Examining the relationship between electoral violence, State security and democratization in Ghana's Fourth Republic.

Electoral violence has become a recurring challenge in Africa, posing a significant threat to democracy, political stability, and human security. This paper explores the relationship between state security and electoral violence in the African context and draws valuable lessons for Ghana. The objectives of this study include investigating the causes and dynamics of electoral violence in Africa, analyzing the role of state security agencies in preventing and mitigating electoral violence, assessing lessons that Ghana could learn from other African countries' experiences, and proposing recommendations to enhance state security and prevent electoral violence in Ghana's Fourth Republic. The paper adopted an explanatory sequential mixed method design. The approach combined the quantitative data analysis and qualitative data collection through literature reviews, interviews, and focus group discussions. By identifying causes and dynamics of electoral violence, assessing the performance of state security agencies, and drawing lessons from successful strategies employed in other African countries. The aim of the study is to add to the existing literature the development of effective measures for ensuring peaceful and fair elections in Ghana. The results reveal a positive and direct relationship between electoral violence and democratization. Furthermore, the research discloses a positive and direct relationship between electoral violence and state security. Finally, the results reveal that state security has an intermediating effect between electoral violence and democratization. The research findings will inform policymakers, election officials, and relevant stakeholders in designing strategies to reduce electoral violence and promote democratic processes in Ghana and across the African continent. Based on these, the study recommends the provision of trusted security, a constitutional review, and an increment of trust in the electoral space.

ChatGPT and Medicine: Together We Embrace the AI Renaissance.

The generative artificial intelligence (AI) model ChatGPT holds transformative prospects in medicine. The development of such models has signaled the beginning of a new era where complex biological data can be made more accessible and interpretable. ChatGPT is a natural language processing tool that can process, interpret, and summarize vast data sets. It can serve as a digital assistant for physicians and researchers, aiding in integrating medical imaging data with other multiomics data and facilitating the understanding of complex biological systems. The physician's and AI's viewpoints emphasize the value of such AI models in medicine, providing tangible examples of how this could enhance patient care. The editorial also discusses the rise of generative AI, highlighting its substantial impact in democratizing AI applications for modern medicine. While AI may not supersede health care professionals, practitioners incorporating AI into their practices could potentially have a competitive edge.

Koina: Democratizing machine learning for proteomics research.

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

Augmenting the landscape of chimeric antigen receptor T-cell therapy.

INTRODUCTION: The inception of recombinant DNA technology and live cell genomic alteration have paved the path for the excellence of cell and gene therapies and often provided the first curative treatment for many indications. The approval of the first Chimeric Antigen Receptor (CAR) T-cell therapy was one of the breakthrough innovations that became the headline in 2017. Currently, the therapy is primarily restricted to a few nations, and the market is growing at a CAGR (current annual growth rate) of 11.6% (2022-2032), as opposed to the established bio-therapeutic market at a CAGR of 15.9% (2023-2030). The limited technology democratization is attributed to its autologous nature, lack of awareness, therapy inclusion criteria, high infrastructure cost, trained personnel, complex manufacturing processes, regulatory challenges, recurrence of the disease, and long-term follow-ups. AREAS COVERED: This review discusses the vision and strategies focusing on the CAR T-cell therapy democratization with mitigation plans. Further, it also covers the strategies to leverage the mRNA-based CAR T platform for building an ecosystem to ensure availability, accessibility, and affordability to the community. EXPERT OPINION: mRNA-guided CAR T cell therapy is a rapidly growing area wherein a collaborative approach among the stakeholders is needed for its success.

From Ostalgie To Ostodium. The Anti-Communist Novel in Post-1989 East-Central Europe1.

This study seeks to theorize the post-communist anti-communist novel as a distinct and productive genre in East-Central European literatures, which we describe - in polemic with the better-known ostalgie - as a narrative of ostodium. We argue that anti-communist fiction became a cohesive genre in post-communism owing to its rigid view of the past, which was kept alive and significant, while simultaneously being antagonized, even after communism had collapsed. To that end, we explain how the anti-communist mindset assumed by intellectuals from the region during communism (which had then been branded as 'anti-politics') maintained monopoly over post-communist cultural production, and merged with ascending post-communist neoliberalism that promoted an anti-statist public mythology. We further outline the shifting shapes in which the ideological bias of the post-communist anti-communist novel was conveyed, and draw distinctions from proximate genres, such as the political novel, le roman á la thèse and historiographic metafiction. One crucial argument in this respect regards the postmodern entanglements of the post-communist anti-communist novel: in maintaining an univocal rejection of the communist metanarrative, they took on a stronger political thèse than in Western postmodernism, but also enhanced postmodernism's anti-realist drive by failing to provide an understanding of the post-communist present.

The Pulse of AI: Implementation of Artificial Intelligence in Healthcare and its Potential Hazards.

In this editorial, we explore the existing utilization of artificial intelligence (AI) within the healthcare industry, examining both its scope and potential harms if implemented and relied upon on a broader scale. Collaboration among corporations, government bodies, policymakers, and medical experts is essential to address potential concerns, ensuring smooth AI integration into healthcare systems.

Can digital health democratize health care?

Much has been said about the potential of digital health technologies for democratizing health care. But how exactly is democratization with digital health technologies conceptualized and what does it involve? We investigate debates on the democratization of health care with digital health and identify that democratization is being envisioned as a matter of access to health information, health care, and patient empowerment. However, taking a closer look at the growing pool of empirical data on digital health, we argue that these technologies come short of materializing these goals, given the unequal health outcomes they facilitate. Building on this evidence, we argue that not only debates on democratization need to be connected to concerns of social determinants of health but also debates on the impact of digital health need to go far beyond democratization and engage with concerns of health justice.

The Wrong Place at the Wrong Time? Territorial Autonomy and Conflict During Regime Transitions.

This article evaluates how territorial autonomy affects ethnic mobilization and conflict during regime transitions. Previous research has highlighted its conflict-inducing role during prominent transition contexts. Alternatively, it has shown its pacifying role in the "average" case, without distinguishing transition periods from stable contexts. Addressing these gaps, we argue that the de-escalatory consequences of autonomy depend on critical stabilizing factors which are themselves "muted" during transitions. We test our expectations in a cross-national analysis, covering all regime transitions between 1946 and 2017. We also revisit the 1989 transition from Communism, focusing on the role of "inherited" autonomy in the post-communist successor states. This enables us to address concerns whereby autonomy is offered to ward off transitions or whereby transitions are themselves induced by mobilization. Our findings indicate that during transitions, territorial autonomy increases the likelihood of ethnic mobilization, government concessions in response, and violent escalation where these are not forthcoming.

Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial.

Deep learning-based clinical imaging analysis underlies diagnostic artificial intelligence (AI) models, which can match or even exceed the performance of clinical experts, having the potential to revolutionize clinical practice. A wide variety of automated machine learning (autoML) platforms lower the technical barrier to entry to deep learning, extending AI capabilities to clinicians with limited technical expertise, and even autonomous foundation models such as multimodal large language models. Here, we provide a technical overview of autoML with descriptions of how autoML may be applied in education, research, and clinical practice. Each stage of the process of conducting an autoML project is outlined, with an emphasis on ethical and technical best practices. Specifically, data acquisition, data partitioning, model training, model validation, analysis, and model deployment are considered. The strengths and limitations of available code-free, code-minimal, and code-intensive autoML platforms are considered. AutoML has great potential to democratize AI in medicine, improving AI literacy by enabling "hands-on" education. AutoML may serve as a useful adjunct in research by facilitating rapid testing and benchmarking before significant computational resources are committed. AutoML may also be applied in clinical contexts, provided regulatory requirements are met. The abstraction by autoML of arduous aspects of AI engineering promotes prioritization of data set curation, supporting the transition from conventional model-driven approaches to data-centric development. To fulfill its potential, clinicians must be educated on how to apply these technologies ethically, rigorously, and effectively; this tutorial represents a comprehensive summary of relevant considerations.

Health Data Democratization in Austria: Patients' Perspective.

Health data democratization requires a transparent, protected, and interoperable data-sharing environment. We conducted a co-creation workshop with patients living with chronic diseases and relevant stakeholders to explore their opinion on health data democratization, ownership, and sharing in Austria. Participants showed their willingness to share their health data for clinical and research purposes; provided that appropriate transparency and data protection measures are provided.

[Participatory approaches in age(ing) research : Definitions, fields of application and challenges in different research stages]. / Partizipative Alter(n)sforschung : Definitionen, Anwendungsfelder und Herausforderungen in unterschiedlichen Forschungsphasen.

There is much to be gained from participatory research: it can increase the closeness of research to everyday life, the acceptance of the resulting practical implications and holds the potential to fundamentally democratize scientific knowledge production. It is not surprising that this is not without irritation on the part of academic researchers and their institutional environment as well as on the part of nonacademically trained co-researchers. Based on an inspection of the relevant literature this article outlines the different understanding and definitions of participatory age(ing) research, its current fields of application, and utilization in different phases of the research process. Subsequently, the challenges that participatory approaches in age(ing) research can pose in these different fields and phases are discussed and possible solutions are outlined.

Our journeys through the fascinating world of proton radiation therapy.

The purpose of this article is to share the excitement of the science of proton therapy, told by two physicists, who started their career in this area at different times. The authors' journey spans the evolution of proton therapy over the past 30 years, taking the reader from the time when it was an extremely exotic treatment modality until its more common use today. Over this time period, the authors' research and development aimed at an improved understanding of the physical benefits of intensity-modulated proton therapy and arc therapy, treatment planning and optimization to take proton-specific uncertainties into account, and imaging to measure the proton range in the patient. The final section focuses on emerging themes to democratize proton therapy by substantially reducing its size and price, for much greater affordability and global availability of this treatment modality.

Democratization of deep learning for segmenting cartilage from MRIs of human knees: Application to data from the osteoarthritis initiative.

In this study, we aimed to democratize access to convolutional neural networks (CNN) for segmenting cartilage volumes, generating state-of-the-art results for specialized, real-world applications in hospitals and research. Segmentation of cross-sectional and/or longitudinal magnetic resonance (MR) images of articular cartilage facilitates both clinical management of joint damage/disease and fundamental research. Manual delineation of such images is a time-consuming task susceptible to high intra- and interoperator variability and prone to errors. Thus, enabling reliable and efficient analyses of MRIs of cartilage requires automated segmentation of cartilage volumes. Two main limitations arise in the development of hospital- or population-specific deep learning (DL) models for image segmentation: specialized knowledge and specialized hardware. We present a relatively easy and accessible implementation of a DL model to automatically segment MRIs of human knees with state-of-the-art accuracy. In representative examples, we trained CNN models in 6-8 h and obtained results quantitatively comparable to state-of-the-art for every anatomical structure. We established and evaluated our methods using two publicly available MRI data sets originating from the Osteoarthritis Initiative, Stryker Imorphics, and Zuse Institute Berlin (ZIB), as representative test cases. We use Google Colabfor editing and adapting the Python codes and selecting the runtime environment leveraging high-performance graphical processing units. We designed our solution for novice users to apply to any data set with relatively few adaptations requiring only basic programming skills. To facilitate the adoption of our methods, we provide a complete guideline for using our methods and software, as well as the software tools themselves. Clinical significance: We establish and detail methods that clinical personal can apply to create their own DL models without specialized knowledge of DL nor specialized hardware/infrastructure and obtain results comparable with the state-of-the-art to facilitate both clinical management of joint damage/disease and fundamental research.

Participação democrática como instrumento de controle social da gestão pública / Democratic participation as an instrument of social control of public management

No Brasil, a partir do processo de redemocratização, diversos rearranjos políticos e sociais ocorreram, sendo os mais notáveis a mudança política estatal, a descentralização do poder e a mobilização dos grupos em desvantagem na busca do atendimento de suas demandas. Neste particular, persiste uma tensão explícita entre os movimentos sociais e a representação política, característica típica das democracias liberais, especialmente no caso brasileiro, isto porque a participação ativa do cidadão é o mecanismo pelo qual se estabelece o controle social, ou seja, o processo de regulação do Estado pelos civis, o qual busca considerar os interesses dos indivíduos nas decisões públicas. Este estudo visa compreender os mecanismos estabelecidos entre sociedade e Estado por meio da análise das motivações da participação civil nos espaços políticos. Ao fim, conclui que, nas democracias contemporâneas, a participação ativa do sujeito social e o controle social exercido pelos arranjos coletivos ­ face à força política que respalda a resistência e o consenso nos territórios de entendimento ­ tendem a assegurar os direitos básicos e a justiça social, razão pela qual se reconhece a relevância dos espaços de participação como referência para o consubstanciamento da democracia ativa

In Brazil, from the redemocratization process, several political and social rearrangements occurred, the most notable being the state political change, the decentralization of power and the mobilization of disadvantaged groups in the search for meeting their demands. In this particular, an explicit tension persists between social movements and political representation, a typical characteristic of liberal democracies, especially in the Brazilian case, this because the active participation of the citizen is the mechanism by which social control is established, that is, the process of regulation of the State by civilians, which seeks to consider the interests of individuals in public decisions. This study aims to understand the mechanisms established between society and the State, analyzing the motivations of civil participation in political spaces, concluding that in contemporary democracies, the active participation of the social subject and the social control exercised by collective arrangements - in the face of the political force that supports resistance and consensus in territories of understanding ­ tend to ensure basic rights and social justice, which is why the relevance of spaces for participation is recognized as a reference for the substantiation of active democracy

"Democratizing" artificial intelligence in medicine and healthcare: Mapping the uses of an elusive term.

Introduction: "Democratizing" artificial intelligence (AI) in medicine and healthcare is a vague term that encompasses various meanings, issues, and visions. This article maps the ways this term is used in discourses on AI in medicine and healthcare and uses this map for a normative reflection on how to direct AI in medicine and healthcare towards desirable futures. Methods: We searched peer-reviewed articles from Scopus, Google Scholar, and PubMed along with grey literature using search terms "democrat*", "artificial intelligence" and "machine learning". We approached both as documents and analyzed them qualitatively, asking: What is the object of democratization? What should be democratized, and why? Who is the demos who is said to benefit from democratization? And what kind of theories of democracy are (tacitly) tied to specific uses of the term? Results: We identified four clusters of visions of democratizing AI in healthcare and medicine: 1) democratizing medicine and healthcare through AI, 2) multiplying the producers and users of AI, 3) enabling access to and oversight of data, and 4) making AI an object of democratic governance. Discussion: The envisioned democratization in most visions mainly focuses on patients as consumers and relies on or limits itself to free market-solutions. Democratization in this context requires defining and envisioning a set of social goods, and deliberative processes and modes of participation to ensure that those affected by AI in healthcare have a say on its development and use.

Reinforcing Health Data Sharing through Data Democratization.

In this paper, we propose a health data sharing infrastructure which aims to empower a democratic health data sharing ecosystem. Our project, named Health Democratization (HD), aims at enabling seamless mobility of health data across trust boundaries through addressing structural and functional challenges of its underlying infrastructure with the core concept of data democratization. A programmatic design of an HD platform was elaborated, followed by an introduction of one of our critical designs-a "reverse onus" mechanism that aims to incentivize creditable data accessing behaviors. This scheme shows a promising prospect of enabling a democratic health data-sharing platform.

Engaging Through Awareness: Purpose-Driven Framework Development to Evaluate and Develop Future Business Strategies With Exponential Technologies Toward Healthcare Democratization.

Industry 4.0 and digital transformation will likely come with an era of changes for most manufacturers and tech industries, and even healthcare delivery will likely be affected. A few trends are already foreseeable such as an increased number of patients, advanced technologies, different health-related business models, increased costs, revised ethics, and regulatory procedures. Moreover, cybersecurity, digital invoices, price transparency, improving patient experience, management of big data, and the need for a revised education are challenges in response to digital transformation. Indeed, forward-looking innovation about exponential technologies and their effect on healthcare is now gaining momentum. Thus, we developed a framework, followed by an online survey, to investigate key areas, analyze and visualize future-oriented developments concerning technologies and innovative business models while attempting to translate visions into a strategy toward healthcare democratization. When forecasting the future of health in a short and long-term perspective, results showed that digital healthcare, data management, electronics, and sensors were the most common predictions, followed by artificial intelligence in clinical diagnostic and in which hospitals and homes would be the places of primary care. Shifting from a reactive to a proactive digital ecosystem, the focus on prevention, quality, and faster care accessibility are the novel value propositions toward democratization and digitalization of patient-centered services. Longevity will translate into increased neurodegenerative, chronic diseases, and mental illnesses, becoming severe issues for a future healthcare setup. Besides, data privacy, big data management, and novel regulatory procedures were considered as potential problems resulting from digital transformation. However, a revised education is needed to address these issues while preparing future health professionals. The "P4 of health", a novel business model that is outcome-based oriented, awareness and acceptance of technologies to support public health, a different mindset that is proactive and future-oriented, and an interdisciplinary setting to merge clinical and technological advances would be key to a novel healthcare ecosystem. Lastly, based on the developed framework, we aim to conduct regular surveys to capture up-to-date technological trends, sustainable health-related business models, and interdependencies. The engagement of stakeholders through awareness and participation is the key to recognizing and improving healthcare needs and services.