Search

Abstract Relapse remains the main cause of treatment failure in patients with myeloid malignancies even after allogeneic hematopoietic stem cell transplantation (allo‐HSCT). We observed a particularly low incidence of relapse in patients prepared with fludarabine, busulfan and melphalan in our previous study and this multicenter retrospective analysis aimed to confirm the feasibility of the regimen and to identify the potential prognostic factors. This study was performed using registry data from adults patients with myeloid malignancies who underwent their first allo‐HSCT following fludarabine(≥100 mg/m2), busulfan (≥3.2 mg/kg) and melphalan (≥100 mg/m2) based conditioning at nine transplantation centers in China between Jan. 2020 and Mar. 2022. A total of 221 consecutive patients (AML n = 171, MDS‐IB‐1 or 2 n = 44, CMML n = 6) with median age of 46 were enrolled in this study. The median follow‐up was 507 days for survivors. The 2‐year NRM, CIR, OS and DFS were 10.6% ± 2.2%, 14.8% ± 3.3%, 79.4% ± 3.7% and 74.6% ± 3.7%, respectively. In multivariate analyses, high HCT‐CI (≥3) was the only independent factor for higher NRM [hazard ratio (HR), 2.96; 95% confidence interval (CI), 1.11 to 7.90; p = 0.030] and ECOG score ≥2 was the only independent factor for inferior OS (HR, 2.43; 95%CI, 1.15 to 5.16; p = 0.020) and DFS (HR, 2.12; 95%CI, 1.13 to 4.02; p = 0.020). AML diagnosis and positive measurable residual disease (MRD) at transplantation were predictors for higher CIR (HR = 7.92, 95%CI 1.05‐60.03, p = 0.045; HR = 3.64, 95%CI 1.40‐9.44, p = 0.008; respectively), while post‐transplantation cyclophosphamide based graft‐versus‐host disease prophylaxis was associated with lower CIR (HR = 0.24 95%CI 0.11‐0.54, p = 0.001). The intensity of conditioning regimen did not impact CIR, NRM, DFS and OS. These results supported that double alkylating agents of busulfan and melphalan based conditioning regimens were associated with low relapse rate and acceptable NRM in adult patients with myeloid malignancies. The optimal dose remained to be confirmed by further prospective studies.

Imaging hyperspectral technology is becoming popular in agriculture to provide detailed information on crop growth. In this work, we propose an estimation of rapeseed pod’s water content model and identification of maturity levels (green, yellow, and full) model by using this technology. Four types of hyperspectral features are extracted—color, texture, spectral three-edge parameters, and spectral indices. By integrating these features, satisfactory results are achieved: the optimal feature combination is from spectral indices and three-edge parameters, with low RRMSE and RE for yellow maturity. Incorporating spectral indices significantly improved the pod’s water content estimation, reducing RRMSE by up to 43.30% and 30.11% in the green and full maturity stages. Random forest and support vector machine with kernel method (SVM-KM) algorithms outperformed other statistical models, with SVM-KM achieving up to 96.90% accuracy in identifying maturity levels. These findings provide valuable insights for managing rapeseed production during the pod stage.

Background: IVS-II-5 G>C (HBB: c.315+5 G>C) is a rare β-thalassemia mutation. However, there is no clear evidence regarding the effect of this defect or co-inheritance of other β-thalassemia mutations on phenotypes. Methods: The clinical phenotypes associated with compound heterozygosity for the IVS-II-5 G>C mutation with other β-thalassemia mutations, together with the potential effect of the genetic modifiers α-thalassemia were studied in 13 patients. Analyses of red cell indices, hemoglobin component, iron status, and α-globin genes were carried out in 19 heterozygotes. Results: Next-generation sequencing of 24 undiagnosed patients with thalassemia major (TM) or thalassemia intermedia (TI) identified 13 carriers of the IVS-II-5 G>C mutation. There was a wide spectrum of phenotypic severity in compound heterozygotes and 6 (46.2%) of 13 were transfusion dependent. Analysis of 19 heterozygotes indicated that most were hematologically normal without appreciable microcytosis or hypochromia, and approximately half had normal hemoglobin A2 levels at the same time. Conclusion: Compound heterozygotes for IVS-II-5 G>C and other severe β-thalassemia mutations are phenotypically severe enough to necessitate appropriate therapy and counseling. Co-inheritance of this nucleotide substitution with other β-thalassemia mutations may account for a considerable portion of the incidence of undiagnosed patients with TI and TM in Guangxi. The IVS-II-5 G>C mutation can pose serious difficulties in screening and counseling. Keywords: β-thalassemia; IVS-II-5 G>C; genotype; phenotype

As an important oil crop, rapeseed contributes to the food security of the world. In recent years, agronomists have cultivated many new varieties, which has increased human nutritional needs. Variety recognition is of great importance for yield improvement and quality breeding. In view of the low efficiency and damage of traditional methods, in this paper, we develop a noninvasive model for the recognition of rapeseed varieties based on hyperspectral feature fusion. Three types of hyperspectral image features, namely, the multifractal feature, color characteristics, and trilateral parameters, are fused together to identify 11 rapeseed species. An optimal feature is selected using a simple rule, and then the three kinds of features are fused. The support vector machine kernel method is employed as a classifier. The average recognition rate reaches 96.35% and 93.71% for distinguishing two species and 11 species, respectively. The abundance test model demonstrates that our model possesses robustness. The high recognition rate is almost independent of the number of modeling samples and classifiers. This result can provide some practical experience and method guidance for the rapid recognition of rapeseed varieties.

In order to detect the oleic acid content of rapeseed quickly and accurately, we propose, in this paper, an artificial BP neural networks based model for predicting oleic acid content by using rapeseed’s hyperspectral information. Four types of spectral features are selected for our investigation, namely multifractal index, sensitive band, trilateral parameters, and spectral index. Both univariate variable and multiple variables are considered as our model input. The result shows that the combined feature has higher precision and better stability than when using a single parameter. An interesting finding shows that the combined feature involving multifractal parameters can significantly improve the model performance. Taking the combined feature {MF-h(0), SB-DR574, SPI-NDSI(R575, R576)} as the model input, the constructed BP (back propagation) neural networks model has the highest precision, with the coefficient of determination (R2) 0.8753, root mean square error (RMSE) 1.0301, and relative error (RE) 1.047%. This result provides some experience for the rapid detection of rapeseed’s oleic acid content.

AVNDVI (Accumulative Visible Normalized Difference Vegetation Index), a new type of derivative parameters of NDVI, was set up by improving the computational formulas and importing the spectral information of visible bands after analyzing the construction idea of NDVI and its derivative parameters. Then, the characteristic values of VNDVI (Visible NDVI) were calculated by applying a combinational method of sensitive bands of visible bands. The study carried out the fitting analysis between NDVI, VNDVI, AVNDVI, and LAI (Leaf Area Index). Several conclusions are obtained according to data analysis. Firstly, all of the determination coefficients between NDVI, VNDVI, AVNDVI, and LAI of rapeseed can reach or exceed 0.83. The distribution of their RMSE values ranges from 0.4 to 0.5 and absolute values of RE vary from 0.9% to 2.1%. Secondly, the inversion sensitivity SV of VNDVI and LAI ranges from 0.7 to 1.9 relative to NDVI, and the inversion sensitivity SA of AVNDVI decreases in varying degrees with the promotion of capacity of resisting disturbance accordingly. Its value varies from 0.1 to 0.9. Thirdly, the values of SA remain stable between 0.1 and 0.3 with the increase of NDVI. Applying the inversion model of AVNDVI will be a considerable scheme when faced with a complex environment and many interfering factors.

At present, the application of hyperspectral image technology in image target detection is lacking black-and-white correction, and the average spectral reflectance cannot be calculated, which leads to large error in image feature detection and classification. In this study, hyperspectral image technology was applied to the detection of rapeseed storage quality, and germination detection was completed during the storage of rapeseed. The black-and-white board correction to the hyperspectral data was completed and the spectral characteristic curve of the rapeseed sample hyperspectral image was obtained. The average spectral reflectance is calculated, the threshold of hyperspectral image is estimated, and the correlation technique is used to denoise the hyperspectral image. Based on this, the edge feature of the rapeseed hyperspectral image is recognized, and the feature classification of the hyperspectral rapeseed image is realized by combining the gray co-occurrence matrix. The experimental results show that the proposed method can detect the germination of rapeseed with high precision under the application of hyperspectral image technology. This study provides a reliable basis for the application of hyperspectral image technology. [ABSTRACT FROM AUTHOR]

Spectrum technology has been widely used in crop non-destructive testing diagnosis for crop information acquisition. Since spectrum covers a wide range of bands, it is of critical importance to extract the sensitive bands. In this paper, we propose a methodology to extract the sensitive spectrum bands of rapeseed using multiscale multifractal detrended fluctuation analysis. Our obtained sensitive bands are relatively robust in the range of 534 nm-574 nm. Further, by using the multifractal parameter (Hurst exponent) of the extracted sensitive bands, we propose a prediction model to forecast the Soil and plant analyzer development values ((SPAD), often used as a parameter to indicate the chlorophyll content) and an identification model to distinguish the different planting patterns. Three vegetation indices (VIs) based on previous work are used for comparison. Three evaluation indicators, namely, the root mean square error, the correlation coefficient, and the relative error employed in the SPAD values prediction model all demonstrate that our Hurst exponent has the best performance. Four rapeseed compound planting factors, namely, seeding method, planting density, fertilizer type, and weed control method are considered in the identification model. The Youden indices calculated by the random decision forest method and the K-nearest neighbor method show that our Hurst exponent is superior to other three Vis, and their combination for the factor of seeding method. In addition, there is no significant difference among the five features for other three planting factors. This interesting finding suggests that the transplanting and the direct seeding would make a big difference in the growth of rapeseed. [ABSTRACT FROM AUTHOR]

Background: IVS-II-5 G>C (HBB: c.315+5 G>C) is a rare β-thalassemia mutation. However, there is no clear evidence regarding the effect of this defect or co-inheritance of other β-thalassemia mutations on phenotypes. Methods: The clinical phenotypes associated with compound heterozygosity for the IVS-II-5 G>C mutation and other β-thalassemia mutations, together with the genetic modifiers' potential effect of the genetic modifiers α-thalassemia, were studied in 13 patients. In addition, analyses of red cell indices, hemoglobin component, iron status, and α-globin genes were carried out in 19 heterozygotes. Results: Next-generation sequencing of 24 undiagnosed patients with transfusion-dependent thalassemia (TDT) or non-transfusion-dependent thalassemia (NTDT) identified 13 carriers of the IVS-II-5 G>C mutation. There was a wide spectrum of phenotypic severity in compound heterozygotes and 6 (46.2%) of 13 were transfusion dependent. Analysis of 19 heterozygotes indicated that most were hematologically normal without appreciable microcytosis or hypochromia, and approximately half had normal hemoglobin A2 levels at the same time. Conclusion: Compound heterozygotes for IVS-II-5 G>C and other severe β-thalassemia mutations are phenotypically severe enough to necessitate appropriate therapy and counselling. Coinheritance of this nucleotide substitution with other β-thalassemia mutations may account for a considerable portion of the incidence of undiagnosed patients with NTDT and TDT in Guangxi. Therefore, the IVS-II-5 G>C mutation can pose serious difficulties in screening and counselling. [ABSTRACT FROM AUTHOR]

The inconsistency of chlorophyll content in rapeseed directly affects the quality of seed, so it is necessary to establish a rapid and nondestructive detection technology of chlorophyll in mature rapeseed. In this paper, the content of chlorophyll in rapeseed samples was determined by ultraviolet-visible spectrophotometry, and the near-infrared spectrum data were collected. The partial least squares regression model of chlorophyll content in rapeseed was established based on hyperspectral technology through first derivative and standard normal variable transformation pretreatment. The results of the cross-test showed that the determination coefficient of the validation set of the prediction model of chlorophyll content in rapeseed was greater than 0.85, and the root mean square error (RMSE) of the cross-test was less than 0.3 mg/kg. The results showed that the method could accurately predict the chlorophyll content in rapeseed and provide important technical support for rapid monitoring of seed quality. [ABSTRACT FROM AUTHOR]

In the past fewyears, multi-core processors incorporating four, six, eight, or more cores on a single die have become ubiquitous. Those cores, having their own private caches, often share a higher level cache memory, which leads to compete among different tasks. This can seriously affect the average performance of multi-core systems as the probability of cache hit could be lowered. In realizing this, we study the problem of scheduling bag-of-tasks (BoT) applications with shared cache constraint on multi-core systems. We first use cache space isolation techniques to divide shared caches into partitions. Then, we give a motivational example and outline the shared cache aware scheduling problem of multi-core systems. Finally, to provide an optimum solution for this problem, we propose a heuristic shared cache contention aware scheduling (SCAS) algorithm on multi-core systems. Our extensive simulation performance evaluation study clearly demonstrate that our proposed SCAS algorithm outperforms the existing traditional scheduling algorithm Min-min and the modified algorithm MSCAS in terms of schedule length and average response time. [ABSTRACT FROM AUTHOR]