Tuberculosis of acromioclavicular joint: a case report

Abstract Background Osteoarticular tuberculosis is a great masquerader presenting in varied forms and in atypical locations, and it is prone to misdiagnosis and missed diagnosis. Isolated acromioclavicular joint tuberculosis has been reported rarely. Case presentation A 19-year-old man presented with a chronic, mild pain, non-healing ulcer in right shoulder. Imaging of the shoulder revealed destruction of the acromioclavicular joint and histopathology confirmed the diagnosis of acromioclavicular tuberculosis. The patient underwent debridement, synovectomy and drainage of the abscess and recovered well with antitubercular therapy postoperatively. Conclusions Awareness of this uncommon presentation of osteoarticular tuberculosis may assist in earlier diagnosis. Especially, in endemic countries, osteoarticular tuberculosis should be considered as a differential diagnosis in all atypical presentations to avoid residual problems

Enhancing Winter Wheat Soil–Plant Analysis Development Value Prediction through Evaluating Unmanned Aerial Vehicle Flight Altitudes, Predictor Variable Combinations, and Machine Learning Algorithms

Monitoring winter wheat Soil–Plant Analysis Development (SPAD) values using Unmanned Aerial Vehicles (UAVs) is an effective and non-destructive method. However, predicting SPAD values during the booting stage is less accurate than other growth stages. Existing research on UAV-based SPAD value prediction has mainly focused on low-altitude flights of 10–30 m, neglecting the potential benefits of higher-altitude flights. The study evaluates predictions of winter wheat SPAD values during the booting stage using Vegetation Indices (VIs) from UAV images at five different altitudes (i.e., 20, 40, 60, 80, 100, and 120 m, respectively, using a DJI P4-Multispectral UAV as an example, with a resolution from 1.06 to 6.35 cm/pixel). Additionally, we compare the predictive performance using various predictor variables (VIs, Texture Indices (TIs), Discrete Wavelet Transform (DWT)) individually and in combination. Four machine learning algorithms (Ridge, Random Forest, Support Vector Regression, and Back Propagation Neural Network) are employed. The results demonstrate a comparable prediction performance between using UAV images at 120 m (with a resolution of 6.35 cm/pixel) and using the images at 20 m (with a resolution of 1.06 cm/pixel). This finding significantly improves the efficiency of UAV monitoring since flying UAVs at higher altitudes results in greater coverage, thus reducing the time needed for scouting when using the same heading overlap and side overlap rates. The overall trend in prediction accuracy is as follows: VIs + TIs + DWT > VIs + TIs > VIs + DWT > TIs + DWT > TIs > VIs > DWT. The VIs + TIs + DWT set obtains frequency information (DWT), compensating for the limitations of the VIs + TIs set. This study enhances the effectiveness of using UAVs in agricultural research and practices

Effects of Soil Types and Irrigation Modes on Rice Root Morphophysiological Traits and Grain Quality

Soil moisture plays an important role in rice (Oryza sativa L.) root development and grain quality. However, little is known about the effects of soil type on rice root morphophysiological traits (RMTs) and grain quality under different irrigation modes. A soil-grown experiment was conducted during the 2016–2017 rice growing seasons in Yangzhou city with three soil types, namely, clay soil, loamy soil, and sandy soil, and three irrigation regimes, namely, conventional irrigation (CI, 0 kPa), alternate wetting and moderate drying (AWMD, −15 kPa), and alternate wetting and severe drying (AWSD, −25 kPa). The AWMD regime improved the RMT by 3.05–48.95% when compared with the CI and AWSD regimes, and the RMTs in loamy were 7.38–93.67% higher than those in clay and sandy soil under AWMD across 2016 and 2017. The AWMD regime improved the rice milling quality and appearance quality both in clay and loamy soil by 2.88–10.08% and 15.43–45.77%, respectively. The CI regime improved the processing quality and nutritional quality of rice in sandy soil. Both loamy and clay soils improved the rice RMTs and grain quality under an AWMD regime. The RMTs were very significantly correlated with water use efficiency, rice milling, and cooking quality and were negatively correlated with rice appearance quality. The AWMD regime can affect the rice RMT and can improve the rice grain quality in loamy soil. Our results provide a theoretical basis for the design of water-saving rice irrigation regimes and for an improvement in rice grain quality in the process of rice cultivation

The Role of the Ascorbic Acid–Glutathione Cycle in Young Wheat Ears’ Response to Spring Freezing Stress

Freezing stress in spring often causes the death and abnormal development of young ears of wheat, leading to a significant reduction in grain production. However, the mechanisms of young wheat ears responding to freezing are largely unclear. In this study, the role of the ascorbic acid–glutathione cycle (AsA–GSH cycle) in alleviating freezing-caused oxidative damage in young wheat ears at the anther connective tissue formation phase (ACFP) was investigated. The results showed that the release rate of reactive oxygen species (ROS) and the relative electrolyte conductivity in young ears of Jimai22 (JM22, freezing-tolerant) were significantly lower than those in young ears of Xumai33 (XM33, freezing-sensitive) under freezing. The level of the GSH pool (231.8~392.3 μg/g FW) was strikingly higher than that of the AsA pool (98.86~123.4 μg/g FW) in young wheat ears at the ACFP. Freezing significantly increased the level of the AsA pool and the activities of ascorbate peroxidase (APX) and monodehydroascorbate reductase (MDHAR) in the young ears of both varieties. The level of the GSH pool increased in the young ears of XM33 under freezing but decreased in the young ears of JM22. The young ears of JM22 showed higher activities of glutathione reductase (GR), glutathione-S-transferase (GST) and glutathione peroxidase (GPX) than the young ears of XM33 under freezing. Collectively, these results suggest that the AsA–GSH cycle plays a positive role in alleviating freezing-induced oxidative damage in young wheat ears. Furthermore, the ability of utilizing GSH as a substrate to scavenge ROS is an important factor affecting the freezing tolerance of young wheat ears. In addition, abscisic acid (ABA), salicylic acid (SA), 3-indolebutyric acid (IBA) and cis-zeatin (cZ) may be involved in regulating the AsA–GSH cycle metabolism in young wheat ears under freezing

Combining Controlled-Release Urea and Normal Urea to Improve the Yield, Nitrogen Use Efficiency, and Grain Quality of Single Season Late <i>japonica</i> Rice

Controlled-release urea (CRU) is widely adopted to improve yields and nitrogen use efficiencies (NUEs) in rice. However, there are few studies on the effects of the mixed application of CRU and normal urea (at different N ratios) on rice yield, nitrogen efficiency, and grain quality. A series of simplified fertilization modes (SFMs) were set up in 2018–2019. CRU with release periods of 80 days and 120 days were mixed with urea at N ratios of 7:3, 6:4, 5:5, 4:6, and 3:7 and applied during the rice-growing season. We determined the rice yield, dry matter accumulation, NUEs, and grain quality. The yields of SFM_80_6/4 (CRU with release periods of 80 days were mixed with urea at N ratios of 6:4) and SFM_120_5/5 (CRU with release periods of 120 days were mixed with urea at N ratios of 5:5) were 3.69% and 4.39% higher than that of fractionated urea (FU), respectively, across 2018 and 2019. Combining the application of controlled-release urea and normal urea improved the dry matter accumulation, nitrogen accumulation, and nitrogen uptake rate when compared with FU. SFMs improved the processing quality and appearance quality of rice grains and did not reduce the cooking and eating quality. SFM_80_6/4 and SFM_120_5/5 are a one-time fertilization mode with high yield, high efficiency, and good grain quality, which is worthy of further promotion and application

Excessive Nitrogen Application Leads to Lower Rice Yield and Grain Quality by Inhibiting the Grain Filling of Inferior Grains

Nitrogen fertilizer is an important agronomic measure to regulate rice yield and grain quality. Grain filling is crucial for the formation of rice yield and grain quality. However, there are few studies on the effects of excessive nitrogen application (ENA) on grain filling rate and grain quality. A two-year field experiment was conducted to reveal the difference in grain filling characteristics and grain quality of superior grains (SG) and inferior grains (IG), as well as their responses to nitrogen fertilizer. We determined the grain appearance, the rice yield, the grain filling characteristics of SG and IG, and grain quality. We found that with the increasing nitrogen application level, grain yield of both varieties first increased and then decreased. The average yield of excessive nitrogen application (345 kg N ha−1) was 2.68–6.31% lower than that of appropriate nitrogen application (270 kg N ha−1). ENA reduced the grain filling rate by 12.7–25.8%, and the grain filling rate of SG was higher than that of IG. Increasing nitrogen application increased the processing quality and appearance quality of rice grain, but ENA deteriorated the appearance quality, eating quality and nutritional quality. The amylose content and taste value of SS were 3.1–9.7% and 7.1–20.2% higher than those of IS, respectively. The protein components of SG were lower than those of IG. Taken together, our results revealed that ENA leads to the lowering of rice grain yield and grain quality by suppressed grain filling of inferior grains

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening

The Starch Physicochemical Properties between Superior and Inferior Grains of Japonica Rice under Panicle Nitrogen Fertilizer Determine the Difference in Eating Quality

Nitrogen fertilizer is essential for rice growth and development, and topdressing nitrogen fertilizer at panicle stage has a huge impact on rice grain quality. However, the effect of panicle nitrogen fertilizer (PNF) on starch physicochemical properties and fine structure remain unclear. In this study, four PNF levels (0, 60, 120, 180 kg N ha&minus;1) were grown with the same basal and tiller fertilizer (150 kg N ha&minus;1). The starch physicochemical properties, fine structure, texture properties and eating quality of two japonica rice were determined. We found that the content of total protein, crude fat and amylose between superior and inferior grains were significantly different. Compared with inferior grains, superior grains had low relative crystallinity, good pasting characteristics and outstanding eating quality. With the increase of nitrogen application rates, the starch volume mean diameter was lower; the average chain length of amylopectin was longer; and the relative crystallinity of starch was higher. The changes above in starch structure resulted in an increase in starch solubility, swelling power and gelatinization enthalpy, and led to a decrease in retrogradation enthalpy, retrogradation percentage and pasting viscosity, consequently contributing to the increase in hardness and stickiness of rice and the deterioration of taste value. These results indicated that topdressing PNF lengthened the amylopectin chain, decreased starch granule size, enhanced crystallization stability and increased gelatinization enthalpy, which were the direct reasons for the deterioration of cooking and eating quality

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening