Trends and Developments in Virtual Laboratory Research in Indonesian Secondary Schools : A Bibliometric Study Based on Text Mining

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

Charting a Professional Course: A Review of Mentorship in Medicine

Numerous e-news channels publish the daily happenings in the world from different sources. These huge amounts of news articles have lamentably conceived the information overload issue among the users. Hence text mining, which aims in extracting previously unknown information from unstructured text, has been widely used by several researchers to segregate full news articles however, the news headlines categorization is still specifically limited. Therefore, considering this limitation, the current research aims to propose a framework that will self-learn and automatically classify any given news headline into its corresponding news category using artificial intelligence methods i.e. text mining and machine learning algorithms. The proposed framework consists of three stages: Exploratory Data Analysis, Text Pre-processing, and Text Classification. For exploratory data analysis, the top 10 most frequent balanced news categories are chosen so that further processing of data can be done on a more balanced version of the dataset. After exploring the data, text pre-processing techniques are applied to make the data transformed, normalized, and structured. Finally, text classification is carried out with two approaches: unsupervised classification using Mean Shift and K-means algorithms and supervised classification using Logistic Regression with Bag of Words and TF-IDF algorithm. To depict the working of the proposed framework, a case study is presented on a news headlines dataset which accurately performed news headlines classification.

With the overwhelming increase in the amount of texts on the web, it is almost impossible for people to keep abreast of up-to-date information. Text mining is a process by which interesting information is derived from text through the discovery of patterns and trends. Text mining algorithms are used to guarantee the quality of extracted knowledge. However, the extracted patterns using text or data mining algorithms or methods leads to noisy patterns and inconsistency. Thus, different challenges arise, such as the question of how to understand these patterns, whether the model that has been used is suitable, and if all the patterns that have been extracted are relevant. Furthermore, the research raises the question of how to give a correct weight to the extracted knowledge. To address these issues, this paper presents a text post-processing method, which uses a pattern co-occurrence matrix to find the relation between extracted patterns in order to reduce noisy patterns. The main objective of this paper is not only reducing the number of closed sequential patterns, but also improving the performance of pattern mining as well. The experimental results on Reuters Corpus Volume 1 data collection and TREC filtering topics show that the proposed method is promising.

At its core, productive collaboration between speech-language pathologists and behavior analysts requires effective communication between both parties. Successful communication can be gained in par...

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

Character education is a multifaceted effort to develop moral and ethical individuals so that they can make a positive contribution to society. The study aims to analyze character education trends among students at the secondary, secondary, and high school levels in Indonesia. The study uses the Systematic Literature Review (SLR) method to investigate character education trends in elementary, primary, and upper secondary school students in Indonesia (systematic literature review). The study is guided by the PRISMA protocol and uses the Covidence platform to facilitate the review process. The sourcing of data was done through the Google Scholar and Crosreef databases with the sources of articles in reputable international journals, using Harzing’s Publish or Perish application. Of the 1,200 articles found, 69 journals were selected to be reviewed through a systematic exclusion and inclusion process. The articles related to auxiliary technology selected in this study were analyzed using quantitative bibliometric studies to map the structure of knowledge. The results of the research show that the trend of character education in Indonesia is experiencing increasing attention and intensive efforts at every level of education. Each level applies a different approach according to the characteristics and needs of student development but still has the same goal, namely, forming a positive personality and good morals. Overall, the study provides a comprehensive overview of the efforts of the character education application in Indonesia, aimed at developing a young generation that not only achieves academically but also has good personalities and morality.

Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys

The evolutionarily conserved ATP-dependent chromatin remodeling enzyme Fun30 has recently been shown to play important roles in heterochromatin silencing and DNA repair. However, how Fun30 remodels nucleosomes is not clear. Here we report a nucleosome sliding activity of Fun30 and its role in transcriptional repression. We observed that Fun30 repressed the expression of genes involved in amino acid and carbohydrate metabolism, the stress response, and meiosis. In addition, Fun30 was localized at the 5' and 3' ends of genes and within the open reading frames of its targets. Consistent with its role in gene repression, we observed that Fun30 target genes lacked histone modifications often associated with gene activation and showed an increased level of ubiquitinated histone H2B. Furthermore, a genome-wide nucleosome mapping analysis revealed that the length of the nucleosome-free region at the 5' end of a subset of genes was changed in Fun30-depleted cells. In addition, the positions of the -1, +2, and +3 nucleosomes at the 5' end of target genes were shifted significantly, whereas the position of the +1 nucleosome remained largely unchanged in the fun30Δ mutant. Finally, we demonstrated that affinity-purified, single-component Fun30 exhibited a nucleosome sliding activity in an ATP-dependent manner. These results define a role for Fun30 in the regulation of transcription and indicate that Fun30 remodels chromatin at the 5' end of genes by sliding promoter-proximal nucleosomes.

The p7 protein of hepatitis C virus functions as an ion channel both in vitro and in cell-based assays and is inhibited by amantadine, long alkyl chain imino-sugar derivatives, and amiloride compounds. Future drug design will be greatly aided by information on the stoichiometry and high resolution structure of p7 ion channel complexes. Here, we have refined a bacterial expression system for p7 based on a glutathione S-transferase fusion methodology that circumvents the inherent problems of hydrophobic protein purification and the limitations of chemical synthesis. Rotational averaging and harmonic analysis of transmission electron micrographs of glutathione S-transferase-FLAG-p7 fusion proteins in liposomes revealed a heptameric stoichiometry. The oligomerization of p7 protein was then confirmed by SDS-PAGE and mass spectrometry analysis of pure, concentrated FLAG-p7. The same protein was also confirmed to function as an ion channel in suspended lipid bilayers and was inhibited by amantadine. These data validate this system as a means of generating high resolution structural information on the p7 ion channel complex.

The virtual laboratory has been widely used in biology learning at all levels of education, but it remains a challenge for countries with technological shortages. Many biological concepts were abstract and difficult to be understood through simple explanation or even difficult to do in a laboratory. Utilization of virtual labs in biology learning, especially in higher education needs to be reviewed about the characteristics of contents, and function and its efficiency to assess the specific variables. Thus, this systematic review was carried out to analyze research articles published from 2010 to 2018. The articles were selected using the PRISMA. The articles were obtained from Google Scholar, the Science Direct, and the Journal of Science Education, ERIC, and Journal of Biological Education. In total, 199 articles were found. The general analysis was conducted. The results yielded 47 articles to be further analyzed by looking at the content and results of the study. Twenty-three articles were obtained and analyzed. Journal content analysis was focused on the selected topic, the dependent variables, sample size, effect size, research design, data collection techniques, research objectives, and the results. Cell or molecular biology was the most widely used topics in the virtual laboratory. Students’ conceptual understanding was the most studied variable, some studies examine students’ affective and psychomotor skills, but only a few studies examine the learning process. Most biology virtual labs were developed using the Adobe Flash Player application with 3D animation. The results suggested for further research to develop a virtual biology lab that is more inquiry-based, and capable of assessing students’ conceptual change and science process skills which not have been widely studied.

Thrombin exists in two allosteric forms, slow (S) and fast (F), that recognize natural substrates and inhibitors with significantly different affinities. Because under physiologic conditions the two forms are almost equally populated, investigation of thrombin function must address the contribution from the S and F forms and the molecular origin of their differential recognition of ligands. Using a panel of 79 Ala mutants, we have mapped for the first time the epitopes of thrombin recognizing a macromolecular ligand, hirudin, in the S and F forms. Hirudin binding is a relevant model for the interaction of thrombin with fibrinogen and PAR1 and is likewise influenced by the allosteric S-->F transition. The epitopes are nearly identical and encompass two hot spots, one in exosite I and the other in the Na+ site at the opposite end of the protein. The higher affinity of the F form is due to the preferential interaction of hirudin with Lys-36, Leu-65, Thr-74, and Arg-75 in exosite I; Gly-193 in the oxyanion hole; and Asp-221 and Asp-222 in the Na+ site. Remarkably, no correlation is found between the energetic and structural involvements of thrombin residues in hirudin recognition, which invites caution in the analysis of protein-protein interactions in general.

Background: Teacher-student interaction plays a pivotal role in shaping the quality of mathematics learning. However, in many Indonesian classrooms, traditional models of instruction often dominate, limiting opportunities for students to engage in meaningful mathematical discourse. Aims: This study aims to: (1) evaluate the patterns of teacher-student interaction during mathematics instruction in Indonesian secondary schools, and (2) examine the implementation of sociomathematical norms in a redesigned learning model using a problem-posing approach. Methods: A qualitative research approach was adopted involving observations of eight mathematics classrooms in Lampung, Indonesia. During the odd semester, four classes were observed using conventional teaching methods. In the even semester, four teachers implemented a problem-posing model co-designed with the researcher. Data were collected via classroom video recordings and analyzed using the Flanders Interaction Analysis System (FIAS) with support from NVIVO software. Results: The findings reveal that traditional classrooms were heavily teacher-dominated, with 86% of classroom talk attributed to teachers and only 14% to students. Common classroom norms included praying, singing, asking questions, listening to explanations, and discussions. In contrast, problem-posing classrooms demonstrated the emergence of sociomathematical norms such as mathematical explanation, differentiation of ideas, and student-generated problems. Conclusion: The study highlights the limited interaction in traditional classrooms and the potential of sociomathematical norms to enrich student participation and mathematical discourse when implemented through structured problem-posing models. These findings inform future instructional design that prioritizes interactive and student-centered learning environments in mathematics education.

Prediction models for COVID-19 clinical decision making

Time to pay the piper: Fossil fuel companies’ reparations for climate damages