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Objective: To comprehensively analyze the ADRs associated with Denosumab (Prolia) in the treatment of osteoporosis using data from the FAERS database, and gain a better understanding of the potential risks and side effects of Denosumab (Prolia) therapy. Methods: Data of Denosumab (Prolia) were collected from the FAERS database covering the period from first quarter of 2010 to the third quarter of 2023. Disproportionality analysis was performed by calculating the reporting odds ratios (ROR), proportional reporting ratio (PRR), and Bayesian analysis confidence propagation neural network (BCPNN) to detect positive signals. Results: Totally, 17,985,365 reports were collected from the FAERS database, 1,97,807 reports of Denosumab (Prolia) were identified as the "primary suspected (PS)" ADRs. Denosumab (Prolia) induced ADRs occurred in 27 organ systems. 38 significant disproportionality PTs satisfying with the three algorithms were retained at the same time. Unexpected significant ADRs such as bone density abnormal and immobile also occur. The majority of the ADRs occurred within the first 30 days after Denosumab (Prolia) initiation. Conclusion: Based on the American FAERS database, the high frequency ADRs of Denosumab (Prolia) were hypocalcaemia, bone density abnormal, eczema, rebound effect, spinal deformity, etc. Clinical use of this drug should focus on this part of ADRs. Attention should also be paid to newly discovered ADRs, such as immobile, menopausal symptoms, etc., to avoid more serious consequences. Cohort studies, more detailed and comprehensive case information, and longterm clinical investigations are needed to confirm these results and to further understand the safety profile of Denosumab (Prolia). [ABSTRACT FROM AUTHOR]

The ability of wheat to adapt to a wide range of environmental conditions is determined mostly by allelic diversity among genes regulating vernalization requirement. Vrn-1 is a major regulator of this requirement. In this study, two novel alleles of Vrn-A1 were discovered in Chinese cultivars: vrn-A1n was identified in two landraces, Jiunong 2 and Ganchun 16, and Vrn-A1o was detected in Duanhongmangmai. Both novel alleles showed a linked duplication in the promoter region. The common copy of these two alleles was identical to the recessive allele vrn-A1. Compared with the recessive allele vrn-A1, the other copy of vrn-A1n contained a 54-bp deletion in the promoter region and the distinct copy of Vrn-A1o contained an 11-bp deletion in the promoter region. In segregating populations in the greenhouse under nonvernalizing (20–25° C) and long-day (16 h light) conditions, plants with the novel vrn-A1n allele did not head earlier than those with the recessive vrn-A1 allele. However, plants that were either homozygous or heterozygous for the novel Vrn-A1o allele headed earlier than those with the recessive vrn-A1 allele. To identify the novel allele with the small-sized product and facilitate screening, a DNA marker for the novel dominant allele Vrn-A1o was designed. Analysis of the novel-allele distribution showed that two cultivars carrying the vrn-A1n allele were dispersed in the northwestern spring wheat zone, and 12 cultivars carrying the dominant Vrn-A1o allele were widely distributed in the northwestern spring wheat zone, Xinjiang winter and spring wheat zone, Yellow and Huai River valley winter wheat zone, and QinghaiTibetan Plateau spring and winter wheat zone. Our study identifies useful germplasm and a DNA marker for wheat breeding. [ABSTRACT FROM AUTHOR]

The low accuracy of satellite cloud fraction (CF) data over the Arctic seriously restricts the accurate assessment of the regional and global radiative energy balance under a changing climate. Previous studies have reported that no individual satellite CF product could satisfy the needs of accuracy and spatio-temporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties can provide an effective way to produce a spatiotemporally complete CF data record with higher accuracy. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and the Bayesian maximum entropy (BME) method to produce a synthetic 1°×1° CF dataset in the Arctic during 2000-2020. The CDF matching was employed to remove the systematic biases among multiple passive sensor datasets through the constraint of using CF from an active sensor. The BME method was employed to combine adjusted satellite CF products to produce a spatiotemporally complete and accurate CF product. The advantages of the presented fusing framework are that it not only uses the spatiotemporal autocorrelations but also explicitly incorporates the uncertainties of passive sensor products benchmarked with reference data, i.e., active sensor product and ground-based observations. The inconsistencies of Arctic CF between passive sensor products and the reference data were reduced by about 10-20% after fusing. Compared with ground-based observations, R2 increased by about 0.20-0.48 and the root mean square error (RMSE) and bias reductions averaged about 6.09% and 4.04% for land regions, respectively; these metrics for ocean regions were about 0.05-0.31, 2.85%, and 3.15%, respectively. Compared with active sensor data, R2 increased by nearly 0.16, and RMSE and bias declined by about 3.77% and 4.31%, respectively, in land; meanwhile, improvements in ocean regions were about 0.3 for R2, 4.46% for RMSE and, 3.92% for bias. The comparison with the ERA5 reanalysis and CMIP6 CF datasets shows that the proposed fusion algorithm effectively corrected the CF data with differences greater than 30%. Moreover, the fused product effectively supplements the temporal gaps of AVHRR36 based products caused by satellite faults and the data missing from MODIS-based products prior to the launch of Aqua, and extends the temporal range better than the active product; it addresses the spatial insufficiency of the active sensor data and the AVHRR-based products acquired at latitudes greater than 82.5°N. A continuous monthly 1-degree CF product covering the entire Arctic during 2000-2020 was generated and is freely available to the public at https://doi.org/10.5281/zenodo. 7624605 (Liu et al., 2022). This is of great importance for reducing the uncertainty in the estimation of surface radiation parameters and thus helps researchers to better understand the earth's energy imbalance. [ABSTRACT FROM AUTHOR]

Land surface reflectance (LSR) and aerosol types are the two main factors that affect aerosol inversions over land. According to LSR determination methods, Moderate resolution Imaging Spectroradiometer (MODIS) aerosol products are produced using the Deep Blue (DB) and Dark Target (DT) algorithms. Five aerosol types that are determined from Aerosol Robotic Network (AERONET) ground measurements are used to describe the global distribution of aerosol types in each algorithm. To assess the influence of LSR and the method used to determine aerosol type from aerosol retrievals, 10-km global aerosol products that cover 2013 are selected for validation using Level 2.0 aerosol observations from 175 AERONET sites. The variations in the retrieval accuracy of the DB and DT algorithms for different LSR values are analyzed by combining them with a global 10-km LSR database. Meanwhile, the adaptability of the MODIS products over areas covered with different aerosols is also explored. The results are as follows. (1) Compared with DT retrievals, the DB algorithm yields lower root mean squared error (RMSE) and mean absolut error (MAE) values, and a greater number of appropriate sample points fall within the expected error (EE). The DB algorithm shows higher overall reliability; (2) The aerosol retrieval accuracy of the DB and DT algorithms decline irregularly as the surface reflectance increases; the DB algorithm displays relatively high accuracy; (3) Both algorithms have a high retrieval accuracy over areas covered by weak absorbing aerosols, whereas dust aerosols and continental aerosols produce a low retrieval accuracy. The DB algorithm shows good retrieval results for most aerosols, but a lower accuracy for strong absorbing aerosols. [ABSTRACT FROM AUTHOR]

The advanced very high resolution radiometer (AVHRR) data from the National Oceanic and Atmospheric Administration satellite is one of the earliest data applied in aerosol research. The dense dark vegetation (DDV) algorithm is a popular method for the present land aerosol retrieval. One of the most crucial steps in the DDV algorithm with AVHRR data is estimating the land surface reflectance (LSR). However, LSR cannot be easily estimated because of the lack of a 2.13 µm band. In this article, the moderate resolution imaging spectroradiometer (MODIS) vegetation index product (MYD13) is introduced to support the estimation of AVHRR LSR. The relationship between MODIS NDVI and the AVHRR LSR of the visible band is analysed to retrieve aerosol optical depth (AOD) from AVHRR data. Retrieval experiments are carried out in mid-eastern America. The AOD data from AErosol RObotic NETwork (AERONET) measurements are used to evaluate the aerosol retrieval from AVHRR data, the results indicate that about 74% of the retrieved AOD are within the expected error range of ±(0.05+0.2), and a cross comparison of the AOD retrieval results with the MODIS aerosol product (MYD04) shows that the AOD datasets have a similar spatial distribution. [ABSTRACT FROM AUTHOR]