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Abstract Salicylic acid (SA) production in Brassicaceae plants is uniquely accelerated from isochorismate by EPS1, a newly identified enzyme in the BAHD acyltransferase family. We present crystal structures of EPS1 from Arabidopsis thaliana in both its apo and substrate-analog-bound forms. Integrating microsecond-scale molecular dynamics simulations with quantum mechanical cluster modeling, we propose a pericyclic rearrangement lyase mechanism for EPS1. We further reconstitute the isochorismate-derived SA biosynthesis pathway in Saccharomyces cerevisiae, establishing an in vivo platform to examine the impact of active-site residues on EPS1 functionality. Moreover, stable transgenic expression of EPS1 in soybean increases basal SA levels, highlighting the enzyme’s potential to enhance defense mechanisms in non-Brassicaceae plants lacking an EPS1 ortholog. Our findings illustrate the evolutionary adaptation of an ancestral enzyme’s active site to enable a novel catalytic mechanism that boosts SA production in Brassicaceae plants.

Abstract The gold standard to estimate muscle mass and quality is computed tomography (CT) scan. Lower mass and density (intramuscular fat infiltration) of skeletal muscles are markers of sarcopenia, associated with increased mortality risk, impaired physical function, and poorer prognosis across various populations and medical conditions. We aimed to describe standard reference values in healthy population, prospective kidney donors, and correlate clinical parameters to muscle mass and density. Included in the cohort 384 consecutive kidney donors. Mean age was 44.6 ± 11.5 (range 18.4–74.2), 46% were female and mean BMI was 25.6 ± 3.8 kg/m2. Our quantified reference values for psoas cross -sectional area (CSA) index at L3 level (males/females respectively) were 6.3 ± 1.8 and 4.8 ± 1.9 cm2 /m2, and density was 46.1 ± 5 and 41 ± 5 HU at that level. Older age (standardized beta coefficient − 0.12, p = 0.04), sex (− 0.32, p

Abstract The Illumina Methylation array platform has facilitated countless epigenetic studies on DNA methylation (DNAme) in health and disease, yet relatively few studies have so studied its reliability, i.e., the consistency of repeated measures. Here we investigate the reliability of both type I and type II Infinium probes. We propose a method for excluding unreliable probes based on dynamic thresholds for mean intensity (MI) and ‘unreliability’, estimated by probe-level simulation of the influence of technical noise on methylation β values using the background intensities of negative control probes. We validate our method in several datasets, including newly generated Illumina MethylationEPIC BeadChip v1.0 data from paired whole blood samples taken six weeks apart and technical replicates spanning multiple sample types. Our analysis revealed that specifically probes with low MI exhibit higher β value variability between repeated samples. MI was associated with the number of C-bases in the respective probe sequence and correlated negatively with unreliability scores. The unreliability scores were substantiated through validation in a new EPIC v1.0 (blood and cervix) and a publicly available 450k (blood) dataset, as they effectively captured the variability observed in β values between technical replicates. Finally, despite promising higher robustness, the newer version v2.0 of the MethylationEPIC BeadChip retained a substantial number of probes with poor unreliability scores. To enhance current pre-processing pipelines, we developed an R package to calculate MI and unreliability scores and provide guidance on establishing optimal dynamic score thresholds for a given dataset.

ChatGPT is a chatbot with potential educational benefits, particularly in enhancing computational thinking (CT) proficiencies such as programming, debugging, and algorithmic thinking for students. Despite its promise, there is limited research on how ChatGPT can specifically support the integration of CT into mathematics education using tools like GeoGebra. The researchers implemented plugged-computational thinking in mathematics (Math+CT) lessons by means of the utilization of GeoGebra, an application that requires students to input commands in order to generate mathematical objects. The present investigation employed an educational design research (EDR) methodology in which the researchers incorporate ChatGPT into our Math+CT lessons to assist students in accomplishing the task. We purposely selected the participants who are mainly postgraduate students and collected data from the participants’ conversation with ChatGPT and recorded their screens while interacting with ChatGPT and our Math+CT task. We analyzed the data through descriptive qualitative method on the participants’ prompts, the final codes and the number of iterations. The researchers examined how ChatGPT could be utilized to assist the participants in writing GeoGebra commands in terms of its benefits and limitations. ChatGPT assisted most participants in completing the task successfully, with only a basic need for proficiency in GeoGebra commands, mathematics, and critical thinking. However, it revealed that participants did not yet utilize an affective prompt to ChatGPT. Furthermore, ChatGPT has the potential to be utilized for differentiated instruction due to the fact that its responses to individual users vary significantly based on the input prompts. Limited understanding of basic GeoGebra commands, and mathematical concepts could hinder the participants from training ChatGPT or prevent them from arguing with ChatGPT. This study enhances the existing literature by illustrating that ChatGPT can facilitate critical CT aspects, including programming and debugging, in a mathematics education context. This suggests that AI tools such as ChatGPT can contribute to the development of independent problem-solving skills, provide tailored support based on the needs of individual students, and enhance personalized learning experiences. Additional research involving students in school is required in order to gain a deeper understanding of the integration of ChatGPT into Math+CT lessons.

This article presents the findings of a comparative study of values-based adult learning and education (ALE) fields; specifically, in Austria, critical-emancipatory adult basic education; Scotland, learner-centered, community-based adult learning; and South Tyrol, Italy, the "Winterschule," a radical-critical popular education format. We studied the voices of experienced practitioners to understand their challenges and constraints and how they resisted changes that did not reflect their values. It highlights local practices to promote visibility, create dialogue, and strengthen the understanding and recognition of these fields in which practitioners are unequivocally committed to social justice. Our findings highlighted repertoires of resistance, affirmed pedagogy as a values-based endeavor, and elucidated practitioners' commitments to holistic approaches that re-negotiate ideas of social justice for people and the planet. Our analysis shows that when ideas of social justice are shared amongst allies, practitioners can effectively maintain their values-based approaches and thus re-affirm and protect democratic ALE practice.

IntroductionSocial isolation is a main risk factor for loneliness, health issues and psychological diseases. With its restriction measures, the coronavirus pandemic has led to an objective reduction in meaningful interactions, communication, and social contacts in general (social isolation). However, it has been shown that older adults cope differently with social isolation. Therefore, the aim of the present study was to investigate the changes of social contacts of older adults over the pandemic period of 4 years.MethodsFor this purpose, N = 175 older adults (Mage = 72.60, SDage = 6.12 years, Mdnage = 72, Range: 60–87 years) were asked at 3 time points (2019, 2021, 2023) with how many people they had contact in the reference month (May, November). In addition to the number of contacts, participants were also asked about the type of the relationship (e.g., family, friends, neighbors), the type of contact (e.g., telephone, video conference and/or by written messages) and the emotional closeness (close, medium, low). We used an ego-centered “social network” circle to measure social contacts of older adults before, during and after the pandemic. The data collection was limited by the changing corona restrictions.ResultsResults indicate that behavior in social contacts essentially depends on age, gender, and level of depression. We found a clear temporal drop in social contacts independently of age and gender during the pandemic. After the pandemic close contacts did not recover to prepandemic level. Especially, Young-Old (

Abstract Background The impact of social frailty on older adults is profound including mortality risk, functional decline, falls, and disability. However, effective strategies that respond to the needs of socially frail older adults are lacking and few studies have unpacked how social determinants operate or how interventions can be adapted during periods requiring social distancing and isolation such as the COVID-19 pandemic. To address these gaps, we conducted a scoping review using JBI methodology to identify interventions that have the best potential to help socially frail older adults (age ≥65 years). Methods We searched MEDLINE, CINAHL (EPSCO), EMBASE and COVID-19 databases and the grey literature. Eligibility criteria were developed using the PICOS framework. Our results were summarized descriptively according to study, patient, intervention and outcome characteristics. Data synthesis involved charting and categorizing identified interventions using a social frailty framework. Results Of 263 included studies, we identified 495 interventions involving ~124,498 older adults who were mostly female. The largest proportion of older adults (40.5%) had a mean age range of 70-79 years. The 495 interventions were spread across four social frailty domains: social resource (40%), self-management (32%), social behavioural activity (28%), and general resource (0.4%). Of these, 189 interventions were effective for improving loneliness, social and health and wellbeing outcomes across psychological self-management, self-management education, leisure activity, physical activity, Information Communication Technology and socially assistive robot interventions. Sixty-three interventions were identified as feasible to be adapted during infectious disease outbreaks (e.g., COVID-19, flu) to help socially frail older adults. Conclusions Our scoping review identified promising interventions with the best potential to help older adults living with social frailty.

Abstract Objectives The objective of this study was to conduct a formative evaluation of the Art Your Service (AYS) arts-based program to determine the program’s potential for improving the social and physical well-being of older adults during the COVID-19 pandemic. Design, settings and participants An online questionnaire was administered to the AYS members who consented to be invited to participate in the study. Questionnaire items consisted of a Likert scale and open-ended questions delivered using an online platform (SurveyMonkey). Participants provided feedback on their perceptions and experiences of the AYS program, such as its impact on their health and well-being during the COVID-19 pandemic, the benefits and challenges of participating, and any suggestions for program improvement. Outcome measures and analysis Quantitative data were analyzed using descriptive statistics (frequencies, means with standard deviations), and open-ended questions (qualitative data) were analyzed using content analysis. Outcomes included participant demographics, perceptions about the program, usability (System Usability Scale [SUS]), eHealth literacy (eHealth Literacy Scale), and social isolation (Lubben Social Network Scale; LSNS-6). Results Program participants revealed consistent patterns of their perceptions and experiences about the program, including a high satisfaction rate (95%) and a perceived positive impact on participants’ health and well-being. The program sessions were perceived to be a well-organized, convenient, and safe way to engage with one another socially during the COVID-19 pandemic. The program usability was also perceived to be high (SUS mean score 86.2). Participants felt a sense of connectedness and had reduced feelings of social isolation. Most participants (75%) reported that the program improved their physical health. Conclusions Findings from this formative evaluation study identified key strengths and opportunities to improve the Art Your Service arts-based program, which can be used to help enhance the program’s functioning and long-term sustainability potential.

The GALEX Nearby Young Star Search (GALNYSS) yielded the identification of more than 2000 late-type stars that, based on their ultraviolet and infrared colors and pre-Gaia proper motions, are potentially of age

Additive manufacturing (AM) of ceramic bone implants from tricalcium phosphate (TCP) offers several benefits for bone regeneration and defect treatment. TCP scaffolds, e.g. featuring lattice or gyroid geometries, can effectively induce bone ingrowth and integration, showing a high potential in the treatment of large bone defects, e.g. as filler material for large bone defects. A major advantage of TCP is its osteoconductivity making it an effective choice for a broad range of orthopedic and dental applications. In addition, AM allows for the possibility to create precise, patient-specific implants with controllable mechanical properties. Those properties can be controlled by the implants' microstructure, e.g. in relation to bulk density and internal porosity. In this contribution, eleven resorbable bone implants were produced from β-tricalcium phosphate (β-TCP) in order to quantify the internal porosity in three dimensions using microcomputed tomography (μ CT). All components were manufactured using an extrusion-based process and scanned using an industrial μCT system at a voxel size of 10 μm. Two samples were physically prepared to allow a high-resolution μCT analysis at a voxel size of 1 μm. Results show that post-processed image data enables the non-destructive inspection of highly complex ceramic AM implants. Using μCT we were able to quantify internal porosity in β-TCP bone implant and quantify the geometry and distribution of wall thicknesses in the gyroid geometry. However, a detailed microstructural analysis is only possible using high-resolution μCT volume data, e.g. in relation to internal porosity. The findings emphasize that ceramic AM is able to produce complex components. However, NDT using μCT is crucial in the development of new materials and geometries. μCT provides high-resolution insights into the internal and external structure of ceramic AM components. It plays a critical role in detecting internal features, including small-scale porosity and delamination which are crucial for the integrity and functionality of medical implants. Moreover, μCT provides volumetric data that supports the design and manufacturing process at various stages, enabling an iterative approach of continuous improvement in mechanical performance and osseointegration.

BackgroundOutbreaks of dengue can overburden hospital systems, drastically reducing capacity for other care. The 2017 dengue serotype 2 (DENV-2) outbreak in Sri Lanka coincided with vaccination in an ongoing phase 3 efficacy trial of a tetravalent dengue vaccine, TAK-003 (NCT02747927). Here, we present data on the efficacy of TAK-003 following two doses of the vaccine administered 3 months apart in participants aged 4-16 years in Sri Lanka. In addition, we have used the 2017 outbreak dynamics to model the potential impact of TAK-003 on virologically confirmed dengue (VCD) cases and hospitalizations during an outbreak situation.Methodology/principal findingsModeling was performed using an age-structured, host-vector, spatial and stochastic transmission model, assuming 65% vaccine coverage and 30 days until initiation of vaccination. Efficacy of TAK-003 against VCD and hospitalized VCD cases was based on data against DENV-2 from the first year of the phase 3 trial. Vaccine efficacy and safety findings in Sri Lanka were in line with those of the overall trial population. The efficacy estimates in Sri Lanka up to the first 12 months after the second dose of TAK-003 were 94.7% and 95.7% against VCD and hospitalized VCD cases, respectively. Modeling of the trial data over an extended geographic area showed a substantial reduction in cases and a flattening of outbreak curves from TAK-003 use. The baseline vaccination scenario (initiation at 30 days, 65% target coverage, vaccine effective at 14 days, 70% hospitalization rate, VE of 95% for VCD and 97% for hospitalized VCD, and 47% for asymptomatic) resulted in a 69.1% reduction in VCD cases and 72.7% reduction in VCD hospitalizations compared with no vaccination. An extreme high scenario (vaccination initiated at Day 15, 80% coverage rate, baseline VE) resulted in 80.3% and 82.3% reduction in VCD and VCD hospitalizations, respectively. Vaccine performance, speed of vaccination campaign initiation, and vaccine coverage were key drivers in reducing VCD cases and hospitalizations.Conclusions/significanceOverall, the study and modeling results indicate that TAK-003 has the potential of meaningful utility in dengue outbreaks in endemic areas.

The question of where the inertial properties of matter come from has been open for a long time. Isaac Newton considered inertia an intrinsic property of matter. Ernst Mach held a different view whereby the inertia of a body comes from its interaction with the rest of the universe. This idea is known today as Mach’s principle. We discuss Mach’s principle based on transactional gravity, the recently developed connection of entropic gravity to the physics of quantum events, induced by transactions. It is shown that Mach’s principle holds and that there is a fundamental relation between the gravitational constant G and the total mass in the causal universe. This relationship, derived by means of entropic principles, is rigorously proven.

Despite increasing digitalization and automation, complex production processes often require human judgment/decision-making adaptability. Humans can abstract and transfer knowledge to new situations. People in production are an irreplaceable resource. This paper presents a new concept for digitizing human expertise and their ability to make knowledge-based decisions in the production area based on ontologies and causal Bayesian networks for further research. Dedicated approaches for the ontology-based creation of Bayesian networks exist in the literature. Therefore, we first comprehensively analyze previous studies and summarize the approaches. We then add the causal perspective, which has often not been an explicit subject of consideration. We see a research gap in the systematic and structured approach to ontology-based generation of causal graphs (CGs). At the current state of knowledge, the semantic understanding of a domain formalized in an ontology can contribute to developing a generic approach to derive a CG. The ontology functions as a knowledge base by formally representing knowledge and experience. Causal inference calculations can mathematically imitate the human decision-making process under uncertainty. Therefore, a systematic ontology-based approach to building a CG can allow digitizing the human ability to make decisions based on experience and knowledge.

Abstract Nature's contributions to people (NCP) are essential for the production and trade of agricultural, forestry and fishery commodities. Often, there is a spatial disconnect between consumers and the natural systems where the commodities are produced. Traded agricultural products are therefore dependent on nature and NCP in their region of origin. The dependencies of agricultural products on NCP are, however, insufficiently recognised by consumers and are rarely considered in global environmental governance and trade policies along value chains. Here, we synthesise studies highlighting dependencies of agricultural products on NCP in their origin locations to identify opportunities and challenges in quantifying their contribution in sustaining trade flows. We suggest three methodological steps for quantifying NCP dependencies in international agricultural trade: spatial mapping of NCP supply and demand, linking NCP to agricultural trade flows, and tracing trade flows. Each methodological step requires further development and harmonisation to enable a complete accounting of how international agricultural trade depends on NCP. Given the lack of knowledge and data on how NCP support agricultural trade, social and environmental trade‐offs of natural resource management are currently hard to quantify. Quantifying the role of NCP dependencies of traded agricultural products can support their sustainable management, contribute to supply chain accountability and serve as input to sustainable natural resource governance and foster responsibility and equity in supply chains. Read the free Plain Language Summary for this article on the Journal blog.

Abstract In pancreatic ductal adenocarcinoma (PDAC), endogenous MYC is required for S-phase progression and escape from immune surveillance. Here we show that MYC in PDAC cells is needed for the recruitment of the PAF1c transcription elongation complex to RNA polymerase and that depletion of CTR9, a PAF1c subunit, enables long-term survival of PDAC-bearing mice. PAF1c is largely dispensable for normal proliferation and regulation of MYC target genes. Instead, PAF1c limits DNA damage associated with S-phase progression by being essential for the expression of long genes involved in replication and DNA repair. Surprisingly, the survival benefit conferred by CTR9 depletion is not due to DNA damage, but to T-cell activation and restoration of immune surveillance. This is because CTR9 depletion releases RNA polymerase and elongation factors from the body of long genes and promotes the transcription of short genes, including MHC class I genes. The data argue that functionally distinct gene sets compete for elongation factors and directly link MYC-driven S-phase progression to tumor immune evasion.

Uma série de registros do eletrograma intracavitário tem sido utilizada para monitorização não invasiva da rejeição em pacientes transplantados usando um marcapasso de dupla câmara e eletrodos endocavitários revestidos com estrutura fractal. Os sinais têm sido avaliados usando o sistema CHARM (Computerized Heart Acute Rejection Monitoring _ sistema computadorizado para monitorização da rejeição cardíaca aguda). Os relatórios obtidos com este sistema contêm curvas com parâmetros sensíveis à rejeição, que demonstram uma boa correlação com a clínica e os resultados das biópsias convencionais. A monitorização a longo prazo, usando estas análises, mostrou ser uma ferramenta valiosa no acompanhamento destes pacientes.

As a treatment for escape-maintained problem behavior, differential reinforcement of alternative behavior (DRA) is typically initiated by reinforcing compliance on a dense reinforcement schedule. This work schedule is then progressively thinned such that the individual is required to complete more work, up to some socially acceptable terminal goal, before earning access to reinforcement. Two variations of this procedure appear in the literature but have not been compared directly. One variation involves maintaining a fixed reinforcement duration as the work requirements increase; the other involves increasing the reinforcement duration incrementally, coinciding with increases in work requirements. The current study compared these procedures with three children with intellectual and developmental disabilities who exhibited problem behavior maintained by escape from instruction. Schedule thinning evoked less problem behavior when reinforcement durations increased incrementally for all three participants. These reductions in problem behavior allowed more rapid progress toward terminal goals.

In higher education, teaching and learning are undergoing a variety of innovations that involve the use of technology through blended learning. This pedagogical approach has been popular and expanding quickly in institutions internationally with a shift in focus from the technological aspects of learning management systems to theoretical frameworks to enhance practices in a move from traditional to blended learning. This study explores the benefits, barriers, and professional development practices utilized in higher education settings to implement blended learning classes. This quantitative research analyzed the practices of blended learning as an approach to teaching and learning in higher education through survey research. This study includes additional insight and understanding of the current and future trends regarding how to surpass these barriers and enhance the overall practices of blended learning. Higher education institutions can utilize this blended learning research to develop comprehensive guidelines and increase collaboration and innovation to improve practices and move beyond the threaded discussions. This study concluded that instructors rate the quality of the educational experience in their blended learning courses as superior or very superior compared to the traditional face-to-face format of instruction and that a lack of professional development is impacting the growth and effectiveness of the blended learning environment in higher education.

Abstract Gape‐limited predators (e.g., snakes, many fish) are not generally expected to pose a predation threat to prey that are too large for them to swallow. However, the extent to which snakes predate on prey that exceed their gape limitation remains largely unknown. We conducted the first study to investigate the influence of both prey and predator sizes on the frequency of ingestion success by snakes in a natural system. We combined survival monitoring of an avian prey species (Aplonis opaca) via radio‐telemetry with a survey of the size distribution of their major predator (Boiga irregularis) on Guam. This allowed us to assess (1) the frequency of unsuccessful ingestion by the predator, (2) whether the size of the prey predicts ingestion success, (3) whether the size of the predator predicts ingestion success, and (4) the relationship between prey and predator sizes in successful ingestion attempts. We found that nearly half (47.95%) of ingestion attempts by snakes on fledgling birds were unsuccessful, and no instances where unsuccessful ingestion caused the mortality of the snake. Attempts to consume smaller fledglings were as likely to be unsuccessful as attempts to swallow larger fledglings. However, snakes that successfully ingested fledglings were among the largest snakes in the population, and larger than average conspecifics attracted to endothermic prey. The smallest snakes that successfully ingested fledglings attained remarkably high relative prey mass values for their species, consuming prey weighing up to 79.9% of their own mass. Our study indicates that B. irregularis routinely predate prey that are too large for them to successfully ingest, which causes mortality to the prey but poses little risk to the predator. The potential reward for snakes in consuming oversized prey may outweigh the inherent risks, while instances of predation that do not result in consumption may have considerable impacts on prey populations.

The integration of information technology (IT) and operational technology (OT) is deepening, amplifying the interconnectedness of operational, safety, and security demands within industrial automation systems. Lacking comprehensive guidance, risk managers often resort to manual solutions based on best practices or rely on domain experts, who usually offer insights limited to their specific areas of expertise. Given the intricate interplay among these domains, employing ontologies for knowledge representation could hold the key to capturing all necessary relationships and constraints for effective risk management processes. This study conducts a systematic mapping analysis of ontologies published over the past five years, focusing on at least one domain relevant to OT system risk management. Its objective is to categorize papers, offer a panoramic view of research themes and contributors, discern potential publication patterns, and identify research avenues based on a comprehensive review of these ontologies. Findings indicate a relatively stable research interest, with most publications presenting proof of concepts or initial experimental results for their ontological applications. This study establishes a foundation for classifying comprehensive OT ontologies and pinpoints unresolved issues that can steer future research efforts. It offers insights into the current state-of-the-art within this research area.

Industrial control systems (ICSs) play a crucial role in the smooth operation of critical infrastructures, and their increasing complexity and interconnectedness necessitate integrating safety and security measures. Thus, an integrated risk assessment approach is essential to identify and address potential hazards and vulnerabilities. However, conducting such risk assessments becomes complex and challenging due to the difficulty in data availability. Acquiring data from various sources poses a significant hurdle. To address these challenges, automation markup language (AML) provides a standardized framework that facilitates the seamless exchange of engineering information. This article uses AML libraries and connection setup techniques to generate a valuable model of a single source of data for an integrated safety and security risk assessment. The automated risk assessment employs the AML model as a data source and the Bayesian belief network (BBN) as the risk assessment method. The value of risk associated with the system is calculated using the BBN models as the product of the probability of occurrence and severity. An evaluation of the proposed risk assessment method is also provided based on ISO 31000. AML's effectiveness as a valuable information model in meeting the growing need for comprehensive safety and security risk assessment in ICSs is demonstrated.

Text-to-Image (T2I) generation is the task of synthesizing images corresponding to a given text input. The recent innovations in artificial intelligence have enhanced the capacity of conventional T2I generation, yielding more and more powerful models day by day. However, their behavior is known to become unstable in the face of text inputs containing nonwords that have no definition within a language. This behavior not only results in situations where image generation does not match human expectations but also hinders these models from being utilized in psycholinguistic applications and simulations. This paper exploits the human nature of associating nonwords with their phonetically and phonologically similar words and uses it to propose a T2I generation framework robust against nonword inputs. The framework comprises a phonetics-aware language model as well as an adjusted T2I generation model. Our evaluations confirm that the proposed nonword-to-image generation synthesizes images that depict visual concepts of phonetically similar words more stably than comparative methods. We also assess how the image generation results match human expectations, showing a better agreement than the phonetics-blind baseline.

Summarization tasks aim to summarize multiple pieces of information into a short description or representative information. A text summarization task summarizes textual information into a short description, whereas an image collection summarization task summarizes an image collection into images or textual representation in which the challenge is to understand the relationship between images. In recent years, scene-graph generation has shown the advantage of describing the visual contexts of a single-image, and incorporating external knowledge into the scene-graph generation model has also given effective directions for unseen single-image scene-graph generation. While external knowledge has been implemented in related work, it is still challenging to use this information efficiently for relationship estimation during the summarization. Following this trend, in this paper, we propose a novel scene-graph-based image-collection summarization model that aims to generate a summarized scene-graph of an image collection. The key idea of the proposed method is to enhance the relation predictor toward relationships between images in an image collection incorporating knowledge graphs as external knowledge for training a model. With this approach, we build an end-to-end framework that can generate a summarized scene graph of an image collection. To evaluate the proposed method, we also build an extended annotated MS-COCO dataset for this task and introduce an evaluation process that focuses on estimating the similarity between a summarized scene graph and ground-truth scene graphs. Traditional evaluation focuses on calculating precision and recall scores, which involve true positive predictions without balancing precision and recall. Meanwhile, the proposed evaluation process focuses on calculating the F-score of the similarity between a summarized scene graph and ground-truth scene graphs, which aims to balance both false positives and false negatives. Experimental results show that using external knowledge to enhance the relation predictor achieves better results than existing methods.

This article argues for the beneficial interconnectedness of adult basic education as an educational practice, community-based participatory research as a methodological approach, and the framework of transformative learning, for exploring and theorizing about adult learning and education. It is elaborated that these three approaches are connected by shared core values that counter the dominant economistic discourse on adult basic education. A community-based participatory research project, comprising researchers with an adult basic education learners' background, adult basic education practitioners, and the two authors as university-based researchers, serves as a local empirical example. Selected data from the research process illustrate how these three approaches complement each other and can show their inherent potential. Together, these three approaches establish a "democratic space of learning" and thus act as a "resource of hope" for education and research aimed at (self-) empowerment, emancipation, participation, and collective action toward humanization, democratization, and social justice.