BACKGROUND: Although Sanger sequencing remains widely used in human genetic disease diagnosis and livestock breeding, software packages for analyzing such data have seen little innovation over time. Determining the genotypes of tens to hundreds of loci across hundreds or thousands of samples still typically relies on manual visual confirmation with traditional software, a process that is both time-consuming and prone to error. RESULTS: We present SAGPEK, a tool that automatically identifies genotypes at target loci from hundreds to thousands of ABI-format Sanger sequencing files and directly outputs the results. SAGPEK extracts the signal intensities for A, G, C, and T bases, performs base calling, and determines each site's homozygous or heterozygous status. It then generates a primary sequence composed of the bases with the highest signal intensities and records secondary bases for heterozygous sites. Using either built-in or user-provided anchor sequences, SAGPEK maps the coordinates of target loci, reports their genotypes, and, when applicable, annotates the corresponding amino acid changes. CONCLUSIONS: SAGPEK provides an efficient, flexible, and user-friendly solution for analyzing ABI-format Sanger sequencing data, enabling simultaneous genotyping of tens of loci across hundreds of samples. Its innovation lies not in introducing new base-calling methods, but in integrating versatile functionalities-batch genotyping, customizable anchor sequences, amino acid alteration reporting, chromatogram visualization, and local execution-into a single open-source package. This makes SAGPEK well suited for applications such as human genetic disease screening, drug-resistance mutation detection, and functional mutation identification in livestock and other organisms.

To obtain fused images with rich information, visible and infrared images are combined. Most current fusion techniques provide decent results. However, they have shortcomings in extracting information of the source images. This limitation prevents the fused images from adequately considering thermal radiation regions and texture details. As a result, the detailed texture information of the source visible image in the final fusion image is much more than the thermal target information of the source infrared image, or vice versa. Since features at a single scale fail to adequately capture the spatial details of complex scenes, a multi-scale attention network is used to extract the deep feature information of source images. For latent variable issues, the Expectation Maximization (EM) technique can yield maximum likelihood estimates. This not only stabilizes the training of the Generative Adversarial Network (GAN) but also aids in addressing the issue of labels lacking in the fusion of visible and infrared images. Although the EM algorithm framework can greatly enhance the training stability of GAN models, the improvement in fusion quality is not large. Therefore, a diffusion model is introduced into the generator to capture the potential joint structure information between infrared and visible images. Massive experiments show that Dif-GAN outperforms the state-of-the-art.

Knowledge building emphasizes allowing students to participate in the collaborative knowledge building process of proposing and improving ideas, providing a new way of teaching and learning for schools. In order to investigate the main contents and ideas of knowledge building research in Taiwan over the past two decades, this study selected 37 English literature from web of science, ACM, and IEEE databases. Through the use of content analysis and visualization analysis, an overview of the researcher, high-frequency keywords, research subjects, sources of experimental data, experimental auxiliary platforms, and analysis methods are presented. Finally, we drew the following three enlightenments: the influence of collaborative learning, students' personality and participation roles on students' collaborative knowledge building, and the deep integration of emerging technologies and classroom teaching are proposed, in order to provide directions for the theoretical research and practical exploration of later knowledge building.

In the face of the COVID-19 epidemic, “classes suspended but learning continues” has made online learning a must-have option for students. In order to avoid too many difficulties in students' online learning process and improve students' participation, we built a learning design mode in the context of large-scale online teaching, and combined massive realtime live teaching with personalized asynchronous learning. We combine existing online teaching cases with personalized teaching that provides timely intervention to build a learning design mode and provide detailed teaching strategies for front-line teachers. The teaching practice data show that this learning mode can improve students' knowledge mastery ability, cultivate students' practical operation ability, and significantly improve students' learning efficiency. Finally, it reflects on the functional value of learning mode from three aspects: students' cognitive development, students' behavior development and teaching structure reform, which can provide some experience and follow by other teachers and teaching organizers.

Collaborative knowledge construction is a process in which individuals assume cognitive responsibilities, conduct collective reflection, exchange, and improve community views, thereby creating new knowledge. Its development has received attention from the education field. This paper uses meta-analysis to quantify 16 studies on the learning effect of collaborative knowledge construction in the past two decades. It is found that collaborative knowledge construction (1) has a great positive impact on teacher education; (2) has a medium impact on students' learning, and has different levels of influence on students at different stages; (3) has a medium impact on teaching and learning, which helps to reshape educational concepts. In the future, collaborative knowledge construction should focus on the cultivation of normal students and the development of children's ability, and set up a trinity of family, school and society.

Integrating different perspectives in the generation of knowledge is essential for understanding learning problems. And crowdsourcing provides new approaches to harvest distributed knowledge. However, students are more inclined to connect with people with similar characteristics, and the identity of a group has an impact on cooperative learning performance. Therefore, there is an attempt to integrate the homogeneous students in crowdsourcing and to explore the influence of student homophily knowledge generation framework through teaching experiments for more meaningful and effective participation in the process of knowledge generation. The results demonstrate that homophily can improve students' academic performance, and the knowledge generation framework in crowdsourcing has a positive effect on innovation generation.

With the continuous advancement of education reform, collaborative learning has become an important way of learning. The occurrence of in-depth collaborative learning is inseparable from the effective regulation of learning. However, few studies focus on the co-regulation between peers. Based on this, the article explores the general conditions for the occurrence of co-regulation from the perspective of social cognitive theory and uses the crowdsourcing knowledge construction environment as the inducing condition. It aims to explore the correlation between co-regulated strategies and the effect of deep learning. The results show that crowdsourcing knowledge construction can effectively promote the occurrence of co-regulation. There is a clear and positive correlation between co-regulated strategies and knowledge construction behaviors, especially between reflective strategies and in-depth knowledge construction behaviors. Finally, the article provides three suggestions on how to promote the occurrence of co-regulation.

Based on the whole-brain model, the paper uses GSEQ software to analyze the specific sequences of learner behaviors with the lag sequence analysis method. Then, analyzes and explains its transformation paths and differences. There are four types of learners, such as logical learners, organizational learners, communicative learners, and utopian learners. Among them, the sequence of the first two is a network structure, and the latter two is a linear structure. Finally, this paper puts forward some relevant suggestions on the design, path and resource recommendation of online learning platform, to promote the further development of personalized learning.

This paper considers the problem of recovering multidimensional array, in particular third-order tensor, from a random subset of its arbitrarily corrupted entries. Our study is based on a recently proposed algebraic framework in which the tensor-SVD is introduced to capture the low-tubal-rank structure in tensor. We analyze the performance of a convex program, which minimizes a weighted combination of the tensor nuclear norm, a convex surrogate for the tensor tubal rank, and the tensor l1 norm. We prove that under certain incoherence conditions, this program can recover the tensor exactly with overwhelming probability, provided that its tubal rank is not too large and that the corruptions are reasonably sparse. The number of required observations is order optimal (up to a logarithm factor) when comparing with the degrees of freedom of the low-tubal-rank tensor. Numerical experiments verify our theoretical results and real-world applications demonstrate the effectiveness of our algorithm.