONCLUSIONS: The findings of the present study highlight the functional significance of miR-22 in osteoblast differentiation and suggest its role as a possible therapeutic target in metabolic bone disorders.

Tanshinol alleviates impaired bone formation by inhibiting adipogenesis via KLF15/PPARγ2 signaling in GIO rats.

Glucocorticoid (GC)-induced osteoporosis (GIO) is characterized by impaired bone formation, which can be alleviated by tanshinol, an aqueous polyphenol isolated from Salvia miltiorrhiza Bunge. In this study we investigated the molecular mechanisms underlying GC-induced modulation of osteogenesis as well as the possibility of using tanshinol to interfere with GIO. Female SD rats aged 4 months were orally administered distilled water (Con), prednisone (GC, 5 mg·kg-1·d-1), GC plus tanshinol (Tan, 16 mg·kg-1·d-1) or GC plus resveratrol (Res, 5 mg·kg-1·d-1) for 14 weeks. After the rats were sacrificed, samples of bone tissues were collected. The changes in bone formation were assessed using Micro-CT, histomorphometry, and biomechanical assays. Expression of Kruppel-like factor 15 (KLF15), peroxisome proliferator-activated receptor γ 2 (PPARγ 2) and other signaling proteins in skeletal tissue was measured with Western blotting and quantitative RT-PCR. GC treatment markedly increased the expression of KLF15, PPARγ2, C/EBPα and aP2, which were related to adipogenesis, upregulated FoxO3a pathway proteins (FoxO3a and Gadd45a), and suppressed the canonical Wnt signaling (ß-catenin and Axin2), which was required for osteogenesis. Thus, GC significantly decreased bone mass and bone quality. Co-treatment with Tan or Res effectively counteracted GC-impaired bone formation, suppressed GC-induced adipogenesis, and restored abnormal expression of the signaling molecules in GIO rats. We conclude that tanshinol counteracts GC-decreased bone formation by inhibiting marrow adiposity via the KLF15/PPARγ2/FoxO3a/Wnt pathway.

[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.

[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.

Influence of stone load on the outcome of same-session flexible ureteroscopy for bilateral upper urinary tract stones: a multicenter retrospective study.

Purpose: This study aimed to evaluate the efficacy and safety of same-session flexible ureteroscopy (fURS) for the treatment of bilateral upper urinary tract stones and to examine the influence of stone load on the outcome of same-session fURS, stratifying by total diameter of stones (TDS) ≤30 mm vs. >30 mm. Patients and methods: We retrospectively reviewed all cases of same-session fURS performed for bilateral upper urinary tract stones at four institutions between January 2017 and September 2020. All patients were divided into two groups based on TDS, ≤30 mm and >30 mm. Data on patient demographics, stone characteristics, surgical results, and complications were collected and analyzed for differences between the two groups. Stone-free rate (SFR) was defined as patients endoscopically stone-free or with radiological fragments <2 mm of each renal unit. Results: A total of 121 patients with bilateral upper urinary tract stones underwent same-session fURS, consisting of 73 patients in the TDS ≤ 30 mm group and 48 patients in the TDS > 30 mm group. The mean bilateral stone size was 28.2 ± 12.2 mm (range: 9.1-38.4 mm), with a mean operating time of 97.1 ± 39.6 min (range: 19-220 min). The SFR was 54.5% after the first fURS, and SFR increased to 97.5% after re-fURS for residual stones. The operation time for the TDS > 30 mm group was longer than that of the TDS ≤ 30 mm group (85.1 ± 36.5 vs. 115.4 ± 37.4 min, p < 0.001). The SFR after the first fURS was significantly lower in the TDS > 30 mm group than in the TDS ≤ 30 mm group (25.0% vs. 73.9%, p < 0.001). Although there was no statistically significant difference in overall SFR between the two groups (93.7% vs. 100%, p = 0.060), the rate of re-fURS for residual stones was higher in the TDS > 30 mm group than in the TDS ≤ 30 mm group (75% vs. 26%, p < 0.001). There were no significant differences in length of hospital stay (LOS) (2.2 ± 0.7 vs. 2.3 ± 1.0, p = 0.329) or complication rate (10.9% vs. 14.6%, p = 0.582) between the two groups. Conclusion: The results suggested that same-session fURS can be effectively performed with a low complication rate. A higher SFR after the first fURS can be achieved in the case of bilateral upper urinary tract stones with TDS ≤ 30 mm, and priority should be given to same-session fURS.

[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.