Analysis Of Learning Techniques Research Articles

Development Of English Education Using Big Data And Learning Analytics

With English education using big data and learning analytics represents a transformative approach to enhancing language learning outcomes and pedagogical practices. By harnessing the power of big data analytics, educational institutions can collect, process, and analyze vast amounts of learner data, including student interactions with digital learning materials, performance on assessments, and engagement metrics. Through sophisticated learning analytics techniques, educators gain valuable insights into students' learning behaviors, preferences, strengths, and areas for improvement. The paper explores the application of big data analytics and learning analytics in English language education within Chinese universities. Utilizing a diverse dataset, including proficiency levels, engagement metrics, and demographic characteristics, the study employs Median Clustering Fuzzy Learning Analytics (MCFLA) to predict students' engagement levels based on language proficiency scores. Findings reveal varying proficiency levels among students, alongside mixed patterns of engagement. The integration of MCFLA proves effective in identifying trends and guiding educational interventions. Median scores provide benchmarks for assessing performance, while demographic factors offer additional insights. Findings reveal varying proficiency levels among students: 40% beginner, 30% intermediate, and 30% advanced. Mixed patterns of engagement are observed, with 50% high engagement, 30% medium, and 20% low. Integration of MCFLA effectively predicts engagement trends, with 75% accuracy. Median scores provide benchmarks for assessing performance: beginner (Vocabulary: 63, Grammar: 66, Reading: 68, Oral Communication: 55), intermediate (Vocabulary: 78, Grammar: 79, Reading: 80, Oral Communication: 72), and advanced (Vocabulary: 90, Grammar: 88, Reading: 95, Oral Communication: 80). Demographic factors such as gender (50% male, 50% female) and socio-economic status (40% middle class, 30% upper class, 30% lower class) offer additional insights.

Using Learning Analytics to Understand K–12 Learner Behavior in Online Video-Based Learning

This research investigated the potential of learning analytics (LA) as a tool for identifying and evaluating student behaviours associated with active learning within an online learning environment. The study focused on the application of LA for evaluating student engagement in video-based learning – an area of inquiry highlighted in literature as needing further investigation. Results determined that the LA method could identify active learning behaviours, and that LA can play a valuable role in providing information on learner activity in autonomous learning environments. However, LA did not provide a complete picture of learner behaviour and viewing strategies, highlighting the importance of a multi-method approach to research on online learner behaviours. It is anticipated the accessible approach outlined in this study will provide educators with a viable means of using LA techniques to better understand how learners interact with course content and learning objects, greatly assisting the design of online learning programmes.

Interpretable prediction of acute respiratory infection disease among under-five children in Ethiopia using ensemble machine learning and Shapley additive explanations (SHAP).

Although the prevalence of childhood illnesses has significantly decreased, acute respiratory infections continue to be the leading cause of death and disease among children in low- and middle-income countries. Seven percent of children under five experienced symptoms in the two weeks preceding the Ethiopian demographic and health survey. Hence, this study aimed to identify interpretable predicting factors of acute respiratory infection disease among under-five children in Ethiopia using machine learning analysis techniques. Secondary data analysis was performed using 2016 Ethiopian demographic and health survey data. Data were extracted using STATA and imported into Jupyter Notebook for further analysis. The presence of acute respiratory infection in a child under the age of 5 was the outcome variable, categorized as yes and no. Five ensemble boosting machine learning algorithms such as adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), Gradient Boost, CatBoost, and light gradient-boosting machine (LightGBM) were employed on a total sample of 10,641 children under the age of 5. The Shapley additive explanations technique was used to identify the important features and effects of each feature driving the prediction. The XGBoost model achieved an accuracy of 79.3%, an F1 score of 78.4%, a recall of 78.3%, a precision of 81.7%, and a receiver operating curve area under the curve of 86.1% after model optimization. Child age (month), history of diarrhea, number of living children, duration of breastfeeding, and mother's occupation were the top predicting factors of acute respiratory infection among children under the age of 5 in Ethiopia. The XGBoost classifier was the best predictive model with improved performance, and predicting factors of acute respiratory infection were identified with the help of the Shapely additive explanation. The findings of this study can help policymakers and stakeholders understand the decision-making process for acute respiratory infection prevention among under-five children in Ethiopia.

A Survey on Information Bottleneck.

This survey is for the remembrance of one of the creators of the information bottleneck theory, Prof. Naftali Tishby, passing away at the age of 68 on August, 2021. Information bottleneck (IB), a novel information theoretic approach for pattern analysis and representation learning, has gained widespread popularity since its birth in 1999. It provides an elegant balance between data compression and information preservation, and improves its prediction or representation ability accordingly. This survey summarizes both the theoretical progress and practical applications on IB over the past 20-plus years, where its basic theory, optimization, extensive models and task-oriented algorithms are systematically explored. Existing IB methods are roughly divided into two parts: traditional and deep IB, where the former contains the IBs optimized by traditional machine learning analysis techniques without involving any neural networks, and the latter includes the IBs involving the interpretation, optimization and improvement of deep neural works (DNNs). Specifically, based on the technique taxonomy, traditional IBs are further classified into three categories: Basic, Informative and Propagating IB; While the deep IBs, based on the taxonomy of problem settings, contain Debate: Understanding DNNs with IB, Optimizing DNNs Using IB, and DNN-based IB methods. Furthermore, some potential issues deserving future research are discussed. This survey attempts to draw a more complete picture of IB, from which the subsequent studies can benefit.

Analysis of the optimization of resources with Learning Analytics techniques

This paper presents an empirical study based on the learning environment through different data analysis tools. The study is applied to the subject of Theory of Machines and Materials Resistance of the Chemical Engineering degree at the Universitat Politècnica de València (Spain), with the aim of being able to understand and optimise with greater knowledge the way of learning taught, to know what is more difficult for the students and to create a more personalised learning environment. In order to achieve this, it is important to have as much information as possible about the use and usefulness of the resources provided to the students as a teacher. Knowing this data will allow us to provide more efficient resources and to change those that, through data analysis, are not being useful to students. The results of this research show how, through applications such as Learning Analytics, greater performance can be obtained in both teaching and learning.

Utilizing augmented reality for embodied mental rotation training: A learning analytics study

AbstractMental rotation is the ability to mentally rotate two‐dimensional and three‐dimensional objects in one's mind. Previous studies have examined the relationship between mental rotation abilities and embodied cognition. However, conflicting findings underscore the necessity for further investigation. This study aims to investigate how different levels of embodiment in mental rotation training affect mental rotation training. Three versions of mental rotation training with different levels of embodiment (keyboard, touchpad and augmented reality) were developed for this study. A quasi‐experimental study was conducted with a total of 125 university students to explore how different training versions affect participants' training performances, perception of the usability of the training and mental rotation ability. The results suggest those who used the versions with higher levels of bodily engagement performed better than those who used the versions with lower levels of bodily engagement (p < 0.001). Participants' mental rotation ability also improved significantly after training with the augmented reality (AR1) version compared to those in the keyboard group (p = 0.01). The learning analytics results provide additional clarity by indicating that the participants' mental rotation ability can be predicted by their training performance in the AR condition (p < 0.001). This study has significant implications for the potential of using embodied AR training to improve mental rotation ability. Practitioner notesWhat is already known about this topic Mental rotation ability is a malleable trait that can be improved through repeated and long‐term game‐based training. Short‐term training using digital games has limited impacts on mental rotation ability. Mental rotation abilities are linked to our bodily interactions with the physical world. What this paper adds Investigates how different levels of bodily engagement in a digital game affect short‐term mental rotation training. Investigates how different levels of bodily engagement in a mental rotation training game affect players' game performance and perception of the usability of the game. Investigates if players' performance in a mental rotation training game can predict their mental rotation ability and self‐diagnosed sense of direction. Leverages learning analytics to explain findings generated by traditional research methods. Implications for practice and/or policy Leveraging augmented reality technology in more embodied mental rotation training games can facilitate short‐term gains in mental rotation. Game‐based learning researchers should consider leveraging learning analytics techniques to provide richer and more nuanced explanations for findings generated by traditional research methods. Game performance in effective mental rotation training games can predict mental rotation abilities, thus potentially assessing such abilities.

Influence of Learning Intervention on Online Learners’ Performance

With the expansion of the online learning scale and the rapid development of learning analysis techniques, learning systems can completely record learners’ online learning behavior. What types of learning intervention measures can influence learning performance? How is the intervention working? The above questions are the focus of online courses and emphasize improving the online learning effect. In this research, 96 undergraduates (Classes 1 and 2, Grade 2) majoring in civil engineering and enrolled in the Engineering Surveying online course at the College of Transportation Engineering, Huanghe Jiaotong University, Henan Province, were selected as the research objects and randomly divided into two groups (the experimental group (N = 48) and the control group (N = 48)). Then, conducted a 15-week sustained intervention study through quasi-experimental research, and verified the effectiveness of different types of intervention measures on the learners’ learning performance. Next, a one-way analysis of variance was conducted, based on the pretesting of the performance data, the quality of online discussion posts, the effective learning duration, and the online final test results. Research results showed no obvious differences between the experimental group and control group in the online final test results from the previous semester (P = 0.347). The online learners’ learning performance was influenced by three aspects of the learning intervention, that is, apparent differences in the posting quality in the learning prompt intervention (T = 8.23, P < 0.01), the online learning duration in the process monitoring intervention (T = 23.19, P < 0.01), and the online final test results in the achievement incentive intervention (T = 6.08, P < 0.01). The research results have an important reference value for implementing intervention strategies in the online learning environment, judging the effectiveness of such strategies according to curve changes in performance, and using the mass data of learners recorded by online learning platforms.

Um modelo de Sistema de Recomendação integrado a Metodologias Ativas, MDE e Learning Analytics para a mitigação de evasão em EaD

Distance Education enabled educational practices based on digital platforms. Despite its wide adoption, the high dropout rates are a reason for concern for teachers and institutional managers. There are initiatives to mitigate this situation, such as Educational Data Mining (EDM), Learning Analytics (LA), and the use of Recommendation Systems (RS). Although effective in specific aspects, these techniques lack mechanisms for students' motivation and pedagogical intervention by teachers, as they do not present methodological proposals to encourage learning. Therefore, this article describes an RS model that shows a differential integration of the pedagogical approach of Active Methodologies with the support of Educational Data Mining and Learning Analytics techniques to identify students with dropout risks and enhance permanence. For this, a prototype was implemented, and a case study was carried out with professors from two universities to assess functionality and acceptance. According to the TAM Model, more than 87% of teachers agree with the ease of use, and 77% agree that RS can be helpful in students' teaching and learning process. Therefore, the model contributes to teaching practices, encourages collaborative learning, and favors monitoring this process and the activities developed by the students.

Novel Bilinear Fusion Network Based on Multimodal Data for Student Distracted Behavior Recognition

As governments, education departments, and academic accreditation bodies have begun to encourage schools to develop evidence-based decision-making and innovation systems, learning analysis techniques have shown great advantages in decision-making aid and teaching evaluation. After integrating relevant algorithms and technologies in artificial intelligence and machine learning, learning analysis has achieved higher analysis accuracy. In order to realize the recognition of students' classroom behaviors such as standing up, sitting up, and raising hands and improve the recognition accuracy and recall rate, multi-modal data such as human key point information and RGB images are used for experiments. To further improve the feature extraction capability of the model, features are extracted from the improved ResNet-50 and EfficientNet-B0 models, and bilinear fusion is performed to further improve the recognition accuracy of the models.

Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis.

Microfluidic cultivation devices that facilitate O2 control enable unique studies of the complex interplay between environmental O2 availability and microbial physiology at the single-cell level. Therefore, microbial single-cell analysis based on time-lapse microscopy is typically used to resolve microbial behavior at the single-cell level with spatiotemporal resolution. Time-lapse imaging then provides large image-data stacks that can be efficiently analyzed by deep learning analysis techniques, providing new insights into microbiology. This knowledge gain justifies the additional and often laborious microfluidic experiments. Obviously, the integration of on-chip O2 measurement and control during the already complex microfluidic cultivation, and the development of image analysis tools, can be a challenging endeavor. A comprehensive experimental approach to allow spatiotemporal single-cell analysis of living microorganisms under controlled O2 availability is presented here. To this end, a gas-permeable polydimethylsiloxane microfluidic cultivation chip and a low-cost 3D-printed mini-incubator were successfully used to control O2 availability inside microfluidic growth chambers during time-lapse microscopy. Dissolved O2 was monitored by imaging the fluorescence lifetime of the O2-sensitive dye RTDP using FLIM microscopy. The acquired image-data stacks from biological experiments containing phase contrast and fluorescence intensity data were analyzed using in-house developed and open-source image-analysis tools. The resulting oxygen concentration could be dynamically controlled between 0% and 100%. The system was experimentally tested by culturing and analyzing an E. coli strain expressing green fluorescent protein as an indirect intracellular oxygen indicator. The presented system allows for innovative microbiological research on microorganisms and microbial ecology with single-cell resolution.

Online Learning Models in The Era and Post-Pandemic COVID-19

Pandemic COVID-19 has shifted education from face-to-face teaching to online-based distance learning. Educators must prepare strategic steps in responding to this transition to achieve learning objectives. To implement an online learning system, learning strategies and models that meet online learning demands must be made. For this reason, this study aims to analyze the online learning model in the era and post-pandemic COVID-19. Literature research methods are used in this study. Data sources were obtained by collecting relevant literature such as books, journals or scientific articles related to the topic: (i) Pandemic COVID-19; (ii) learning model; and (iii) online learning—analysis technique using content analysis. The results showed four appropriate learning models used in the online learning process: ICT -based learning models, problem-based learning models, blended learning models, and module-based learning models. The four learning models can support a distance learning system, increase student independence, improve digitalization skills and discipline, and increase student collaboration.

Matters of Frequency, Immediacy and Regularity: Engagement in an Online Asynchronous Course.

Many models of online student engagement posit a "more is better" relationship between students' course-related actions and their engagement. However, recent research indicates that the timing of engagement is also an important consideration. In addition to the frequency (how often) of engagement, two other constructs of timing were explored in this study: immediacy (how early) and regularity (in what ordered pattern). These indicators of engagement were applied to three learning assessment types used in an online, undergraduate, competency-based, technology skills course. The study employed advanced data collection and learning analytics techniques to collect continuous behavioral data over seven semesters (n = 438). Results revealed that several indicators of engagement predicted academic success, but significance differed by assessment type. "More" is not always better, as some highly engaged students earn lower grades. Successful students tended to engage earlier with lessons regardless of assessment type.

A Scoping Review of the Metaverse for Software Engineering Education: Overview, Challenges, and Opportunities

Abstract In the Software Engineering Education (SEE) context, virtual worlds have been used in order to improve learning outcomes. However, there is a gap in the literature in order to characterize the use of the Metaverse for SEE. The objective of this work is to characterize the state of the art of virtual worlds in SEE and provide research opportunities and challenges to fill the limitations found. We conducted a systematic literature review, and we established 8 research questions that guided the study, as well as performed data extraction. We report on 17 primary studies that deal mostly with immersive experiences in SEE. The results show some limitations: few Software Engineering (SE) topics are covered; most applications simulate environments and do not explore new ways of viewing and interacting; there is no interoperability between virtual worlds; learning analysis techniques are not applied; and biometric data are not considered in the validations of the studies. Although there are virtual worlds for SEE, the results indicate the need to develop mechanisms in order to support the integration between virtual worlds. Therefore, based on the findings of the review, we established a set of components grouped by 5 layers to enable the Metaverse for SEE through fundamental requirements. We hope that this work can motivate promising research in order to foster immersive learning experiences in SE through the Metaverse.

Using Emotional Learning Analytics to Improve Students’ Engagement in Online Learning

Online learning is learning enabled by technology as students’ interact with subject content, access and download resources, watch videos, and participate in online quizzes. Epistemic Emotions (EE), e.g., curiosity, surprise, confusion, frustration or boredom has a complex impact when learning online due to the lack of real-time feedback loop. It becomes vital to detect such emotions timely and accurately before they starts impacting the leaner adversely (e.g., D’Mello et al., 2017; Kosasi et al., 2020). EE activate or confines the learning. EE can be positive (surprise, enjoyment) or negative (frustration, boredom), or two folded (confusion). Positive emotions contribute to learning achievement, while negative emotions can cause frustration or anxiety, leading to impaired learning. Emotions are ambiguous and dynamic, and without innovations in learning analytics (LA) and Educational Data Mining, it is challenging to predict such emotions (D’Mello, S., 2017). This extended abstract focuses on how LA can help detect surprise and confusion in online learning, ultimately accomplishing achievement emotion(s) in learners. LA provides tools and techniques to measure, collect, analyse, and report interaction data of students. Deeper data analysis of all kinds of digital traces collected on online learning platforms has the potential to detect emotional and cognitive states (Karaoglan & Yilmez 2022; Han et. al., 2021; Silvola et al., 2021). Innovations in emotional learning analytics leverage the voluminous and heterogeneous data available and propose adopting data-driven analytical approaches to study the impact of such emotions on students’ engagement and cognition. EE like surprise helps learners discover new knowledge, and improve memory and focus and cognition (Foster & Keane, 2019). It adds the "novelty factor" to nourish students' curiosity and motivation (Hayden et al., 2011; Roesch et al., 2012). Whilst, confusion has a complex impact on cognition. Confusion causes disequilibrium due to incongruent information (Arguel et al., 2017). When students overcome the confusion, it helps in positive learning gains, but prolonged confusion can leave a student frustrated or bored, impeding their learning (Atapattu et al., 2019; Baker et al., 2010). The voluminous activity log data is valuable and non-intrusive source of information in detecting important epistemic emotions and can help identify potential learning difficulties. Such data promotes unbiased real-time assessment of emotions and is technically easy to deploy simultaneously. Text data on online platforms are analysed to isolate emotions like confusion and frustration (e.g., Ai et al., 2006; Lee et al., 2011). Online quiz data is analysed using fuzzy logic inferences (Author, 2021) and multilayer perceptron (Author, 2021) techniques to detect confusion in online learning. Both surprise and confusion can be induced to promote meaningful learning, for example, some studies have added conflicting text or triggered high-confidence errors in their experimentation. Epistemic Emotion Scales (EES) were then used to detect surprise, confusion and even curiosity, by analysing the impact of such unexpected text. (e.g Pekrun et al., 2017, Vogl et al., 2020). We show in this study, that LA tools and techniques can be utilised to detect EE to foster meaningful learning engagement and interventions, hence providing great research potential.

Learning analytics application to examine validity and generalizability of game‐based assessment for spatial reasoning

AbstractGame‐based assessment (GBA), a specific application of games for learning, has been recognized as an alternative form of assessment. While there is a substantive body of literature that supports the educational benefits of GBA, limited work investigates the validity and generalizability of such systems. In this paper, we describe applications of learning analytics methods to provide evidence for psychometric qualities of a digital GBA called Shadowspect, particularly to what extent Shadowspect is a robust assessment tool for middle school students' spatial reasoning skills. Our findings indicate that Shadowspect is a valid assessment for spatial reasoning skills, and it has comparable precision for both male and female students. In addition, students' enjoyment of the game is positively related to their overall competency as measured by the game regardless of the level of their existing spatial reasoning skills. Practitioner notesWhat is already known about this topic: Digital games can be a powerful context to support and assess student learning. Games as assessments need to meet certain psychometric qualities such as validity and generalizability. Learning analytics provide useful ways to establish assessment models for educational games, as well as to investigate their psychometric qualities. What this paper adds: How a digital game can be coupled with learning analytics practices to assess spatial reasoning skills. How to evaluate psychometric qualities of game‐based assessment using learning analytics techniques. Investigation of validity and generalizability of game‐based assessment for spatial reasoning skills and the interplay of the game‐based assessment with enjoyment. Implications for practice and/or policy: Game‐based assessments that incorporate learning analytics can be used as an alternative to pencil‐and‐paper tests to measure cognitive skills such as spatial reasoning. More training and assessment of spatial reasoning embedded in games can motivate students who might not be on the STEM tracks, thus broadening participation in STEM. Game‐based learning and assessment researchers should consider possible factors that affect how certain populations of students enjoy educational games, so it does not further marginalize specific student populations.

Learning Analytics to Determine Profile Dimensions of Students Associated with Their Academic Performance

With the recent advancements of learning analytics techniques, it is possible to build predictive models of student academic performance at an early stage of a course, using student’s self-regulation learning and affective strategies (SRLAS), and their multiple intelligences (MI). This process can be conducted to determine the most important factors that lead to good academic performance. A quasi-experimental study on 618 undergraduate students was performed to determine student profiles based on these two constructs: MI and SRLAS. After calibrating the students’ profiles, learning analytics techniques were used to study the relationships among the dimensions defined by these constructs and student academic performance using principal component analysis, clustering patterns, and regression and correlation analyses. The results indicate that the logical-mathematical intelligence, intrinsic motivation, and self-regulation have a positive impact on academic performance. In contrast, anxiety and dependence on external motivation have a negative effect on academic performance. A priori knowledge of the characteristics of a student sample and its likely behavior predicted by the models may provide both students and teachers with an early-awareness alert that can help the teachers in designing enhanced proactive and strategic decisions aimed to improve academic performance and reduce dropout rates. From the student side, knowledge about their main academic profile will sharpen their metacognition, which may improve their academic performance.

Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design

National educational standards stress the importance of science and mathematics learning for today’s students. However, across disciplines, students frequently struggle to meet learning goals about core concepts like energy. Digital learning environments enhanced with artificial intelligence hold the promise to address this issue by providing individualized instruction and support for students at scale. Scaffolding and feedback, for example, are both most effective when tailored to students’ needs. Providing individualized instruction requires continuous assessment of students’ individual knowledge, abilities, and skills in a way that is meaningful for providing tailored support and planning further instruction. While continuously assessing individual students’ science and mathematics learning is challenging, intelligent tutoring systems show that it is feasible in principle. However, the learning environments in intelligent tutoring systems are typically not compatible with the vision of how effective K-12 science and mathematics learning looks like. This leads to the challenge of designing digital learning environments that allow for both – meaningful science and mathematics learning and the reliable and valid assessment of individual students’ learning. Today, digital devices such as tablets, laptops, or digital measurement systems increasingly enter science and mathematics classrooms. In consequence, students’ learning increasingly produces rich product and process data. Learning Analytics techniques can help to automatically analyze this data in order to obtain insights about individual students’ learning, drawing on general theories of learning and relative to established domain specific models of learning, i.e., learning progressions. We call this approach Learning Progression Analytics (LPA). In this manuscript, building of evidence-centered design (ECD), we develop a framework to guide the development of learning environments that provide meaningful learning activities and data for the automated analysis of individual students’ learning – the basis for LPA and scaling individualized instruction with artificial intelligence.

Using the DiCoT framework for integrated multimodal analysis in mixed-reality training environments.

Simulation-based training (SBT) programs are commonly employed by organizations to train individuals and teams for effective workplace cognitive and psychomotor skills in a broad range of applications. Distributed cognition has become a popular cognitive framework for the design and evaluation of these SBT environments, with structured methodologies such as Distributed Cognition for Teamwork (DiCoT) used for analysis. However, the analysis and evaluations generated by such distributed cognition frameworks require extensive domain-knowledge and manual coding and interpretation, and the analysis is primarily qualitative. In this work, we propose and develop the application of multimodal learning analysis techniques to SBT scenarios. Using these analysis methods, we can use the rich multimodal data collected in SBT environments to generate more automated interpretations of trainee performance that supplement and extend traditional DiCoT analysis. To demonstrate the use of these methods, we present a case study of nurses training in a mixed-reality manikin-based (MRMB) training environment. We show how the combined analysis of the video, speech, and eye-tracking data collected as the nurses train in the MRMB environment supports and enhances traditional qualitative DiCoT analysis. By applying such quantitative data-driven analysis methods, we can better analyze trainee activities online in SBT and MRMB environments. With continued development, these analysis methods could be used to provide targeted feedback to learners, a detailed review of training performance to the instructors, and data-driven evidence for improving the environment to simulation designers.

Characterizing the extracellular matrix transcriptome of cervical, endometrial, and uterine cancers

Increasingly, the matrisome, a set of proteins that form the core of the extracellular matrix (ECM) or are closely associated with it, has been demonstrated to play a key role in tumor progression. However, in the context of gynecological cancers, the matrisome has not been well characterized. A holistic, yet targeted, exploration of the tumor microenvironment is critical for better understanding the progression of gynecological cancers, identifying key biomarkers for cancer progression, establishing the role of gene expression in patient survival, and for assisting in the development of new targeted therapies. In this work, we explored the matrisome gene expression profiles of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS) using publicly available RNA-seq data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) portal. We hypothesized that the matrisomal expression patterns of CESC, UCEC, and UCS would be highly distinct with respect to genes which are differentially expressed and hold inferential significance with respect to tumor progression, patient survival, or both. Through a combination of statistical and machine learning analysis techniques, we identified sets of genes and gene networks which characterized each of the gynecological cancer cohorts. Our findings demonstrate that the matrisome is critical for characterizing gynecological cancers and transcriptomic mechanisms of cancer progression and outcome. Furthermore, while the goal of pan-cancer transcriptional analyses is often to highlight the shared attributes of these cancer types, we demonstrate that they are highly distinct diseases which require separate analysis, modeling, and treatment approaches. In future studies, matrisome genes and gene ontology terms that were identified as holding inferential significance for cancer stage and patient survival can be evaluated as potential drug targets and incorporated into in vitro models of disease.

Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence.

X-ray computed tomography (CT) is a non-destructive imaging technique in which contrast originates from the materials' absorption coefficient. The recent development of laboratory nanoscale CT (nano-CT) systems has pushed the spatial resolution for battery material imaging to voxel sizes of 50 nm, a limit previously achievable only with synchrotron facilities. Given the non-destructive nature of CT, in situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area and volume expansion, during battery operation or cycling. Combined with artificial intelligence and machine learning analysis techniques, nano-CT has enabled the development of predictive models to analyse the impact of the electrode microstructure on cell performances or the influence of material heterogeneities on electrochemical responses. In this Review, we discuss the role of X-ray CT and nano-CT experimentation in the battery field, discuss the incorporation of artificial intelligence and machine learning analyses and provide a perspective on how the combination of multiscale CT imaging techniques can expand the development of predictive multiscale battery behavioural models.