Search Results (151)

Background: Buffaloes are considered an indispensable genetic resource for dairy production. However, improvements in lactation performance have been relatively limited. Advances in sequencing technology, combined with genome-wide association studies, have facilitated the breeding of high-quality buffalo. Methods: We conducted an integrated analysis of genomic sequencing data from 120 water buffalo, the high-quality water buffalo genome assembly designated as UOA_WB_1, and milk production traits, including 305-day milk yield (MY), peak milk yield (PM), total protein yield (PY), protein percentage (PP), fat percentage (FP), and total milk fat yield (FY). Results: The results identified 56 significant SNPs, and based on these markers, 54 candidate genes were selected. These candidate genes were significantly enriched in lactation-related pathways, such as the cAMP signaling pathway (ABCC4), TGF-β signaling pathway (LEFTY2), Wnt signaling pathway (CAMK2D), and metabolic pathways (DGAT1). Conclusions: These candidate genes (e.g., ABCC4, LEFTY2, CAMK2D, DGAT1) provide a substantial theoretical foundation for molecular breeding to enhance milk production in buffaloes. Full article

Soil moisture is a key factor affecting the exchange of heat and water between the land and the atmosphere. Land data assimilation (LDA) methods that leverage the strengths of both models and observations can generate more accurate initial conditions. However, soil moisture exhibits significant spatial heterogeneity, implying strong local characteristics for both observational and background errors. To elucidate the impact of error localization on LDA, we constructed a land data assimilation system (LDAS) suitable for the Common Land Model (CoLM), based on the simplified extended Kalman filter (SEKF) method. Through practical assimilation experiments using soil moisture retrieval products from the Soil Moisture Active Passive (SMAP) and Fenyun-3D (FY3D) satellites, we investigated the influence of spatial static and dynamic observational and background errors on LDA. The results indicate that by incorporating dynamic errors that account for the spatial heterogeneity of soil, LDAS can adaptively absorb observational information, thereby significantly enhancing assimilation impact and subsequent model forecast accuracy. Compared to experiments applying static errors, dynamic errors increased the spatial correlation coefficients by 17.4% and reduced the root mean square error (RMSE) by 11.2%. The results clearly demonstrate that for soil variable assimilation studies with strong spatial heterogeneity, progressively refined dynamic error estimation is a crucial direction for improving land surface assimilation performance. Full article

Islatravir (ISL) is a novel antiretroviral that inhibits HIV-1 reverse transcriptase translocation. The M184V mutation, known to reduce ISL’s viral susceptibility in vitro, could arise from prolonged exposure to nucleoside reverse transcriptase inhibitors (NRTI) (3TC). This study evaluated the predictive efficacy of ISL and identified potentially active antiretrovirals in combination among treatment-experienced patients in Cameroon, where NRTIs (3TC) have been the backbone of ART for decades now. Although ISL is a long-acting antiretroviral, it will provide other therapeutic options in combination with other reverse transcriptase inhibitors that remain effective. We analyzed 1170 HIV-1 sequences from patients failing first-, second-, and third-line ART using the CIRCB Antiviral Resistance Evaluation (CIRCB-CARE) database. Drug resistance mutations (DRMs) were interpreted using Stanford HIVdb.v9, and covariation patterns between M184V and major NRTI/NNRTI DRMs were assessed. The study population, with a median age of 40 years, showed a high prevalence of resistance to NRTIs (77.4%) and NNRTIs (49.2%). The most frequent NRTI DRMs were M184V/I (83.3%), M41L (25.0%), and T215FY (36.8%), while common NNRTI DRMs included K103NS (53.3%), Y181CIV (27.7%), and G190ASE (22.2%). In first-line ART failure, M184V significantly covaried with K70R, L74I, and M41L for NRTIs and K103N and G190A for NNRTIs. In second-line failure, the covariation with M184V extended to T215Y, M41L, and D67N for NRTIs and G190A, K103N, and K103S for NNRTIs. No significant covariation with M184V was observed in third-line treatment failures. Based on these covariations and on the effect of these mutations on available anti-HIV drugs, TDF (partial efficacy) and Doravirine (fully active) were identified as potentially suitable candidates in combination with ISL among patients failing the first, second, and third lines, and could serve as a valuable therapeutic option in LMICs facing similar treatment challenges. Full article

The Microwave Temperature Sounder-3 (MWTS-3) onboard the Chinese FengYun-3E (FY-3E) satellite is the third generation of Chinese microwave temperature sounder. Based on MWTS-2, the number of MWTS-3 channels has been increased from 13 to 17, which can observe the atmospheric temperature and water vapor profiles from the surface to the lower stratosphere. In this study, two generations of MWTSs onboard FY-3D/3E were inter-calibrated by the Double Difference (DD) method to eliminate bias. The results showed that the biases of tropospheric channels were stable (within 1 K) and the biases of stratospheric channels were relatively large (over 2 K). In addition, the weighting functions of all MWTS channels varied with fields of view (FOVs) due to different optical paths, causing the brightness temperature (TB) observations to display strong scan-dependent features, i.e., the limb effect. This work used a limb correction method to remove scan-dependent patterns so that the underlying weather signals could be uncovered. After inter-calibration and limb correction, this work converted the TB observations from MWTS-2/3 onto a global gridded dataset at 0.5° × 0.5° latitudinal and longitudinal resolutions using a method of nested interpolation. Based on this research, more long-term FengYun series satellite climate datasets can be established in the future. Full article

Utilizing crushed spontaneous combustion coal gangue as a coarse aggregate in concrete preparation effectively reduces reliance on natural resources and mitigates environmental pollution; however, the suboptimal workability of spontaneous combustion coal gangue coarse aggregate concrete (SCG-CAC) limits its engineering applications. To address this issue, this study places SCGCAC at the center of a CFDST (Concrete-Filled Double-Skin Steel Tubular) stub column. Through finite element modeling validated for reliability, this study examines the structural mechanical response to axial loading, along with the effects of various parameters. The analysis encompasses parameters such as the strength of the core SCGCAC (f_c,i), the strength of the sandwiched concrete (f_c,o), the yield strength of the outer steel tube (f_y,o), the yield strength of the inner steel tube (f_y,i), the width-to-thickness ratio (B/t_o), the diameter-to-thickness ratio of the inner tube (D/t_i), and the diameter-to-width ratio of the outer tube (D/B). Results show that this structural configuration significantly enhances the core SCGCAC ultimate bearing capacity, and increases in D/t_i, f_c,i, f_c,o, f_y,i, and B/t_o all lead to an increase in the peak load. Particularly, when D/t_i increases from 28.57 to 80, the peak load increases by 42.72%. However, changes in f_y,o and D/B have no significant effect on the peak load. Full article

This study evaluated the reliability of satellite-derived atmospheric temperature and humidity profiles derived from occultations of Fengyun-3D (FY-3D), the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), the Meteorological Operational Satellite program (METOP), and the microwave observations of NOAA Polar Orbital Environmental Satellites (POES) using various conventional sounding datasets from 2020 to 2021. Satellite-derived profiles were also used to explore the precursors of severe convective precipitations in terms of the atmospheric boundary layer (ABL) characteristics and convective parameters. It was found that the satellite-derived temperature profiles exhibited high accuracy, with RMSEs from 0.75 K to 2.68 K, generally increasing with the latitude and decreasing with the altitude. Among these satellite-derived profile sources, the COSMIC-2-derived temperature profiles showed the highest accuracy in the middle- and low-latitude regions, while the METOP series had the best performance in high-latitude regions. Comparatively, the satellite-derived relative humidity profiles had lower accuracy, with RMSEs from 13.72% to 24.73%, basically increasing with latitude. The METOP-derived humidity profiles were overall the most reliable among the different data sources. The ABL temperature and humidity structures from these satellite-derived profiles showed different characteristics between severe precipitation and non-precipitation regions and could reflect the evolution of ABL characteristics during a severe convective precipitation event. Furthermore, some convective parameters calculated from the satellite-derived profiles showed significant and rapid changes before the severe precipitation, indicating the feasibility of using satellite-derived temperature and humidity profiles as precursors to severe convective precipitation. Full article

Islatravir (ISL) is the first-in-class nucleoside reverse transcriptase translocation inhibitor (NRTtI) with novel modes of action. Data on ISL resistance are currently limited, particularly to HIV-1 non-B subtypes. This study aimed to assess prevalent nucleos(t)ide reverse transcriptase inhibitor (NRTI)-resistant mutations in HIV-1 subtype C for their phenotypic resistance to ISL. Prevalent single and combinations of NRTI-resistant mutations were selected from a routine HIV-1 genotypic drug resistance testing database and introduced into HIV-1 subtype C-like pseudoviruses, which were then tested for ISL susceptibility. Single NRTI-resistant mutations were susceptible or showed only a low level of resistance to ISL. This included thymidine analogue mutations (TAMs, i.e., M41L, D67N, K70R, T215FY, and K219EQ) and non-TAMs (i.e., A62V, K65R, K70ET, L74IV, A114S, Y115F, and M184V). Combinations of M184V with one or more additional NRTI-resistant mutations generally displayed reduced ISL susceptibilities. This was more prominent for combinations that included M184V+TAMs, and particularly M184V+TAM-2 mutations. Combinations that included M184V+K65R did not impact significantly on ISL susceptibility. Our study suggests that ISL would be effective in treating people living with HIV (PLWH) failing tenofovir disoproxil fumarate (TDF)/lamivudine (3TC) or TDF/emtricitabine (FTC)-containing regimens, but would be less effective in PLH failing zidovudine (AZT) with 3TC or FTC-containing regimens. Full article

Root and butt rot, caused by Heterobasidion species, poses a significant threat to coniferous forests in the Northern Hemisphere. Innovative and effective strategies are crucial to enhance the control of this disease. This study aimed at identifying a Streptomyces strain, FY4, and evaluating its biocontrol potential against H. annosum and H. parviporum. Strain FY4 was identified as Streptomyces blastmyceticus based on morphological, physiological, and biochemical characteristics, supported by a multigene phylogenetic analysis using the 16S rRNA, atpD, rpoB, and trpB genes. In vitro dual-culture experiments showed that S. blastmyceticus exhibited antagonistic activity against both H. annosum and H. parviporum, with an inhibition zone diameter exceeding 15 mm. Moreover, the fermentation broth of S. blastmyceticus FY4 displayed significant inhibitory effects on the mycelial growth and spore germination of both Heterobasidion species. At a 10% concentration, the fermentation broth inhibited the mycelial growth by over 90% and reduced the spore germination rate by more than 60%. Additionally, the fermentation broth exhibited significant inhibitory effects on the mycelial growth of four common pathogenic fungi—Phytophthora cinnamomi, P. sojae, Rhizoctonia solani, and Verticillium dahlia, with an inhibition rate over 50%. These findings suggest that S. blastmyceticus FY4 produces antifungal substances capable of effectively suppressing infection of Heterobasidion species in conifers. Consequently, strain FY4 holds great promise as a biological control agent for managing root and butt rot caused by these pathogens, as well as potential for controlling other fungal diseases. Full article

In this study, we utilized ECMWF Reanalysis of the Global Climate at Atmospheric Resolution 5 (ERA5) data, FengYun-4B satellite (FY-4B) data, a Wind3D 6000 Three-Dimensional Scanning Laser Wind Radar, and raindrop spectrum data to analyze the circulation background, convective cloud changes, boundary layer wind field variations, and precipitation drop size spectrum characteristics of a severe convective rainfall process that occurred on 3 April 2024 in the eastern margin of the Tibetan Plateau. The findings indicated the following: (1) The rain belt of this precipitation event showed a southwest–northeast trend. During the vigorous development of convection, the rainfall intensity and total precipitation at the station increased, with a wider raindrop spectrum, and the raindrop spectrum of this precipitation process was unimodal. (2) On 3 April, the interaction between the eastward movement of the plateau trough at 500 hPa and the upper-level jet stream at 200 hPa in the eastern Tibetan Plateau and the Sichuan Basin area, along with the necessary conditions for precipitation, such as energy and moisture, led to severe convective rainfall. (3) This intense convective precipitation process was caused by the vigorous convective clouds that developed in the eastern part of the Tibetan Plateau. As these clouds developed and moved eastward out of the plateau, they precipitated with increased turbulence intensity at the station, leading to the generation of intense convective activities at the site. (4) One hour before the precipitation, there were significant increases in horizontal wind speed, vertical air velocity, and turbulence intensity within the boundary layer, and there were also significant changes in the horizontal wind direction. The results obtained can provide important theoretical references for the prediction of severe convective rainfall and the performance of numerical simulations thereon. Full article

The spatial resolution (250–1000 m) of the FY-3D MERSI is too coarse for agricultural monitoring at the farmland scale (20–30 m). To achieve the winter wheat yield (WWY) at the farmland scale, based on FY-3D, a method framework is developed in this work. The enhanced deep convolutional spatiotemporal fusion network (EDCSTFN) was used to perform a spatiotemporal fusion on the 10 day interval FY-3D and Sentinel-2 vegetation indices (VIs), which were compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). In addition, a BP neural network was built to calculate the farmland-scale WWY based on the fused VIs, and the Aqua MODIS gross primary productivity product was used as ancillary data for WWY estimation. The results reveal that both the EDCSTFN and ESTARFM achieve satisfactory precision in the fusion of the Sentinel-2 and FY-3D VIs; however, when the period of spatiotemporal data fusion is relatively long, the EDCSTFN can achieve greater precision than ESTARFM. Finally, the WWY estimation results based on the fused VIs show remarkable correlations with the WWY data at the county scale and provide abundant spatial distribution details about the WWY, displaying great potential for accurate farmland-scale WWY estimations based on reconstructed fine-spatial-temporal-resolution FY-3D data. Full article

Hyperspectral infrared atmospheric sounding data, characterized by their high vertical resolution, play a crucial role in capturing three-dimensional atmospheric spatial information. The hyperspectral infrared atmospheric detectors HIRAS/HIRAS-II, mounted on the FY3D/EF satellite, have established an initial global coverage network for atmospheric sounding. The collaborative observation approach involving multiple satellites will improve both the coverage and responsiveness of data acquisition, thereby enhancing the overall quality and reliability of the data. In response to the increasing number of channels, the rapid growth of data volume, and the specific requirements of multi-satellite joint observation applications with infrared hyperspectral sounding data, this paper introduces an efficient storage and indexing method for infrared hyperspectral sounding data within a distributed architecture for the first time. The proposed approach, built on the Kubernetes cloud platform, utilizes the Google S2 discrete grid spatial indexing algorithm to establish a grid-based hierarchical model for unified metadata-embedded documents. Additionally, it optimizes the rowkey design using the BPDS model, thereby enabling the distributed storage of data in HBase. The experimental results demonstrate that the query efficiency of the Google S2 grid-based embedded document model is superior to that of the traditional flat model, achieving a query time that is only 35.6% of the latter for a dataset of 5 million records. Additionally, this method exhibits better data distribution characteristics within the global grid compared to the H3 algorithm. Leveraging the BPDS model, the HBase distributed storage system adeptly balances the node load and counteracts the detrimental effects caused by the accumulation of time-series remote sensing images. This architecture significantly enhances both storage and query efficiency, thus laying a robust foundation for forthcoming distributed computing. Full article

Fractional vegetation cover (FVC) is a crucial indicator for measuring the growth of surface vegetation. The changes and predictions of FVC significantly impact biodiversity conservation, ecosystem health and stability, and climate change response and prediction. Southwest China (SWC) is characterized by complex topography, diverse climate types, and rich vegetation types. This study first analyzed the spatiotemporal variation of FVC at various timescales in SWC from 2000 to 2020 using FVC values derived from pixel dichotomy model. Next, we constructed four machine learning models—light gradient boosting machine (LightGBM), support vector regression (SVR), k-nearest neighbor (KNN), and ridge regression (RR)—along with a weighted average heterogeneous ensemble model (WAHEM) to predict growing-season FVC in SWC from 2000 to 2023. Finally, the performance of the different ML models was comprehensively evaluated using tenfold cross-validation and multiple performance metrics. The results indicated that the overall FVC in SWC predominantly increased from 2000 to 2020. Over the 21 years, the FVC spatial distribution in SWC generally showed a high east and low west pattern, with extremely low FVC in the western plateau of Tibet and higher FVC in parts of eastern Sichuan, Chongqing, Guizhou, and Yunnan. The determination coefficient R² scores from tenfold cross-validation for the four ML models indicated that LightGBM had the strongest predictive ability whereas RR had the weakest. WAHEM and LightGBM models performed the best overall in the training, validation, and test sets, with RR performing the worst. The predicted spatial change trends were consistent with the MODIS-MOD13A3-FVC and FY3D-MERSI-FVC, although the predicted FVC values were slightly higher but closer to the MODIS-MOD13A3-FVC. The feature importance scores from the LightGBM model indicated that digital elevation model (DEM) had the most significant influence on FVC among the six input features. In contrast, soil surface water retention capacity (SSWRC) was the most influential climate factor. The results of this study provided valuable insights and references for monitoring and predicting the vegetation cover in regions with complex topography, diverse climate types, and rich vegetation. Additionally, they offered guidance for selecting remote sensing products for vegetation cover and optimizing different ML models. Full article

The leaf area index (LAI) is a key indicator of vegetation canopy structure and growth status, crucial for global ecological environment research. The Moderate Resolution Spectral Imager-II (MERSI-II) aboard Fengyun-3D (FY-3D) covers the globe twice daily, providing a reliable data source for large-scale and high-frequency LAI estimation. VI-based LAI estimation is effective, but species and growth status impacts on the sensitivity of the VI–LAI relationship are rarely considered, especially for MERSI-II. This study analyzed the VI–LAI relationship for eight biomes in China with contrasting leaf structures and canopy architectures. The LAI was estimated by adaptively combining multiple VIs and validated using MODIS, GLASS, and ground measurements. Results show that (1) species and growth stages significantly affect VI–LAI sensitivity. For example, the EVI is optimal for broadleaf crops in winter, while the RDVI is best for evergreen needleleaf forests in summer. (2) Combining vegetation indices can significantly optimize sensitivity. The accuracy of multi-VI-based LAI retrieval is notably higher than using a single VI for the entire year. (3) MERSI-II shows good spatial–temporal consistency with MODIS and GLASS and is more sensitive to vegetation growth fluctuation. Direct validation with ground-truth data also demonstrates that the uncertainty of retrievals is acceptable (R² = 0.808, RMSE = 0.642). Full article

Mastitis (MAS), endometritis (MET), and ketosis (KET) are prevalent diseases in dairy cows that result in substantial economic losses for the dairy farming industry. This study gathered 26,014 records of the health and sickness of dairy cows and 99,102 data of reproduction from 13 Holstein dairy farms in Central China; the milk protein and milk fat content from 56,640 milk samples, as well as the pedigree data of 37,836 dairy cows were obtained. The logistic regression method was used to analyze the variations in the prevalence rates of MAS, MET, and KET among various parities; the mixed linear model was used to examine the effects of the three diseases on milk production, milk quality, and reproductive traits. DMU software (version 5.2) utilized the DMUAI module in conjunction with the single-trait and two-trait animal model, as well as best linear unbiased prediction (BLUP), to estimate the genetic parameters for the three diseases, milk production, milk quality, and reproductive traits in dairy cows. The primary findings of the investigation comprised the following: (1) The prevalence rates of MAS, MET, and KET in dairy farms were 20.04%, 10.68%, and 7.33%, respectively. (2) MAS and MET had a substantial impact (p < 0.01) on milk production, resulting in significant decreases of 112 kg and 372 kg in 305-d Milk Yield (305-d MY), 4 kg and 12 kg in 305-d Protein Yield (305-d PY), and 6 kg and 16 kg in 305-d Fat Yield (305-d FY). As a result of their excessive 305-d MY, some cows were diagnosed with KET due to glucose metabolism disorder. The 305-d MY of cows with KET was significantly higher than that of healthy cows (205 kg, p < 0.01). (3) All three diseases resulted in an increase in the Interval from Calving to First Service (CTFS, 0.60–1.50 d), Interval from First Service to Conception (FSTC, 0.20–16.20 d), Calving Interval (CI, 4.00–7.00 d), and Number of Services (NUMS, 0.07–0.35). (4) The heritabilities of cows with MAS, MET, and KET were found to be low, with values of 0.09, 0.01, and 0.02, respectively. The genetic correlation between these traits ranged from 0.14 to 0.44. This study offers valuable insights on the prevention and control of the three diseases, as well as feeding management and genetic breeding. Full article

This study investigates the impacts of the joint assimilation of ground-based microwave radiometer (MWR) and FY-3D microwave sounder (MWTS-2/MWHS-2) observations on the analyses and forecasts for precipitation forecast. Based on the weather research and forecasting data assimilation (WRFDA) system, four experiments are conducted in this study, concerning a heavy precipitation event in Beijing on 2 July 2021, and 10-day batch experiments were also conducted. The key study findings include the following: (1) Both ground-based microwave radiometer and MWTS-2/MWHS-2 data contribute to improvements in the initial fields of the model, leading to appropriate adjustments in the thermal structure of the model. (2) The forecast fields of the experiments assimilating ground-based microwave radiometer and MWTS-2/MWHS-2 data show temperature and humidity performances closer to the true fields compared with the control experiment. (3) Separate assimilation of two types of microwave radiometer data can improve precipitation forecasts, while joint assimilation provides the most accurate forecasts among all the experiments. In the single-case, compared with the control experiment, the individual and combined assimilation of MWR and MWTS-2/MWHS-2 improves the six-hour cumulative precipitation threat score (TS) at the 25 mm level by 57.1%, 28.9%, and 38.2%, respectively. The combined assimilation also improves the scores at the 50 mm level by 54.4%, whereas individual assimilations show a decrease in performance. In the batch experiments, the MWR_FY experiment’s TS of 24 h precipitation forecast improves 28.5% at 10 mm and 330% at 25 mm based on the CTRL. Full article