Affiliation Networks Research Articles

Physician knowledge, practice patterns, and barriers encountered regarding guideline-concordant use of bone modifying agents for prostate cancer.

Guidelines recommend bone-modifying agents (BMAs) for patients with castrate-resistant prostate cancer (CRPC) and bone metastasis, but not for castrate-sensitive prostate cancer (CSPC). Physicians beliefs and practices regarding BMA therapy are poorly understood. This was a qualitative interview study with embedded Likert-scale elements. Study participants were physicians who treat prostate cancer, located within an academic cancer center or an affiliated community-based network. Participants were asked about their experiences and practice patterns regarding BMA therapy. Participants used Likert-scale items to identify the most common barriers to guideline-concordant BMA use and the most effective potential interventions. Participants were subsequently asked to rank the three most common barriers and the three most effective interventions to reduce underuse (for CRPC) and overuse (for CSPC). Nineteen physicians were invited and 15 participated; one physician did not answer some questions as outside of their practice scope. All were aware of the recommendation for BMAs in CRPC. 14% (2/14) were unaware of the recommendation against BMA use for CSPC; an additional 29% (4/14) believed that BMA use could be appropriate for CSPC depending on the metastatic disease burden. 36% (5/14) were unaware of recommendations for screening and treatment of low bone mineral density. The most common barriers (occurring "often" or "sometimes") were obtaining dental clearance (11/15) and insufficient clinic time (6/15). The interventions identified as most effective to reduce underuse were dental navigation (11/15) and electronic medical record (EMR)-based guidance (9/15). The interventions identified as most effective to reduce overuse were peer-to-peer education (14/15) and EMR-based guidance (13/15). Awareness of guideline recommendations for screening and treatment of low bone mineral density and against BMA use for CSPC was good, but not complete. Dental navigation, peer-to-peer education, and EMR-based guidance were preferred intervention strategies to improve guideline-concordant use.

Controversies and progress on standardization of large-scale brain network nomenclature.

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.

Collaboration and growth in a large research cooperative: A network analytic approach

Research networks encourage team science and facilitate collaboration within and across research teams. While many analyses have examined the output of these collaborative networks (e.g., authorship networks, publications, grant applications), less attention has been paid to the formative phases of these initiatives. This article presents analyses of a whole-network survey of investigators participating in a new research initiative, and examines the development of collaborative ties over the network’s first year. In particular, we examine the influence of research center affiliation, seniority, and prior network experience on the number and structure of collaborative ties, including participants’ bridging and broker roles. Such analyses can inform the overall management of the project in purposefully promoting new collaboration opportunities, and may ultimately predict the number of collaborative products generated by the network members.

The role of farmer networks in supporting adaptive capacity: Opening the door for innovation and transformation in the Northeastern United States

This article explores the role of farmer networks in building the adaptive capacity of small and diversified farmers in the Northeastern United States. Previous research suggests that farmers’ networks are the backbone of practical agricultural knowledge systems in the United States, serving as a critical venue where growers exchange and negotiate new ideas. Drawing upon empirical evidence from a regional survey on climate resilience and a series of focus groups conducted in collaboration with 9 farmer organizations from Pennsylvania to Eastern Canadian provinces, this article examines how the emergence of new ideas and agroecological innovations are influenced by geography, network affiliation, and perceived agency. First, we use regression analysis to identify factors that influence the use of no-till on diversified vegetable and berry farms, which is an emerging innovation in this community. Our analysis shows that geography may not be a significant driver of adoption among the population we sampled, which contrasts with previous research on explanatory factors, yet affiliation with certain farmer networks was significant in predicting the use or intended use of the practice. This quantitative analysis is complemented by qualitative data from a series of focus groups in which farmers identify the characteristics of certain networks which support them in addressing new challenges. Farmers identified that networks support them in learning about new ideas, accessing resources, and engaging in creative problem-solving, through facilitation of spaces for exchange with peers and experts and being responsive to their emerging needs.

Twenty-two years of International Journal of Hospitality Management: A bibliometric analysis 2000–2021

The International Journal of Hospitality Management is one of the leading scientific journals publishing high-quality research applied to the hospitality industry since 1982. The current paper aims to provide a detailed analysis of the impact of IJHM in the last twenty-two years, using Data Science techniques, namely Data Visualization, Text Mining, and Network theory. This research is based on a comprehensive bibliometric review, including top keywords, top terms, top productive and influential authors, top productive countries/regions, researched topics, topics publication trends, most cited and influential papers, author networks, keywords networks, and affiliation networks. The analysis highlighted several interesting facts, such as (1) the impact of open-access papers not differing from regular papers, (2) sustainability and online reviews are among the most cited topics, and (3) there is an increase in the diversity of topics being published. These findings are essential for authors considering publishing in IJHM and for the journal’s editors.

Modeling and analysis of affiliation networks with preferential attachment and subsumption.

Preferential attachment describes a variety of graph-based models in which a network grows incrementally via the sequential addition of new nodes and edges, and where existing nodes acquire new neighbors at a rate proportional to their degree. Some networks, however, are better described as groups of nodes rather than a set of pairwise connections. These groups are called affiliations, and the corresponding networks affiliation networks. When viewed as graphs, affiliation networks do not necessarily exhibit the power law distribution of node degrees that is typically associated with preferential attachment. We propose a preferential attachment mechanism for affiliation networks that highlights the power law characteristic of these networks when presented as hypergraphs and simplicial complexes. The two representations capture affiliations in similar ways, but the latter offers an intrinsic feature of the model called subsumption, where an affiliation cannot be a subset of another. Our model of preferential attachment has interesting features, both algorithmic and analytic, including implicit preferential attachment (node sampling does not require knowledge of node degrees), a locality property where the neighbors of a newly added node are also neighbors, the emergence of a power law distribution of degrees (defined in hypergraphs and simplicial complexes rather than at a graph level), implicit deletion of affiliations (through subsumption in the case of simplicial complexes), and to some extent a control over the affiliation size distribution. By varying the parameters of the model, the generated affiliation networks can resemble different types of real-world examples, so the framework also serves as a synthetic generation algorithm for simulation and experimental studies.

Examining the effect of sociodemographic factors on feral horses' social networks

AbstractThe benefits of sociality are not equitably shared among members of a social group. The position individuals occupy in social networks is key to maximizing their fitness and contribution to group cohesion, as well as stability in the network structure. Individuals' position in a social network is highly influenced by intra‐group competition and affiliation; therefore, it should be assessed by examining various spatial‐social variables. In this study, we examined the relationship between proximity, grooming and agonistic networks, controlling for genetic relatedness, and the effects of sociodemographic factors on the strength centrality (SC) of these social networks. We combined drone technology and social network analysis to test several hypotheses on 16 feral horse (Equus ferus caballus) groups during three consecutive breeding seasons. Our results show a clear relationship between spatial and social behaviours, suggesting that proximity and grooming are intimately connected in promoting social bonding, and agonism may be a consequence of close proximity between conspecifics. Sociodemographic factors shaped the three network centralities differently. Females and higher‐ranking individuals are more central in spatial networks, whereas younger ones may benefit from higher centrality in affiliative networks. Newly integrated individuals tend to be peripheral on spatial networks and engage more in agonistic behaviours; corroborating that integration into a new group is a costly process for females, ultimately leading to a decreased foaling rate. Individuals in multi‐male groups showed higher SC in proximity, but not in agonistic networks, suggesting that a second male may play a role in decreasing overall intra‐group competition. Our results provide a step forward in understanding the costs and benefits of network centrality in non‐matrilineal societies and mechanisms leading to social cohesion, namely in populations under the constant threat of predation and human pressures.

Parliamentary political parties in Poland in the face of the climate change issue in the years 2005–2019 (perspective of social networks)

The aim of the article was to define the attitude of Polish parliamentary political parties towards the issue of climate change in the years 2005–2019. Discourse Network Analysis, used in the study, allowed to identify the most frequently co-occurring concepts expressed by different parties over the past fifteen years, as well as, to present the affiliation networks between the parties. The postulates regarding climate policy proposed by Polish election committees were gradually modified in the analyzed period. The gradual convergence of pro-climate positions of Polish parliamentary parties is visible over time, while the structure of anti-climate positions becoming less coherent. However, there is still a visible difference in the approach to climate protection between the centrist and left-wing parties and those identifying themselves as right-wing.

Banking across borders: Are Chinese banks different?

Chinese banks have become the largest cross-border creditors for almost half of all emerging market and developing economies (EMDEs). While they look similar to other EMDE banks in terms of ownership and balance-sheet structure, their global cross-border lending resembles that of banks from advanced economies along several dimensions, especially when lending to EMDEs. We find that geographical distance poses a barrier for cross-border lending, including for Chinese banks. For them, given their network of affiliates, this barrier is lower than for other EMDE banks, more like US or European banks. We show that across all bank nationalities, bilateral economic interactions, like trade, FDI and portfolio investment, all positively correlate with cross-border lending. What stands out is that Chinese banks’ lending to EMDEs correlates more than any other nationality with trade, but there is no such correlation with FDI and, unlike all other banks, their lending correlates negatively with portfolio investment.

Interconnections between Minister Cabinets in Greece. A Bicentennial Study with Implications on Economy

In this paper we deal with a network analysis of interconnected cabinets in Greece for an extended time period. In parallel, we present a small review of the economic crises that have occurred in Greece over this period. More particularly, we used historical sources to locate all different Greek governments and all economic crises starting from the 1821 Greek revolution to the present days. We also formed a two-mode (also known as affiliation) network of ministers and cabinets and subsequently created a network of interconnected cabinets. We used dedicated software to visualize this network and used Social Network Analytical procedures in order to calculate its properties. Finally, in an attempt to investigate possible relations between network metrics and economic crises, we note and discuss an interesting observation between a specific metrics and such major economic events. In our paper, we firstly introduce the context and present our research questions. We then present the relevant literature, mainly discussing the extent to which Social Network Analysis has been used to investigate patterns of behaviors in politics. We then proceed to presenting and applying our methodology on network creation, visualization and metrics computations. The following section discusses the longitudinal evolution of our network and the relation between its clustering coefficient and the emergence of economic crises. We then finalize our paper with some conclusions.

The Empirical Determinants of Violent Nonstate Actor Drone Adoption

Commercial drone advancements have enabled terrorists with crude airpower, challenging states’ aerial dominance. Today, many groups skillfully use drones for propaganda generation, surveillance and reconnaissance, command and control, and attacks. Despite their obvious value, there is wide variation in adoption begging questions about who is using drones and why. Prominent in practitioner and security provider circles, academics are just skimming the surface of this important phenomenon. The small existing literature suggests that violent nonstate actor drone use is little more than Iran-sponsored jihadist terrorists with territory in the Middle East. Using an original data set on characteristics across 998 armed nonstate groups from 1995 to 2019, we explore the empirical determinants of drone adoption. Although Iran-sponsorship is a significant factor, we find that network affiliations are the strongest predictors of adopting a drone program. We also demonstrate that groups with more intensive attack profiles and narco groups are more likely to pursue unmanned aerial systems. Our study provides the first quantitative probe of the drivers of armed nonstate drone use, putting academic assertions and policy prescriptions on firmer empirical ground.

An Image Recommendation Algorithm Based on Target Alternating Attention and User Affiliation Network

Currently, how to exploit the deep features of images in image recommender systems to achieve image enhancement still needs further research. In addition, little research has explored the implicit and increasing preferences of users by using the affiliation generated by indirect users and virtual users of the main users, which leads to the phenomenon of information cocoon. An Image Recommendation Algorithm Based on Target Alternating Attention and User Affiliation Network (TAUA) is proposed in this paper that addresses the problems of inadequate extraction of semantic features in an image and information cocoon in image recommender systems. First, to complete the multi-dimensional description of the image, we extract the category, color, and style features of the image through a multi-channel convolutional neural network (MCNN), and we then perform migration and integration on these features. Then, to enhance the pixel-level representation ability of the image and achieve image feature enhancement, we propose target alternating attention to capture the information of surrounding pixels alternately from inside to outside. Finally, a user affiliation network, including indirect users and virtual users, is established according to the user behavior and transaction record, and the users’ increasing preferences and affiliated users are mined through the implicit interaction relationship of users. Experimental results show that compared with baselines on the Amazon dataset, the results of F@10, NDCG@10, and AUC of the proposed algorithm are 4.02%, 5.00%, and 2.14% higher than those of ACF, and 5.76%, 0.86% and 1.16% higher than those of VPOI. On the Flickr dataset, our algorithm outperforms ACF by 5.74%, 5.12%, and 3.68% in F@10, NDCG@10, and AUC, respectively, and outperforms VPOI by 0.45%, 0.47%, and 0.49%. TAUA has better recommendation performance and can significantly improve the recommendation effect.

Evaluating Advancements in Forensic Medicine and Toxicology Through a Scientometric Mapping

The study focused on using scientometric methods to analyze forensic medicine and toxicology literature. The selected period for the study was from 2013 to 2022, allowing for a recent and comprehensive field analysis. The study used bibliographic data from a PubMed database, a widely used biomedical literature database. The study aimed to provide a comprehensive picture of the production and dissemination of the literature in the field. To achieve this, the study analyzed various bibliometric indicators, including theme analysis, keyword evolvement, and collaboration map. The analysis of trending topics and future thematic directions in the field was also conducted to provide insight that could inform and stimulate the research interests of young researchers. This scholarly article explains the construction of author networks, the assessment of author productivity yearly, and the analysis of affiliation networks and organizations. The number of articles published by an institution is an essential indicator of research productivity and expertise in the particular field. The study's findings provided valuable insights into the trends and patterns in forensic medicine and toxicology.

Abstract OT1-12-02: Preliminary report of the PRECISION Trial (Profiling Early Breast Cancer for Radiotherapy Omission): A Phase II Study of Breast-Conserving Surgery Without Adjuvant Radiotherapy for Favorable-Risk Breast Cancer

Abstract Background: Breast conserving surgery (BCS) is typically followed by adjuvant radiotherapy (RT) based on several landmark trials demonstrating improvements in disease control and survival. Since completion of these historical trials, the advent of molecular subtyping has revealed that breast cancer is not a single disease entity, but rather a class of cancers with differential risk profiles. We evaluated whether RT could be safely omitted following BCS for patients with the most favorable subtype as defined by the Prosigna PAM50 assay. Methods: We conducted a multicenter prospective single-arm cohort study with IRB approval and an FDA investigational device exemption (IDE). Eligible patients were women 50 to 75 years of age (inclusive) who had undergone BCS revealing tumors ≤2cm in size, that were estrogen or progesterone receptor positive (HR+), HER2 negative, grade 1-2, node negative (N0), with negative excision margins (no ink on tumor). Intent to take endocrine therapy was required. Upon registration, tumors were submitted for central Prosigna testing and those with Risk of Recurrence (ROR) score ≤40 were deemed eligible for the investigational omission of RT. The primary endpoint was the 5-year locoregional recurrence rate (LRR). Anticipating a total of 345 RT-omitting patients to enroll over 3.5 years, the study was designed with 90% power to exclude a 5-year LRR of 5% using a one-sample exponential test with one-sided type I error of 0.025. Results: From 2016 to 2020, 671 patients were registered from 13 centers, inclusive of affiliated regional network sites. Of these, 382 patients had a ROR Score ≤40 and opted to forego RT, comprising the main intention-to-treat (ITT) study population. Median age was 65 years (range 50 to 75), and median tumor size was 0.9 cm (range 0.1 to 2.0 cm). At a median follow-up of 26.9 months, 12 events were observed: 4 patients had ipsilateral in-breast recurrences, 7 had contralateral breast cancers, and 1 developed an unrelated melanoma. There were no regional-nodal or distant recurrences. The 2-year cumulative rate of LRR was 0.3% (95% CI: 0 – 1.0%). Of the 4 ipsilateral breast recurrences, 2 were in the same quadrant as the original primary tumor. Conclusion: In this preliminary report of the PRECISION trial, patients 50-75 years of age undergoing BCS and endocrine therapy for pT1N0 HR+ HER2-negative breast cancer with ROR score ≤40 had exceedingly low rates of LRR in the absence of adjuvant RT at a median follow-up of 26.9 months. Additional follow-up is required to determine whether these favorable results are durable. Citation Format: Lior Z. Braunstein, Julia Wong, Deborah A. Dillon, Yu-Hui Chen, Paul Catalano, Oren Cahlon, Mahmoud B. El-Tamer, Rachel Jimenez, Atif Khan, Carmen Perez, Rinaa Punglia, Ron Shiloh, Laura Warren, David Wazer, Jean Wright, Elizabeth Buckley, Tari King, Simon Powell, Eric Winer, Jennifer Bellon. Preliminary report of the PRECISION Trial (Profiling Early Breast Cancer for Radiotherapy Omission): A Phase II Study of Breast-Conserving Surgery Without Adjuvant Radiotherapy for Favorable-Risk Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr OT1-12-02.

Barriers to guideline-concordant use of bone modifying agents for prostate cancer.

68 Background: NCCN Guidelines recommend the use of bone-modifying agents (BMAs) to prevent skeletal-related events (SRE) for patients with castrate-resistant prostate cancer (CRPC) and bone metastasis, but not for castrate-sensitive prostate cancer (CSPC). Prior studies have identified both underuse of BMAs for CRPC and overuse for CSPC, but the clinical circumstances underlying these apparent gaps in care are unknown. Methods: Qualitative interview study, with physicians who treat prostate cancer within an academic cancer center and an affiliated network of community-based practices. Using a semi-structured interview guide, an experienced moderator probed participants’ experiences and perceptions around NCCN Guidelines recommendations, guideline adherence and non-adherence, and barriers to adherence. Interviews also probed participants’ views of potential interventions to promote guideline-concordant BMA use. Participants used Likert-scale items to rate the likely effectiveness of each intervention in influencing BMA practice patterns. They also identified the 3 most helpful interventions for reducing BMA underuse and overuse separately. Results: 19 physicians were invited, of whom 15 agreed to participate; 1 physician did not respond to some questions as outside scope of practice. All were aware of the recommendation for use of BMAs in CRPC. 14% (2/14) were unaware of the recommendation against BMA use for CSPC; an additional 29% (4/14) believed that BMA use could be appropriate for CSPC depending on the burden of bony metastatic disease. 36% (5/14) were unaware of recommendations for baseline DEXA scan and BMA for patients with low bone mineral density. The most commonly reported barriers (occurring “often” or “sometimes”) to BMA use for CRPC were obtaining dental clearance (11/15) and insufficient time in clinic (6/15). The interventions perceived as most helpful to reduce underuse for CRPC were dental navigation (11/15) and EMR-based guidance (9/15). The interventions identified as most helpful to reduce overuse for CSPC were peer-to-peer education (14/15) and EMR-based guidance (13/15). Conclusions: Among physicians treating prostate cancer in our study, there was incomplete awareness of guideline recommendations for screening and treatment of low bone mineral density, and against BMA use for SRE prevention in CSPC. Dental navigation, peer-to-peer education, and EMR-based guidance were preferred implementation strategies to reduce underuse and overuse of BMAs.

ADSAttack: An Adversarial Attack Algorithm via Searching Adversarial Distribution in Latent Space

Deep neural networks are susceptible to interference from deliberately crafted noise, which can lead to incorrect classification results. Existing approaches make less use of latent space information and conduct pixel-domain modification in the input space instead, which increases the computational cost and decreases the transferability. In this work, we propose an effective adversarial distribution searching-driven attack (ADSAttack) algorithm to generate adversarial examples against deep neural networks. ADSAttack introduces an affiliated network to search for potential distributions in image latent space for synthesizing adversarial examples. ADSAttack uses an edge-detection algorithm to locate low-level feature mapping in input space to sketch the minimum effective disturbed area. Experimental results demonstrate that ADSAttack achieves higher transferability, better imperceptible visualization, and faster generation speed compared to traditional algorithms. To generate 1000 adversarial examples, ADSAttack takes 11.08s and, on average, achieves a success rate of 98.01%.

Distributed Approaches to Butterfly Analysis on Large Dynamic Bipartite Graphs

Tip decomposition has a pivotal role in mining cohesive subgraphs in bipartite graphs by computing the tip number of each vertex in accordance with the non-trivial motif butterfly ((2,2)-biclique). It has been a popular research topic with applications in document clustering, spam group detection, and analysis of affiliation networks. In such applications, the graphs are not only large, but they evolve quickly with new edges being continuously added/deleted. While existing centralized techniques could solve the tip decomposition problem for static bipartite graphs, they are not efficient for maintaining the tip numbers of vertices on large-scale graphs. In this paper, we study butterfly analysis problems on bipartite graphs in a distributed environment with the vertex-centric model. We first extend a centralized butterfly counting algorithm to a distributed version, called DBCA. An ingenious message aggregation strategy is designed to reduce massive redundant messaging and avoid the memory overflow problem while processing large-scale graphs. Based on the results of DBCA, we develop a distributed tip decomposition algorithm (DTDA) to get the tip number of each vertex in parallel. Finally, to maintain the tip numbers of vertices efficiently while graphs evolve over time, we explore a distributed tip maintenance algorithm (DTMA) along with a novel task-split strategy. Specifically, for an updated edge (insertion/deletion), several sub-tasks will be generated in line with the topology structure of the original bipartite graph. To our best knowledge, this is the first study to process the butterfly analysis problems in a distributed environment. In addition, comprehensive experiments have been conducted on real-world bipartite graphs. The experiment results demonstrate that our proposed algorithms are efficient and scalable.

Necessary change: What student success centers can do to support more equitable student success

AbstractAddressing the Student Success Center Network's newly adopted statement on racial equity and economic recovery, this chapter describes actions the student success centers and their affiliated college networks are taking to address policies and practices that perpetuate systemic inequalities and recommends additional steps to enhance educational and economic outcomes for racially minoritized students.

Establishing an effective partnership network in the sports paradigm

The paper offers a theoretical overview based on strategic approaches to the establishment of an effective partnership network to help the adaptability of sports clubs to modern competitive and everchanging conditions. Methodologically, the article is conceptual, based on contemporary data and multi-perspective development directions. Initially, the article indicates the importance of establishing affiliate networks in business. Subsequently, presents strategies for creating long-term relationships with diverse organizations and guidelines for practical implementation. Sports organizations should analyse their resources and potential future partners and choose the most appropriate practices to connect with them. Thus, they would strengthen their effectiveness of strategic club cooperation.

APRENDIZAGEM AFETIVA E INCLUSIVA DE LÍNGUAS PELO WHATSAPP: UM ESTUDO DE CASO ENTRE UM GRUPO DE PROFESSORES ESTAGÍARIOS E ALUNOS DE ITALIANO COM FORMAÇÃO PLURILÍNGUE E PLURICULTURAL

ABSTRACT The aim of the present study is twofold: 1 to investigate the strategies used by a group of trainee teachers during the implementation via WhatsApp of pre-task activities inspired by the autobiographical approach; 2. to analyse whether and how learners show emotional engagement through online messages, referred here as the ability to express their own personal sphere in the host country’s language. The case study consists of the observation of 900 WhatsApp messages sent over a period of 39 days, aimed at carrying out 9 pre-task activities to enhance the intra-personal emotional sphere. The data were collected during an Italian L2 course designed for students of A2 level according to Common European Framework of Reference for languages (CEFR) with a multilingual and multicultural background and taught by trainee teachers. The quantitative-qualitative survey, carried out on the basis of studies analysing digital communication and supported by the autobiographical approach, shows that pre-tasks offered the community of practice the opportunity to go beyond the teaching activity and to build an affiliate network with emotionally engaging communicative spaces, enriched by iconographic material and stylistically and linguistically refined life stories. From a professional perspective, the study shows the path taken by trainee teachers to acquire skills and competences – stylistic, pragmatic, linguistic, interactional and emotional – necessary to foster forms of affective and inclusive language learning through multimodal resources.