2,036 research outputs found

    Personality Dysfunction Manifest in Words : Understanding Personality Pathology Using Computational Language Analysis

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    Personality disorders (PDs) are some of the most prevalent and high-risk mental health conditions, and yet remain poorly understood. Today, the development of new technologies means that there are advanced tools that can be used to improve our understanding and treatment of PD. One promising tool – indeed, the focus of this thesis – is computational language analysis. By looking at patterns in how people with personality pathology use words, it is possible to gain access into their constellation of thinking, feelings, and behaviours. To date, however, there has been little research at the intersection of verbal behaviour and personality pathology. Accordingly, the central goal of this thesis is to demonstrate how PD can be better understood through the analysis of natural language. This thesis presents three research articles, comprising four empirical studies, that each leverage computational language analysis to better understand personality pathology. Each paper focuses on a distinct core feature of PD, while incorporating language analysis methods: Paper 1 (Study 1) focuses on interpersonal dysfunction; Paper 2 (Studies 2 and 3) focuses on emotion dysregulation; and Paper 3 (Study 4) focuses on behavioural dysregulation (i.e., engagement in suicidality and deliberate self-harm). Findings from this research have generated better understanding of fundamental features of PD, including insight into characterising dimensions of social dysfunction (Paper 1), maladaptive emotion processes that may contribute to emotion dysregulation (Paper 2), and psychosocial dynamics relating to suicidality and deliberate self-harm (Paper 3) in PD. Such theoretical knowledge subsequently has important implications for clinical practice, particularly regarding the potential to inform psychological therapy. More broadly, this research highlights how language can provide implicit and unobtrusive insight into the personality and psychological processes that underlie personality pathology at a large-scale, using an individualised, naturalistic approach

    Converging organoids and extracellular matrix::New insights into liver cancer biology

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    Converging organoids and extracellular matrix::New insights into liver cancer biology

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    Primary liver cancer, consisting primarily of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), is a heterogeneous malignancy with a dismal prognosis, resulting in the third leading cause of cancer mortality worldwide [1, 2]. It is characterized by unique histological features, late-stage diagnosis, a highly variable mutational landscape, and high levels of heterogeneity in biology and etiology [3-5]. Treatment options are limited, with surgical intervention the main curative option, although not available for the majority of patients which are diagnosed in an advanced stage. Major contributing factors to the complexity and limited treatment options are the interactions between primary tumor cells, non-neoplastic stromal and immune cells, and the extracellular matrix (ECM). ECM dysregulation plays a prominent role in multiple facets of liver cancer, including initiation and progression [6, 7]. HCC often develops in already damaged environments containing large areas of inflammation and fibrosis, while CCA is commonly characterized by significant desmoplasia, extensive formation of connective tissue surrounding the tumor [8, 9]. Thus, to gain a better understanding of liver cancer biology, sophisticated in vitro tumor models need to incorporate comprehensively the various aspects that together dictate liver cancer progression. Therefore, the aim of this thesis is to create in vitro liver cancer models through organoid technology approaches, allowing for novel insights into liver cancer biology and, in turn, providing potential avenues for therapeutic testing. To model primary epithelial liver cancer cells, organoid technology is employed in part I. To study and characterize the role of ECM in liver cancer, decellularization of tumor tissue, adjacent liver tissue, and distant metastatic organs (i.e. lung and lymph node) is described, characterized, and combined with organoid technology to create improved tissue engineered models for liver cancer in part II of this thesis. Chapter 1 provides a brief introduction into the concepts of liver cancer, cellular heterogeneity, decellularization and organoid technology. It also explains the rationale behind the work presented in this thesis. In-depth analysis of organoid technology and contrasting it to different in vitro cell culture systems employed for liver cancer modeling is done in chapter 2. Reliable establishment of liver cancer organoids is crucial for advancing translational applications of organoids, such as personalized medicine. Therefore, as described in chapter 3, a multi-center analysis was performed on establishment of liver cancer organoids. This revealed a global establishment efficiency rate of 28.2% (19.3% for hepatocellular carcinoma organoids (HCCO) and 36% for cholangiocarcinoma organoids (CCAO)). Additionally, potential solutions and future perspectives for increasing establishment are provided. Liver cancer organoids consist of solely primary epithelial tumor cells. To engineer an in vitro tumor model with the possibility of immunotherapy testing, CCAO were combined with immune cells in chapter 4. Co-culture of CCAO with peripheral blood mononuclear cells and/or allogenic T cells revealed an effective anti-tumor immune response, with distinct interpatient heterogeneity. These cytotoxic effects were mediated by cell-cell contact and release of soluble factors, albeit indirect killing through soluble factors was only observed in one organoid line. Thus, this model provided a first step towards developing immunotherapy for CCA on an individual patient level. Personalized medicine success is dependent on an organoids ability to recapitulate patient tissue faithfully. Therefore, in chapter 5 a novel organoid system was created in which branching morphogenesis was induced in cholangiocyte and CCA organoids. Branching cholangiocyte organoids self-organized into tubular structures, with high similarity to primary cholangiocytes, based on single-cell sequencing and functionality. Similarly, branching CCAO obtain a different morphology in vitro more similar to primary tumors. Moreover, these branching CCAO have a higher correlation to the transcriptomic profile of patient-paired tumor tissue and an increased drug resistance to gemcitabine and cisplatin, the standard chemotherapy regimen for CCA patients in the clinic. As discussed, CCAO represent the epithelial compartment of CCA. Proliferation, invasion, and metastasis of epithelial tumor cells is highly influenced by the interaction with their cellular and extracellular environment. The remodeling of various properties of the extracellular matrix (ECM), including stiffness, composition, alignment, and integrity, influences tumor progression. In chapter 6 the alterations of the ECM in solid tumors and the translational impact of our increased understanding of these alterations is discussed. The success of ECM-related cancer therapy development requires an intimate understanding of the malignancy-induced changes to the ECM. This principle was applied to liver cancer in chapter 7, whereby through a integrative molecular and mechanical approach the dysregulation of liver cancer ECM was characterized. An optimized agitation-based decellularization protocol was established for primary liver cancer (HCC and CCA) and paired adjacent tissue (HCC-ADJ and CCA-ADJ). Novel malignancy-related ECM protein signatures were found, which were previously overlooked in liver cancer transcriptomic data. Additionally, the mechanical characteristics were probed, which revealed divergent macro- and micro-scale mechanical properties and a higher alignment of collagen in CCA. This study provided a better understanding of ECM alterations during liver cancer as well as a potential scaffold for culture of organoids. This was applied to CCA in chapter 8 by combining decellularized CCA tumor ECM and tumor-free liver ECM with CCAO to study cell-matrix interactions. Culture of CCAO in tumor ECM resulted in a transcriptome closely resembling in vivo patient tumor tissue, and was accompanied by an increase in chemo resistance. In tumor-free liver ECM, devoid of desmoplasia, CCAO initiated a desmoplastic reaction through increased collagen production. If desmoplasia was already present, distinct ECM proteins were produced by the organoids. These were tumor-related proteins associated with poor patient survival. To extend this method of studying cell-matrix interactions to a metastatic setting, lung and lymph node tissue was decellularized and recellularized with CCAO in chapter 9, as these are common locations of metastasis in CCA. Decellularization resulted in removal of cells while preserving ECM structure and protein composition, linked to tissue-specific functioning hallmarks. Recellularization revealed that lung and lymph node ECM induced different gene expression profiles in the organoids, related to cancer stem cell phenotype, cell-ECM integrin binding, and epithelial-to-mesenchymal transition. Furthermore, the metabolic activity of CCAO in lung and lymph node was significantly influenced by the metastatic location, the original characteristics of the patient tumor, and the donor of the target organ. The previously described in vitro tumor models utilized decellularized scaffolds with native structure. Decellularized ECM can also be used for creation of tissue-specific hydrogels through digestion and gelation procedures. These hydrogels were created from both porcine and human livers in chapter 10. The liver ECM-based hydrogels were used to initiate and culture healthy cholangiocyte organoids, which maintained cholangiocyte marker expression, thus providing an alternative for initiation of organoids in BME. Building upon this, in chapter 11 human liver ECM-based extracts were used in combination with a one-step microfluidic encapsulation method to produce size standardized CCAO. The established system can facilitate the reduction of size variability conventionally seen in organoid culture by providing uniform scaffolding. Encapsulated CCAO retained their stem cell phenotype and were amendable to drug screening, showing the feasibility of scalable production of CCAO for throughput drug screening approaches. Lastly, Chapter 12 provides a global discussion and future outlook on tumor tissue engineering strategies for liver cancer, using organoid technology and decellularization. Combining multiple aspects of liver cancer, both cellular and extracellular, with tissue engineering strategies provides advanced tumor models that can delineate fundamental mechanistic insights as well as provide a platform for drug screening approaches.<br/

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Protecting Privacy in Indian Schools: Regulating AI-based Technologies' Design, Development and Deployment

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    Education is one of the priority areas for the Indian government, where Artificial Intelligence (AI) technologies are touted to bring digital transformation. Several Indian states have also started deploying facial recognition-enabled CCTV cameras, emotion recognition technologies, fingerprint scanners, and Radio frequency identification tags in their schools to provide personalised recommendations, ensure student security, and predict the drop-out rate of students but also provide 360-degree information of a student. Further, Integrating Aadhaar (digital identity card that works on biometric data) across AI technologies and learning and management systems (LMS) renders schools a ‘panopticon’. Certain technologies or systems like Aadhaar, CCTV cameras, GPS Systems, RFID tags, and learning management systems are used primarily for continuous data collection, storage, and retention purposes. Though they cannot be termed AI technologies per se, they are fundamental for designing and developing AI systems like facial, fingerprint, and emotion recognition technologies. The large amount of student data collected speedily through the former technologies is used to create an algorithm for the latter-stated AI systems. Once algorithms are processed using machine learning (ML) techniques, they learn correlations between multiple datasets predicting each student’s identity, decisions, grades, learning growth, tendency to drop out, and other behavioural characteristics. Such autonomous and repetitive collection, processing, storage, and retention of student data without effective data protection legislation endangers student privacy. The algorithmic predictions by AI technologies are an avatar of the data fed into the system. An AI technology is as good as the person collecting the data, processing it for a relevant and valuable output, and regularly evaluating the inputs going inside an AI model. An AI model can produce inaccurate predictions if the person overlooks any relevant data. However, the state, school administrations and parents’ belief in AI technologies as a panacea to student security and educational development overlooks the context in which ‘data practices’ are conducted. A right to privacy in an AI age is inextricably connected to data practices where data gets ‘cooked’. Thus, data protection legislation operating without understanding and regulating such data practices will remain ineffective in safeguarding privacy. The thesis undergoes interdisciplinary research that enables a better understanding of the interplay of data practices of AI technologies with social practices of an Indian school, which the present Indian data protection legislation overlooks, endangering students’ privacy from designing and developing to deploying stages of an AI model. The thesis recommends the Indian legislature frame better legislation equipped for the AI/ML age and the Indian judiciary on evaluating the legality and reasonability of designing, developing, and deploying such technologies in schools

    Genomic insights for safety assessment of foodborne bacteria.

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    La sicurezza alimentare e l'accesso ad essa sono fondamentali per sostenere la vita e promuovere una buona salute. Gli alimenti non sicuri, contenenti microrganismi o sostanze chimiche nocive, sono causa di oltre 200 malattie, dalla diarrea al cancro, che colpiscono in particolare i neonati, i bambini piccoli, gli anziani e gli individui immunocompromessi. L'onere globale delle malattie di origine alimentare si ripercuote sulla salute pubblica, sulla società e sull'economia, pertanto è necessaria una buona collaborazione tra governi, produttori e consumatori per contribuire a garantire la sicurezza alimentare e sistemi alimentari più solidi. L'indagine più recente condotta dall'OMS (2015) ha evidenziato una stima di 600 milioni di individui malati e 420.000 decessi annui associati ad alimenti non sicuri. L'impatto economico è dovuto principalmente alla mancanza di alimenti sicuri nei Paesi a basso e medio reddito, con una perdita di 110 miliardi di dollari l'anno in termini di produttività e spese mediche. Le sfide principali per garantire la sicurezza alimentare rimangono legate alla nostra produzione alimentare e alla catena di approvvigionamento, dove fattori come la contaminazione ambientale, le preferenze dei consumatori, il rilevamento tempestivo e la sorveglianza dei focolai giocano un ruolo cruciale. Recentemente, le metodologie basate sul DNA per il rilevamento e l'indagine microbica hanno suscitato particolare interesse, soprattutto grazie allo sviluppo delle tecnologie di sequenziamento. Contrariamente ai metodi tradizionali dipendenti dalla coltura, le tecniche basate sul DNA, come il sequenziamento dell'intero genoma (WGS), mirano a risultati rapidi e sensibili a un prezzo relativamente basso e a tempi di elaborazione brevi. Inoltre, il WGS conferisce un elevato potere discriminatorio che consente di determinare importanti caratteristiche genomiche legate alla sicurezza alimentare, come la tassonomia, il potenziale patogeno, la virulenza e la resistenza antimicrobica e il relativo trasferimento genetico. La comprensione di queste caratteristiche è fondamentale per progettare strategie di rilevamento e mitigazione da applicare lungo l'intera catena alimentare secondo una prospettiva di "One Health", che porta ad acquisire conoscenze sul microbiota che influenza l'uomo, gli animali e l'ambiente. Lo scopo della tesi è quello di approfondire la genomica dei microbi di origine alimentare per la loro caratterizzazione e per creare o migliorare le strategie per la loro individuazione e i metodi di mitigazione. In particolare, questa tesi si concentra sulla valutazione del potenziale patogeno sulla base di analisi genomiche che includono studi di tassonomia, virulenza, resistenza agli antibiotici e mobiloma. Il secondo obiettivo è quello di trarre vantaggio dalle conoscenze genomiche per progettare dispositivi di rilevamento rapidi ed efficaci e metodi di mitigazione affidabili per affrontare i patogeni di origine alimentare. Più in dettaglio, saranno trattati i seguenti argomenti: La presenza di ceppi multiresistenti negli alimenti fermentati pronti al consumo rappresenta un rischio per la salute pubblica per la diffusione di determinanti AMR nella catena alimentare e nel microbiota intestinale dei consumatori. Le analisi genomiche hanno permesso di valutare accuratamente la sicurezza del ceppo UC7251 di E. faecium, in relazione alla sua virulenza e alla co-localizzazione dei geni di resistenza agli antibiotici e ai metalli pesanti in elementi mobili con capacità di coniugazione in diverse matrici. Questo lavoro sottolinea l'importanza di una sorveglianza della presenza di batteri AMR negli alimenti e di incitare lo sviluppo di strategie innovative per la mitigazione del rischio legato alla diffusione della resistenza antimicrobica negli alimenti. L'accuratezza dell'identificazione tassonomica guida le analisi successive e, per questo motivo, un metodo adeguato per identificare le specie è fondamentale. È stata studiata la riclassificazione delle specie di Enterococcus faecium clade B, utilizzando un approccio combinato di filogenomica, tipizzazione di sequenza multilocus, identità nucleotidica media e ibridazione digitale DNA-DNA. L'obiettivo è dimostrare come l'analisi del genoma sia più efficace e fornisca risultati più dettagliati riguardo alla definizione delle specie, rispetto all'analisi della sequenza del 16S rRNA. Ciò ha portato alla proposta di riclassificare tutto il clade B di E. faecium come E. lactis, riconoscendo che i due gruppi sono filogeneticamente separati, per cui è possibile definire una specifica procedura di valutazione della sicurezza, prima del loro utilizzo negli alimenti o come probiotici, compresa la considerazione per l'inclusione nella lista europea QPS. A partire da questa riclassificazione tassonomica, abbiamo sviluppato un metodo basato sulla PCR per la rapida individuazione e differenziazione di queste due specie e per discutere le principali differenze fenotipiche e genotipiche da una prospettiva clinica. A questo scopo, è stato utilizzato un allineamento del core-genoma basato sull'analisi del pangenoma. La differenza allelica tra alcuni geni del core ha permesso la progettazione di primer e l'identificazione della specie mediante PCR con una specificità del 100% e senza reattività crociata. Inoltre, i genomi clinici di E. lactis sono stati classificati come un rischio potenziale a causa della capacità di aumentare la traslocazione batterica. Gli agenti antimicrobici alternativi agli antibiotici sono una delle principali aree di sviluppo e miglioramento dell'attuale catena alimentare. Le nanoparticelle metalliche, come le nanoparticelle di platino (PtNPs), hanno suscitato interesse per le loro potenti attività catalitiche simili alle ossidasi e alle perossidasi che garantiscono forti effetti antimicrobici, e sono state proposte come potenziali candidati per superare gli inconvenienti degli antibiotici come la resistenza ai farmaci. L'obiettivo è studiare la modalità d'azione delle PtNPs in relazione alla capacità di formazione del biofilm, al meccanismo di contrasto delle specie reattive dell'ossigeno (ROS) e al quorum sensing utilizzando batteri di origine alimentare come Enterococcus faecium e Salmonella Typhimurium.Safe food and the access to it is key to sustaining life and promoting good health. Unsafe food containing harmful microorganisms or chemical substances causes more than 200 diseases, ranging from diarrhoea to cancers that particularly affect infants, young children, elderly and immunocompromised individuals. The global burden of foodborne disease affects public health, society, and economy, therefore good collaboration between governments, producers and consumers is needed to help ensure food safety and stronger food systems. The most recent survey conducted by WHO (2015) showed an estimated 600 million ill individuals and 420 000 yearly deaths associated to unsafe food. The economic impact is mainly due to the lack of safe food in low and middle income causing a US$ 110 billion is lost each year in productivity and medical expenses. The main challenges to assure food safety remain tied to our food production and supply chain, where factors like environmental contamination, consumer preferences, timely detection and surveillance of outbreaks play a crucial role. Recently, DNA-based methodologies for microbial detection and investigation have sparked special interest, mainly linked to the development of sequencing technologies. Contrary to the traditional culture-dependent methods, DNA-based techniques such as Whole Genome Sequencing (WGS) that targets fast and sensitive results at a relative low price and short processing time. Moreover, WGS confers high discriminatory power that allows to determine important genomic characteristics linked to food safety like taxonomy, pathogenic potential, virulence and antimicrobial resistance and the genetic transfer thereof. The understanding of these characteristics is fundamental to design detection and mitigation strategies to apply along the entire food-chain following a ‘One Health’ perspective, leading to gain knowledge about the microbiota that affect humans, animals, and environment. The aim of the thesis is to gain insight into the genomics of foodborne microbes for their characterization and to create or improve strategies for their detection and mitigation methods. Particularly, this thesis is focused on the assessment of the pathogenic potential based on genomic analyses including taxonomy, virulence, antibiotic resistance and mobilome studies. The second focus is to profit from the genomic insights to design rapid and time-effective detection devices and reliable mitigation methods to tackle foodborne pathogens. In more detail the following topics will be handled: The presence of multi-drug resistant strains in ready-to-eat fermented food represents a risk of public health for the spread of AMR determinants in the food chain and in the gut microbiota of consumers. Genomic analyses permitted to accurately assess the safety of E. faecium strain UC7251, with respect to its virulence and co-location of antibiotic and heavy metal resistance genes in mobile elements with conjugation capacity in different matrices. This work emphasizes the importance of a surveillance for the presence of AMR bacteria in food and to incite the development of innovative strategies for the mitigation of the risk related to antimicrobial resistance diffusion in food. The accuracy of taxonomic identification drives the subsequent analysis and, for this reason, a suitable method to identify species is crucial. The species re-classification of Enterococcus faecium clade B was investigated, using a combined approach of phylogenomics, multilocus sequence typing, average nucleotide identity and digital DNA–DNA hybridization. The goal is to show how the genome analysis is more effective and give more detailed results concerning the species definition, respect to the analysis of the 16S rRNA sequence. This led to the proposal to reclassify all the E. faecium clade B as E. lactis, recognizing the two groups are phylogenetically separate, where a specific safety assessment procedure can be designed, before their use in food or as probiotics, including the consideration for inclusion in the European QPS list. From this taxonomic re-classification, we developed a PCR-based method for rapid detection and differentiation of these two species and to discuss main phenotypic and genotypic differences from a clinical perspective. To this aim, core-genome alignment base on pangenome analysis was used. Allelic difference between certain core genes allowed primer design and species identification through PCR with 100% specificity and no cross-reactivity. Moreover, clinical E. lactis genomes categorised as a potential risk due to the ability of enhanced bacterial translocation. Antimicrobial agents alternative to antibiotics are one of the main areas of development and improvement in the current food chain. Metallic nanoparticles like Platinum nanoparticles (PtNPs), have awaken interest due to their potent catalytic activities similar to oxidases and peroxidases granting strong antimicrobial effects, have been proposed as potential candidates to overcome the drawbacks of antibiotics like drug resistance. The goal is to study the mode of action of PtNPs related to biofilm formation capacity, reactive oxygen species (ROS) coping mechanism and quorum sensing using foodborne bacteria like Enterococcus faecium and Salmonella Typhimurium

    Comparing the production of a formula with the development of L2 competence

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    This pilot study investigates the production of a formula with the development of L2 competence over proficiency levels of a spoken learner corpus. The results show that the formula in beginner production data is likely being recalled holistically from learners’ phonological memory rather than generated online, identifiable by virtue of its fluent production in absence of any other surface structure evidence of the formula’s syntactic properties. As learners’ L2 competence increases, the formula becomes sensitive to modifications which show structural conformity at each proficiency level. The transparency between the formula’s modification and learners’ corresponding L2 surface structure realisations suggest that it is the independent development of L2 competence which integrates the formula into compositional language, and ultimately drives the SLA process forward

    Vocational tertiary education of young adults in Kenya: model development

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    The purpose of this study is to create a model of tertiary vocational education in Kenya. Despite considerable progress in Kenya over the last 20 years, current education models, low attendance, and low academic proficiency levels preclude many vulnerable learners from becoming employable. Utilizing semi-structured interviews of eight Kenyan participants, this study explores the testable design principles necessary to create such a micro-trade model. Utilizing Epistemic Network Analysis (ENA), a quantitative ethnographic technique, to model the structure of connections in data, this study attempts to systematically identify a set of constructs, as they are recorded in interview codes, connected to one another within these interviews. Two intellectual parallels emerged pertaining to the lack of fundamental and essential needs many Kenyans experience as well as salient issues of corruption often hindering the development of Kenya\u27s politics, economy, and democracy. It was imperative that a targeted approach to education was maintained and underpinned the trajectory of the micro-trade model when identifying the design principles for this study. This study reports the finding that a fresh model of tertiary vocational education, micro-trade, could impact the ability of vulnerable youth to become economically independent. It proposes such a model appearing in Chapter 4, schematizing barriers to tertiary education, microtrade as a response to those barriers through the lens of Kirkpatrick\u27s model of education, and aspirational results from careful design and blend of Kirkpatrick\u27s model with microtrade. Such design and blending through design-based research constitute proposed next steps for this effort
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