ethicalabs/Research-Intent-Collab huggingface dataset:
Serendipity and Scientific Discovery: a new way to see Science and Knowledge?

At the heart of my paper there is the concept of serendipity: it has recently been defined as a Â«discovery at the intersection of chance and wisdomÂ», but it has been described in many other ways since Horace Walpole coined the term to indicate Â«that discoveries of things the observers were not in quest of, which happen by both accidents and sagacityÂ». Finding an unambiguous definition is therefore complex. However, defining the concept of serendipity is not the only way to shed light on its occurrence: by analyzing and understanding its theoretical-conceptual characteristics, in fact, I believe we can clarify how it acts within the process of scientific discovery. In this way, chance, theory ladenness, scientific method, and situated cognition acquire a new light and significance. In particular, what we learn through serendipity is not only new facts about the world, but also other ways of seeing science and knowledge.
Importance of Evidence-Based Health Insurance Reimbursement and Health Technology Assessment for Achieving Universal Health Coverage and Improved Access to Health in India

To review the importance of evidence-based methods in health insurance reimbursement for achieving universal health coverage in India.A narrative literature review was performed.The out-of-pocket (OOP) healthcare expenditure in India is among the highest in the world. This situation is despite the implementation of numerous government health schemes and the availability of a large number of health insurance programs, both public and private. Compromised quality of care in many public healthcare facilities is a major factor driving the average Indian citizen to increasingly depend upon private healthcare facilities, further escalating OOP spending. The low awareness and poor uptake of insurance policies among Indians is one of the biggest challenges in the implementation of universal health coverage (UHC) in India. The catastrophic burden of high OOP expenses on individual households could be reduced by taking steps to enhance health insurance uptake, which can be in turn achieved by strengthening the healthcare reimbursement system in India.The application of the principles of evidence-based healthcare for reimbursement requires the systematic assessment of all health technologies, which is already being done in developed countries. The enactment of health schemes such as Ayushman Bharat-Pradhan Mantri Jan Arogya Yojana and setting up of Health Technology Assessment in India are steps toward reducing OOP expenditure and achieving UHC in India. We review the importance and challenges of evidence-based reimbursement and health technology assessment toward achieving UHC in India.
Mesenchymal Stem/Stromal Cells as a Therapeutic Tool in Cell-Based Therapy and Regenerative Medicine: An Introduction Expertise to the Topical Collection

We are pleased to present this opening editorial, introducing our topical collection, “The New Era of Mesenchymal Stromal/Stem Cell Functional Application: State of the Art, Therapeutic Challenges and Future Directions” [...]
Identification of Requirements for Designing an Organizational Dashboard to Monitor the Research Performance of Educational and Research Institutions in Iran's Scientific Information Database (Ganj)

The Scientific Information Database of Iran (Ganj) contains hundreds of thousands of records and is considered one of the largest scientific repositories in the country. Optimal utilization of this database can be achieved through data analysis and providing representations of the scientific and research performance of stakeholders via organizational dashboards. Databases like "Ganj" require dashboards that educational and research institutions can use to monitor and evaluate their scientific and research performance. This research was conducted to identify the requirements for designing an organizational dashboard in the Scientific Information Database of Iran (Ganj).To achieve this goal, relevant scientific and professional literature was first reviewed, and similar organizational dashboards in Iran and worldwide were examined. Subsequently, interviews were conducted with key stakeholders in this field (such as researchers, policymakers, research vice presidents, and heads of central libraries), and their perspectives were systematically analyzed and coded. Finally, in a focus group, the obtained indicators and metrics were validated and finalized.The findings of the research indicated that the indicators of interest to the panel members for monitoring performance were approved in eight comprehensive categories. These eight categories included indicators for research quantity, research quality, ethical considerations, peer review, awards, derived outputs, commercialization, and research type. According to the panel members' views, all indicators and metrics, except for the ethical consideration indicator and the similarity percentage metric (plagiarism), could be generally accessible. For the ethical consideration indicator, the similarity percentage metric was deemed necessary to be reported at higher levels of accessibility due to its lack of general importance.Finally, a conceptual model of the requirements for designing the organizational dashboard for this database was outlined. The results of this research can assist designers and policymakers of the Scientific Information Database of Iran (Ganj) as well as managers of organizational repositories in various educational and research institutions. It should be noted that the indicators introduced in this research alone cannot provide a clear and accurate view of the evaluation of institutions, and the interpretation and explanation of their results should be a focal point.
De-centering the West: East Asian Philosophies and the Ethics of Applying Artificial Intelligence to Music

Questions about the ethical dimensions of artificial intelligence (AI) become more pressing as its applications multiply. While there is a growing literature calling attention to the ethics of AI in general, sector-specific and culturally sensitive approaches remain under-explored. We thus initiate an effort to establish a framework of ethical guidelines for music AI in the context of East Asia, a region whose rapid technological advances are playing a leading role in contemporary geopolitical competition. We draw a connection between technological ethics and non-Western philosophies such as Confucianism, Buddhism, Shintoism, and Daoism. We emphasize interrelations between AI and traditional cultural heritage and values. Drawing on the IEEE Principles of Ethically Aligned Design, we map its proposed ethical principles to East Asian contexts and their respective music ecosystem. In this process of establishing a culturally situated understanding of AI ethics, we see that the seemingly universal concepts of "human rights", "well-being", and potential "misuse" are ultimately fluid and need to be carefully examined in specific cultural contexts.
Optimal Management of Water for Sustainable Fisheries and Aquaculture

This paper discusses optimal management for water resources in the context of the over‐exploitation of fish and the pollution of freshwater fisheries, along with aquaculture, in inland and coastal areas. An optimal control model maximizing social surplus focusing on the dynamic relationship of fish stock externality, price, outputs, costs and polluting wastes is developed. It provides the necessary conditions for optimal management for both fishery and aquaculture, which produce the same product for the market, and also develops time‐paths and phase diagrams for policy application.
A Computerized Clinical Decision Support System as a Means of Implementing Depression Guidelines

The authors describe the history and current use of computerized systems for implementing treatment guidelines in general medicine as well as the development, testing, and early use of a computerized decision support system for depression treatment among "real-world" clinical settings in Texas. In 1999 health care experts from Europe and the United States met to confront the well-documented challenges of implementing treatment guidelines and to identify strategies for improvement. They suggested the integration of guidelines into computer systems that is incorporated into clinical workflow. Several studies have demonstrated improvements in physicians' adherence to guidelines when such guidelines are provided in a computerized format. Although computerized decision support systems are being used in many areas of medicine and have demonstrated improved patient outcomes, their use in psychiatric illness is limited. The authors designed and developed a computerized decision support system for the treatment of major depressive disorder by using evidence-based guidelines, transferring the knowledge gained from the Texas Medication Algorithm Project (TMAP). This computerized decision support system (CompTMAP) provides support in diagnosis, treatment, follow-up, and preventive care and can be incorporated into the clinical setting. CompTMAP has gone through extensive testing to ensure accuracy and reliability. Physician surveys have indicated a positive response to CompTMAP, although the sample was insufficient for statistical testing. CompTMAP is part of a new era of comprehensive computerized decision support systems that take advantage of advances in automation and provide more complete clinical support to physicians in clinical practice.
2-Keto acids to branched-chain alcohols as biofuels: Application of reaction network analysis and high-level quantum chemical methods to understand thermodynamic landscapes

Abstract   The discovery of biochemical reaction pathways consisting of both known and novel reactions to synthesize carbon-based compounds is of tremendous interest for the production of biofuels. One attractive target is energy-rich alcohols that can be produced via a series of enzymatic transformations. Computational tools for generating networks of reactions to produce alcohols have been demonstrated, yet the number of potential pathways was still too large to be explored experimentally. Here we present the results of high-level quantum chemical calculations to predict the thermodynamic feasibility of various reactions. The conversion of pyruvic acid to 1-butanol was used as an example system. The free energy landscapes generated using ab initio methods were compared against group contribution analysis for a reaction network of ABE fermentation of pyruvic acid. Secondly, detailed reaction energetics were calculated for Ehrlich’s pathway for the conversion of 2-keto acids to alcohols. Finally, the energetics for the conversion of non-native keto acids such as 2-keto-butanoic acid, 2-keto-valeric acid and 2-keto-isovaleric acid to their corresponding alcohols were evaluated, and the thermodynamic landscapes were compared to that of the native substrate, pyruvic acid.
O impacto de uma Central Bank Digital Currency nos depósitos bancários da Zona Euro

The growing digitization of the economy and the means of payment, as well as the growing use of information technologies in the financial sector have inspired Central Banks around the world to reflect on the potential benefits inherent to the creation of a capable Central Bank Digital Currency (CBDC) to suppress not only their own needs, but also those of the citizens. In an era in which it is essential for Central Banks to safeguard their role as monetary authority, the creation and issuance of a CBDC, as a Central Bank liability, which can constitute a reserve of value, without volatility risks and associated value losses, becomes increasingly evident. However, there might be limitations, and there are risks that must be warned. That said, the main goal of this Dissertation is to address the issue of the disintermediation of Commercial Banking and assess the impact of issuing a CBCD on bank deposits in the Eurozone. To this end, quantitative and qualitative work was carried out, including the creation of an econometric model and the development of a questionnaire addressed to Portuguese citizens. Although it is premature to try to find the answers we propose to, we hope that the conclusions obtained in this Dissertation can add value to the academic community and that they can, above all, serve as a trigger for the elaboration of new studies related to this subject.
Machine learning for retrosynthesis and synthesisable molecule generation in drug discovery

Drug discovery is a notoriously difficult and slow process, with high research and development costs and a decreasing success rate. Computer-Aided Drug Design methods show promise in improving the efficiency of early stage drug discovery, increasing the number of compounds that can be evaluated per design cycle and allowing for pre-filtering of molecules with fast computational methods before they are synthesised. However, many of the compounds designed in silico are not synthesisable in practice or the synthesis routes towards them are not obvious. This leads to computational resources being wasted on designing molecules that can never be tested experimentally. This thesis explores new methods for two approaches assessing and improving synthesisability in drug discovery: retrosynthesis prediction and synthesisability-constrained molecule generation.First, the problem of retrosynthesis prediction for molecules containing heterocyclic scaffolds is considered. Four domain adaptation approaches are benchmarked to develop a single-step retrosynthesis prediction model with improved performance for ring disconnections. Accuracy for heterocycle formations and all reaction classes, as well as computational cost, are considered. A further fine-tuning workflow for continual retraining of the model with newly published data is introduced. The application of the most versatile model, trained with a mixed fine-tuning strategy, is then demonstrated in multi-step retrosynthesis in a retrospective analysis for two drug-like compounds.Next, the development of retro-active, a method for synthesisable molecule generation and optimisation, is described. Retro-active generates molecules based on a known synthesis route and a provided starting material pool. The use of active learning for starting material selection allows for the optimisation of the resulting product molecules for user-defined scoring functions. A benchmark of starting material acquisition and product enumeration methods is included, as well as a comparison to alternative non-machine learning-based starting material selection approaches. The applicability of retro-active for both ligand-based and structure-based drug discovery is demonstrated.The use case of retro-active is then extended to multi-parameter optimisation, to simulate a real-life drug discovery scenario. The compounds are optimised for their structural, physicochemical, and ADMET properties, with a scoring function that combines physics-based and machine learning-based scores. The robustness of the method is demonstrated with both convergent and linear synthesis route topologies and ligands for different target proteins.The thesis concludes with final remarks regarding retrosynthesis prediction and synthesisable molecule generation with retro-active, including future research directions and challenges in the field.
Statistical Mechanics of One-Dimensional Systems. I: Phase Transition of a van der Waals Gas

Summary: The model of one-dimensional gas is proposed in which the particles have hard cores and pair potentials \(-2a/L,\) \(a(>0)\) being constant and \(L\) the length of the gas. The partition function unstable portion of the isotherm. The grand partition function and the isothermal-isobaric partition function for this model are exactly obtained and a phase transition is deduced. Comparison of this model with the Husimi-Temperley model of a lattice system is made. The first five (volume-dependent) cluster integrals for this model are calculated.
A novel scheme for Dark Matter Annihilation Feedback in cosmological simulations

ABSTRACT                We present a new self-consistent method for incorporating Dark Matter Annihilation Feedback (DMAF) in cosmological N-body simulations. The power generated by DMAF is evaluated at each dark matter (DM) particle which allows for flexible energy injection into the surrounding gas based on the specific DM annihilation model under consideration. Adaptive, individual time-steps for gas and DM particles are supported and a new time-step limiter, derived from the propagation of a Sedov–Taylor blast wave, is introduced. We compare this donor-based approach with a receiver-based approach used in recent studies and illustrate the differences by means of a toy example. Furthermore, we consider an isolated halo and a cosmological simulation and show that for these realistic cases, both methods agree well with each other. The extension of our implementation to scenarios such as non-local energy injection, velocity-dependent annihilation cross-sections, and DM decay is straightforward.
Comprehensive user requirements engineering methodology for secure and interoperable health data exchange 08 Information and Computing Sciences 0806 Information Systems

Background: Increased digitalization of healthcare comes along with the cost of cybercrime proliferation. This results to patients' and healthcare providers' skepticism to adopt Health Information Technologies (HIT). In Europe, this shortcoming hampers efficient cross-border health data exchange, which requires a holistic, secure and interoperable framework. This study aimed to provide the foundations for designing a secure and interoperable toolkit for cross-border health data exchange within the European Union (EU), conducted in the scope of the KONFIDO project. Particularly, we present our user requirements engineering methodology and the obtained results, driving the technical design of the KONFIDO toolkit. Methods: Our methodology relied on four pillars: (a) a gap analysis study, reviewing a range of relevant projects/initiatives, technologies as well as cybersecurity strategies for HIT interoperability and cybersecurity; (b) the definition of user scenarios with major focus on cross-border health data exchange in the three pilot countries of the project; (c) a user requirements elicitation phase containing a threat analysis of the business processes entailed in the user scenarios, and (d) surveying and discussing with key stakeholders, aiming to validate the obtained outcomes and identify barriers and facilitators for HIT adoption linked with cybersecurity and interoperability. Results: According to the gap analysis outcomes, full adherence with information security standards is currently not universally met. Sustainability plans shall be defined for adapting existing/evolving frameworks to the state-of-the-art. Overall, lack of integration in a holistic security approach was clearly identified. For each user scenario, we concluded with a comprehensive workflow, highlighting challenges and open issues for their application in our pilot sites. The threat analysis resulted in a set of 30 user goals in total, documented in detail. Finally, indicative barriers of HIT acceptance include lack of awareness regarding HIT risks and legislations, lack of a security-oriented culture and management commitment, as well as usability constraints, while important facilitators concern the adoption of standards and current efforts for a common EU legislation framework. Conclusions: Our study provides important insights to address secure and interoperable health data exchange, while our methodological framework constitutes a paradigm for investigating diverse cybersecurity-related risks in the health sector.
Comprehensive assessment of AlphaFold’s predictions of secondary structure and solvent accessibility at the amino acid-level in eukaryotic, bacterial and archaeal proteins

Numerous sequence-based predictors of the amino acid (AA)-level solvent accessibility (SA) and secondary structure (SS) of proteins have been developed. We empirically investigated whether these two key characteristics of AA-level structure can be accurately predicted from putative structures generated by the popular AlphaFold2. We compared AlphaFold2's results against several representative SS and SA predictors on a large test dataset that covers five distinct taxonomic groups (animals, plants, fungi, bacteria, and archaea). We used a broad collection of metrics that evaluate predictions of the numeric and binary (buried vs. solvent exposed) SA and the 3-state SS at both AA- and SS-region levels. We found that AlphaFold2 generated very accurate results, with high average Q3 accuracy of 0.928 for the SS prediction and high Pearson Correlation Coefficient (PCC) of 0.815 between its putative and native SA values. AlphaFold2 significantly and consistently outperforms the considered predictors of SA and SS across the five taxonomic groups and both AA and region level evaluations. Moreover, we demonstrated that AlphaFold2 nearly perfectly reconstructs distributions of the sizes and numbers of the SS regions. We also showed that AlphaFold2 substantially improves over the SS and SA predictors when tested on a low sequence similarity test dataset, although its results and results of two other predictors suffer a modest drop in the quality of predicting SS regions. Altogether, our results suggest that AlphaFold2 makes very accurate predictions of SS and SA, which can be easily extracted from 200+ million pre-computed AF2's structure predictions in AlphaFoldDB.
Adaptive importance sampling in least-squares Monte Carlo algorithms for backward stochastic differential equations

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The precautionary principle and management of uncertainties in EIAs – analysis of waste incineration cases in Finland

This article examines whether and how the practice of environmental impact assessments (EIAs) serves the idea of the precautionary principle. The article provides an empirical examination and description of how uncertainties and risks were dealt with in conclusive outputs of EIAs of chosen example cases of Finnish waste incineration projects. The analysis focuses on the time when several incineration projects were in the planning phase in Finland and the effects of this development were seen as being controversial. The findings of the analysis are evaluated against a legislative and theoretical framework. The challenges and strengths of the practice are identified. The article suggests that EIA as an environmental policy tool can promote the pursuit and the application of the precautionary principle. In addition, uncertainty analysis and risk communication in EIA could benefit from a shift towards more collaborative knowledge-making.