Dynamic insights into research trends and trajectories in early reading: an analytical exploration via dynamic topic modeling.

Introduction: Early reading has gained significant attention in the academic community. With the increasing volume of literature on this subject, it has become crucial to assess the current research landscape and identify emerging trends. Methods: This study utilized the dynamic topic model to analyze a corpus of 1,638 articles obtained from the Web of Science Core Collection to furnish a lucid understanding of the prevailing research and forecast possible future directions. Results: Our in-depth assessment discerned 11 cardinal topics, among which notable ones were interventions' impacts on early reading competencies; foundational elements of early reading: phonological awareness, letters, and, spelling; and early literacy proficiencies in children with autism spectrum disorder. Although most topics have received consistent research attention, there has been a marked increase in some topics' popularity, such as foundational elements of early reading and early literary proficiencies in children with autism spectrum disorder. Conversely, other topics exhibited a downturn. Discussion: This analytical endeavor has yielded indispensable insights for scholars, decision-makers, and field practitioners, steering them toward pivotal research interrogatives, focal interest zones, and prospective research avenues. As per our extensive survey, this paper is a pioneering holistic purview of the seminal areas of early reading that highlights expected scholarly directions.

AC-UNet: an improved UNet-based method for stem and leaf segmentation in Betula luminifera.

Plant phenotypic traits play an important role in understanding plant growth dynamics and complex genetic traits. In phenotyping, the segmentation of plant organs, such as leaves and stems, helps in automatically monitoring growth and improving screening efficiency for large-scale genetic breeding. In this paper, we propose an AC-UNet stem and leaf segmentation algorithm based on an improved UNet. This algorithm aims to address the issues of feature edge information loss and sample breakage in the segmentation of plant organs, specifically in Betula luminifera. The method replaces the backbone feature extraction network of UNet with VGG16 to reduce the redundancy of network information. It adds a multi-scale mechanism in the splicing part, an optimized hollow space pyramid pooling module, and a cross-attention mechanism in the expanding network part at the output end to obtain deeper feature information. Additionally, Dice_Boundary is introduced as a loss function in the back-end of the algorithm to circumvent the sample distribution imbalance problem. The PSPNet model achieves mIoU of 58.76%, mPA of 73.24%, and Precision of 66.90%, the DeepLabV3 model achieves mIoU of 82.13%, mPA of 91.47%, and Precision of 87.73%, on the data set. The traditional UNet model achieves mIoU of 84.45%, mPA of 91.11%, and Precision of 90.63%, and the Swin-UNet model achieves . The mIoU is 79.02%, mPA is 85.99%, and Precision is 88.73%. The AC-UNet proposed in this article achieved excellent performance on the Swin-UNet dataset, with mIoU, mPA, and Precision of 87.50%, 92.71%, and 93.69% respectively, which are better than the selected PSPNet, DeepLabV3, traditional UNet, and Swin-UNet. Commonly used semantic segmentation algorithms. Experiments show that the algorithm in this paper can not only achieve efficient segmentation of the stem and leaves of Betula luminifera but also outperforms the existing state-of-the-art algorithms in terms of both speed. This can provide more accurate auxiliary support for the subsequent acquisition of plant phenotypic traits.

The anode is more beneficial to the advanced treatment of wastewater containing antibiotics by three-dimensional electro-biofilm reactor: Degradation, mechanism and optimization.

The three-dimensional electrode biological aerated filter (3DE-BAF) has the potential to overcome inherent limitations of conventional electrochemical and biofilm methods. Electrochemical means could enhance the performance and sustainability of biofilm technologies and stimulate the spread of new applications in (waste) water treatment. This paper describes the construction and performance of 3DE-BAF in the treatment of simulated wastewater represented by tetracycline (TC). This is followed by a discussion of electrode performance, the electron transport mechanism and the electrode's effect on the biological community of 3D-EBAF. Given the gap between experimental studies and practical applications, the enlarged anode 3DE-BAF named 3DEAE-BAF reactor was applied with good results to duck farm wastewater. This study could provide guidance as to developing new methods to construct a highly stable 3DE-BAF. The paper concludes that improved 3DE-BAF technology is promising for advanced treatment of livestock wastewater containing antibiotics.

[Effect of B Lymphocyte-Induced Mature Protein-1 Expression in Bone Marrow Mononuclear Cells on Prognosis of Patients with Multiple Myeloma].

OBJECTIVE: To study the effect of B lymphocyte-induced mature protein-1(Blimp1) expression in bone marrow mononuclear cells on the prognosis of patients with multiple myeloma. METHODS: Forty-eight patients with multiple myeloma from January 2014 to January 2015 were selected. The expression of Blimp1 in the bone marrow of all the patients was measured. According to the median score of Blimp1 expression level, the patients was divided into low expression group (L group, 22 cases) and high expression group (H group, 26 cases). The related influencing factors of different Blimp1 expression levels, the clinical efficacy, immunophenotypic changes and progression-free survival(PFS) were compared between different Blimp1 expression groups. RESULTS: There were no significant differences in sex, age, type, stage and treatment stage between the 2 groups (P> 0.05). The total remission rate of the low expression group was significantly higher than that in the high expression group (P<0.05). However before treatment, there was no significant difference in the positive rate of CD19, CD38, CD56, CD138 and minimal lesion between the 2 groups (P> 0.05); after treatment the positive expression rates of CD38, CD56, CD138 and minimal lesion in the low expression group were significantly lower than those in the high expression group. While the positive expression rate of CD19 was significantly higher than that in high expression group (P<0.05). The PFS of 1, 2 and 3 years in the low expression group was significantly higher than that in the high expression group (P<0.05). CONCLUSION: The MM patients with the high in staging and the larger in diffeculty of treatment possess high Blimp1 expression, however, the therapeutic efficacy and prognosis of MM patients with low Blimp1 expression are significantly better than those of MM patients with high Blimp1 expression.