Explore the vast range of titles available.

Breast cancer is the second leading cause of mortality among female cancer patients worldwide. Early detection of breast cancer is considerd as one of the most effective ways to prevent the disease from spreading and enable human can make correct decision on the next process. Automatic diagnostic methods were frequently used to conduct breast cancer diagnoses in order to increase the accuracy and speed of detection. The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection. The FID3-AF algorithm is a hybridisation of the fuzzy system, the iterative dichotomizer 3 (ID3) algorithm, and the association function. The fuzzy-neural dynamicbottleneck-detection (FUZZYDBD) is considered as an automatic fuzzy database definition method, would aid in the development of the fuzzy database for the data fuzzification process in FID3-AF. The FID3-AF overcame ID3’s issue of being unable to handle continuous data. The association function is implemented to minimise overfitting and enhance generalisation ability. The results indicated that FID3-AF is robust in breast cancer classification. A thorough comparison of FID3-AF to numerous existing methods was conducted to validate the proposed method’s competency. This study established that the FID3-AF performed well and outperform other methods in breast cancer classification.

Proper understanding of rain yield along with the relevance factors and their extent of relation in the yield of rain is very important to maintain a smooth life style in every one’s life. The definitive classification of mean temperature and heaviest rainy days depends on the weather changes that occur seasonwise during any year. In reality, visualizing the effects of climatic changes such as temperature in the rain-yield during over a period of years is very difficult and there is no method or tool to help us in this aspect. Formal concept analysis (FCA) which is a mathematical model that expresses the relationship between various features and entities in terms of pairs called concepts. These concepts are hierarchically related to form a unique concept lattice which is a diagrammatical view of the information available. In this paper, an approach to facilitate the understanding of temperature-rain data of Vellore district with the use of data collected for the recent 15 years period is presented. For the analysis, the mean temperature data is preprocessed seasonwise over the years. In a similar manner the rainy days also preprocessed seasonwise over the years. We illustrate the process of extracting meaningful information from the data with the use of FCA. In this process an ID3 algorithm based method is employed to identify more important features from the context. These important features are used to compress the concepts obtained from FCA and thereby reduce meaningful information. The efficiency of the proposed method is validated using few efficient metrics available in literature.

Our previous study demonstrated that lymphoid enhancer-binding factor 1 (LEF1) could promote the progression of esophageal squamous cell carcinoma (ESCC). However, the regulatory mechanism of LEF1 was not clear thoroughly. Herein, we continued to explore the downstream mechanism of LEF1 in ESCC. In this study, we applied western blotting, quantitative real-time polymerase chain reaction (qRT-PCR), immunohistochemistry, RNA-Seq analysis, a luciferase reporter assay, chromatin immunoprecipitation (ChIP), bioinformatics analysis, and a series of functional assays and . The results demonstrated that LEF1 regulated directly the expression of Id3. Id3 was highly expressed in ESCC tissues and correlated with histologic differentiation (p=0.011), pT stage (p<0.01) and AJCC stage (p<0.01) in ESCC patients. Moreover, Id3 could serve as a prognostic factor of ESCC. By various functional experiments, overexpression of Id3 promoted the proliferation, migration, invasion, EMT, and tumorgenicity. Mechanistically, Id3 could regulate ERK/MAPK signaling pathway via activating HRAS to perform its biological function. Furthermore, activating ERK/MAPK signaling pathway promoted the expression of Id3 gene in turn, indicating that a positive regulatory loop between Id3 and ERK/MAPK pathway may exist in ESCC. In summary, LEF1/Id3/HRAS axis could promote the tumorigenesis and progression of ESCC via activating ERK/MAPK signaling pathway. Targeting this cascade may provide a valid antitumor strategy to delay ESCC progress.

With the comprehensive construction of quality education, physical training is an important way to cultivate students’ physical quality, and its related construction is gradually receiving comprehensive attention and support. This paper discusses the intelligent generation scheme of sports training plans, which aims to meet the individual needs of students through algorithms while developing the best training plan for them. This paper first introduces the overall architecture of the sports training program generation system. Secondly, the association rule method is utilized to mine the sports training data, and after elaborating the concept of association rules, the FP-growth algorithm is proposed to carry out the data mining work with the FP tree at the very beginning as the core. Then, a decision tree model based on the ID3 algorithm is constructed to correctly classify the training program based on the attributes selected at each level of nodes in order to obtain the attributes with maximum information gain. An empirical analysis of students from all grades in a school showed that there is a correlation between the various sports training programs of male and female students. After using the sports training program generation system designed on this basis, the boys’ 50-meter run performance (P<0.01), boys’ standing long jump performance (P<0.05), and girls’ 50-meter run performance (P<0.05) were significantly improved.

This study examines the hybrid online-offline teaching of civics in colleges and universities, with a particular emphasis on the analysis of the BOPPPS teaching model. The study also evaluates and analyzes the civics application under the BOPPS teaching model using the ID3 decision tree algorithm and the information gain method as attribute selection criteria. By integrating BOPPS teaching and online and offline hybrid Civics teaching, an online, hybrid teaching model platform based on BOPPPS is constructed. The teaching impact of the BOPPS online and offline hybrid Civics education was confirmed when combined with the empirical data. According to the findings, 66% of students anticipate learning about civics online, and 77% anticipate learning about civics both online and offline. The standard deviation for traditional instruction is 5.7819; for “online resources + offline classroom teaching,” it is 5.832; for BOPPPS teaching, it is 8.3988; and for BOPPPS teaching with online and offline integration, it is 6.0921. This study is helpful for fostering innovation and growth in both offline and online education at colleges and institutions. Hybrid civics education’s evolution and innovation.

Cultural patterns, through the process of socialization, determine the national identity of a nation and influence the way of thinking, value orientation, and behavior of individuals. In this paper, we analyze Japanese values education from the dimensions of “group”, “interpersonal”, and “self”. The information mining algorithm based on web logs is used to establish a correlation matrix of Japanese learners’ values, and the values of Japanese learners are clustered according to the similarity of access subgraphs between the correlation matrices. During the period, the ID3 algorithm is used under the decision tree to eliminate the gap in metric size between clustering subgroups and achieve spatial division of learners’ value types. The three critical dimensions of Japanese values education and their sub-dimensions were empirically analyzed, and the F-values of the three dimensions of values for students of different majors were 0.265, 0.431, and 0.078, respectively, with P-values of 0.05 or higher, so there was no significant difference in the perception of values among students of different majors. In the value analysis of “self” sub-dimension, the descriptive statistics mean prestige, economy, and society values are 4.3592, 4.249, and 3.8696 respectively, which are all above 3. The value of education in Japanese universities is practical.

The construction of new liberal arts requires college English teaching to introduce new concepts, utilize new technologies, and promote students’ effective learning through the intersection of disciplines and curricula, to cultivate integrative talents with innovative and practical abilities. Under the perspective of new liberal arts, this paper integrates deep commonality and uniqueness knowledge mining multimodal clustering, DCUMC algorithmic model and multimodal teaching means to construct a multimodal analysis model framework and reform the English teaching model in colleges and universities. In addition, the information entropy is used to improve the ID3 algorithm to construct multidimensional evaluation indexes for college English teaching, and the improved ID3 algorithm is used for data mining of multidimensional evaluation so that teachers can adjust the teaching progress and difficulty according to real-time and accurate feedback information. Experiments are conducted on multimodal teaching mode and teaching evaluation, and the experimental results show that compared with the traditional teaching mode, the average scores of multimodal English teaching mode after five months are 96 and 85, which is an improvement of 11 points, 69.58%, 67.93%, 56.25%, and 53.79% higher in the ratio of excellent and good evaluations, respectively. Compared to the single teaching evaluation, the multidimensional evaluation points out that the traditional teaching mode needs more designer-student interaction as well as a reduction in the difficulty of the teaching content. In conclusion, the multimodal English teaching model can effectively improve teaching effectiveness and is implementable.

Numerous studies have established the critical roles of microRNAs in regulating post-transcriptional gene expression in diverse biological processes. Here, we report on the role and mechanism of miR-24-3p in skeletal muscle differentiation and regeneration. miR-24-3p promotes myoblast differentiation and skeletal muscle regeneration by directly targeting high mobility group AT-hook 1 (HMGA1) and regulating it and its direct downstream target, the inhibitor of differentiation 3 (ID3). miR-24-3p knockdown in neonatal mice increases PAX7-positive proliferating muscle stem cells (MuSCs) by derepressing Hmga1 and Id3 . Similarly, inhibition of miR-24-3p in the tibialis anterior muscle prevents Hmga1 and Id3 downregulation and impairs regeneration. These findings provide evidence that the miR-24-3p/HMGA1/ID3 axis is required for MuSC differentiation and skeletal muscle regeneration in vivo.

With the development of sports science and information technology, how to use mining technology to analyze the correlation between various track and field training programs plays an important role in the improvement of track and field training. A decision support system for track and field training is constructed using the Apriori algorithm and decision tree ID3 algorithm. By mining and analyzing different sports data of track and field athletes, the system blends scientific training theory and advanced training methods to create a set of reasonable track and field training programs for athletes. Through empirical research, after the optimization of the track and field training decision support system, 72.2% of the athletes whose physical fitness test 30-meter run scores were between 80 and 100 had bar pull-up scores between 0 and 55. The average performance of students’ 3000-meter run improved by 81.35s.

CD5L (CD5 molecule-like) is a secreted glycoprotein that controls key mechanisms in inflammatory responses, with involvement in processes such as infection, atherosclerosis, and cancer. In macrophages, CD5L promotes an anti-inflammatory cytokine profile in response to TLR activation. In the present study, we questioned whether CD5L is able to influence human macrophage plasticity, and drive its polarization toward any specific phenotype. We compared CD5L-induced phenotypic and functional changes to those caused by IFN/LPS, IL4, and IL10 in human monocytes. Phenotypic markers were quantified by RT-qPCR and flow cytometry, and a mathematical algorithm was built for their analysis. Moreover, we compared ROS production, phagocytic capacity, and inflammatory responses to LPS. CD5L drove cells toward a polarization similar to that induced by IL10. Furthermore, IL10- and CD5L-treated macrophages showed increased LC3-II content and colocalization with acidic compartments, thereby pointing to the enhancement of autophagy-dependent processes. Accordingly, siRNA targeting ATG7 in THP1 cells blocked CD5L-induced CD163 and Mer tyrosine kinase mRNA and efferocytosis. In these cells, gene expression profiling and validation indicated the upregulation of the transcription factor ID3 by CD5L through ATG7. In agreement, ID3 silencing reversed polarization by CD5L. Our data point to a significant contribution of CD5L-mediated autophagy to the induction of ID3 and provide the first evidence that CD5L drives macrophage polarization.