ethicalabs/Research-Intent-Collab huggingface dataset:
Clinical Application and Challenges of Stem Cell Therapy in Regenerative Medicine

Stem cell therapy, as a core treatment method in the field of regenerative medicine, has attracted much attention in recent years due to its unique self-renewal and differentiation abilities. This article systematically reviews the basic theory of stem cell therapy, including the definition, classification, principle mechanism, and preparation quality control of stem cells. In terms of clinical application, stem cell therapy has demonstrated extensive therapeutic potential: in the field of tissue repair, stem cell therapy can effectively promote the regeneration and repair of various tissues such as skin, muscle, and bone; In terms of disease treatment, it has shown significant efficacy in blood diseases, immune diseases, neurodegenerative diseases, and cardiovascular diseases; In addition, stem cell therapy has shown promising application prospects in the fields of anti-aging and beauty. However, stem cell therapy still faces many challenges in practical applications, including safety issues, difficulties in evaluating effectiveness, and regulatory and ethical limitations. To address these issues, this article proposes a series of solutions, such as establishing a strict quality control system, developing scientifically effective evaluation methods and indicators, and improving relevant regulations and ethical review mechanisms. Research has found that although stem cell therapy still faces many technical and ethical challenges, its enormous therapeutic potential and broad application prospects make it a research hotspot and future development direction in the field of regenerative medicine.
Tracing Digital Contact Tracing: Surveillance Technology and Privacy Rights During COVID-19 in China, South Korea, and the United States

In many societies, during large-scale crises such as those involving public health or national security, privacy is often framed as an individual value, right, or responsibility that can be sacrificed, pitted against more important public and social values. Yet, the social importance of privacy has been suggested by various scholars, considering its value for a democratic political system, societal trust, and interdependent or networked nature of privacy. As such, we also find the dichotomous framing that considers privacy as an individual right to be traded off for other public values misleading. It delegitimates discussions and concerns around privacy, and by doing so, unduly privileges some agendas at the sacrifice of certain individuals or marginalized communities.    We started this collaborative project, acknowledging such individualistic conceptualization of privacy to be problematic. This project is an attempt to start brainstorming alternative thinking and framing of privacy, particularly in the context of restrained mobility during the COVID-19 pandemic in 2020. That is, privacy is not necessarily what individuals or certain groups of people should give up when the bigger communities are perceived to be under threat or crisis. Rather, privacy needs to be considered as a collective value and right, which should be carefully considered in discussion of ways to overcome different modes of crises in society. For example, if we are not carefully protective of everyone’s privacy rights during the pandemic, considering only the benefits of being able to trace individuals’ or specific populations’ health records or movements, the consequence may be tied to dire social problems, especially racism and discrimination, which would reinforce existing social polarization with long-lasting impacts on society.    We focused on digital contact tracing methods introduced in different parts of the world (i.e., USA, China, and South Korea) to conduct a comparative case study that surveys how each country has approached the global public health crisis at the national level and what implications it brought to the understanding, discourse, and protection of privacy. For case studies on the three countries, we took an approach of “thin description” to reflect upon a variety of texts identified for each country’s context (i.e., the popular press, academic articles, legal documents, government and corporate informational materials). The purpose of this project was rather to provide a brief but a critical overview of privacy implications along with the digital contact tracing technologies introduced and implemented in the three countries that have divergent cultures and political systems.
National Evaluation of the State Children's Health Insurance Program: A Decade of Expanding Coverage and Improving Access

Expanding health coverage for children is at the center of policy debates as reauthorization of the 10-year-old State Childrenâ€™s Health Insurance Program (SCHIP) continues.
Paracrine Proangiopoietic Effects of Human Umbilical Cord Blood-Derived Purified CD133                     <sup>+</sup>                     Cells—Implications for Stem Cell Therapies in Regenerative Medicine

CD133                     +                     cells purified from hematopoietic tissues are enriched mostly for hematopoietic stem/progenitor cells, but also contain some endothelial progenitor cells and very small embryonic-like stem cells. CD133                     +                     cells, which are akin to CD34                     +                     cells, are a potential source of stem cells in regenerative medicine. However, the lack of convincing donor-derived chimerism in the damaged organs of patients treated with these cells suggests that the improvement in function involves mechanisms other than a direct contribution to the damaged tissues. We hypothesized that CD133                     +                     cells secrete several paracrine factors that play a major role in the positive effects observed after treatment and tested supernatants derived from these cells for the presence of such factors. We observed that CD133                     +                     cells and CD133                     +                     cell-derived microvesicles (MVs) express mRNAs for several antiapoptotic and proangiopoietic factors, including kit ligand, insulin growth factor-1, vascular endothelial growth factor, basic fibroblast growth factor, and interleukin-8. These factors were also detected in a CD133                     +                     cell-derived conditioned medium (CM). More important, the CD133                     +                     cell-derived CM and MVs chemoattracted endothelial cells and display proangiopoietic activity both in vitro and in vivo assays. This observation should be taken into consideration when evaluating clinical outcomes from purified CD133                     +                     cell therapies in regenerative medicine.
Neuromimetic metaplasticity for adaptive continual learning without catastrophic forgetting

Conventional intelligent systems based on deep neural network (DNN) models encounter challenges in achieving human-like continual learning due to catastrophic forgetting. Here, we propose a metaplasticity model inspired by human working memory, enabling DNNs to perform catastrophic forgetting-resistant continual learning without any pre- or post-processing. A key aspect of our approach involves implementing distinct types of synapses from stable to flexible, and randomly intermixing them to train synaptic connections with different degrees of flexibility. This strategy allowed the network to successfully learn a continuous stream of information, even under unexpected changes in input length. The model achieved a balanced tradeoff between memory capacity and performance without requiring additional training or structural modifications, dynamically allocating memory resources to retain both old and new information. Furthermore, the model demonstrated robustness against data poisoning attacks by selectively filtering out erroneous memories, leveraging the Hebb repetition effect to reinforce the retention of significant data.
Impact of Preservative Medium and Oxidative Cleaning on the Trace Element Geochemistry and Stable Isotope Composition of Modern Foraminifera

AbstractCollection of modern foraminifera for paleoproxy development studies often require samples to be preserved. Shells are then typically cleaned prior to geochemical analysis. Preservation and cleaning reagents could impact the shell trace element (TE) or stable isotope composition via contamination or post‐preservation calcite alteration. For example, the Na/Ca composition of the calcite could be impacted by both the borax (Na2B4O5 (OH)4 · 8H2O)‐buffered formalin preservative and the sodium hydroxide (NaOH) used in cleaning. Here we investigate the impact of preservative medium (borax‐buffered formalin and ethanol) and oxidative cleaning on shell TE/Ca ratios (Mg, Na, Sr, Ba, Mn, Zn) and δ13C and δ18O composition of modern planktic foraminifera collected via plankton tow. Results show that δ13C, δ18O, and all TE/Ca ratios, except for Zn/Ca, are unaffected by preservation medium when compared to a control group. Shells preserved in ethanol exhibit highly elevated Zn/Ca ratios compared to the borax‐buffered formalin and control specimens. Similarly, the oxidative cleaning step had no impact on TE/Ca ratios, except for Zn/Ca, which decreases when additional rinses are performed after the oxidative cleaning step. These impacts on foraminiferal Zn/Ca ratios may be an important contributor to the difficulty in constraining the dominant environmental control on Zn incorporation in foraminifera and will need to be considered in any future paleoproxy development, paleoceanographic, or biomonitoring studies investigating Zn/Ca. Results herein provide promising support for the usage and comparison of most geochemical data collected from modern foraminifera that were exposed to different preservatives or underwent an oxidative cleaning procedure.
Sampling in High-Dimensions using Stochastic Interpolants and Forward-Backward Stochastic Differential Equations

We present a class of diffusion-based algorithms to draw samples from high-dimensional probability distributions given their unnormalized densities. Ideally, our methods can transport samples from a Gaussian distribution to a specified target distribution in finite time. Our approach relies on the stochastic interpolants framework to define a time-indexed collection of probability densities that bridge a Gaussian distribution to the target distribution. Subsequently, we derive a diffusion process that obeys the aforementioned probability density at each time instant. Obtaining such a diffusion process involves solving certain Hamilton-Jacobi-Bellman PDEs. We solve these PDEs using the theory of forward-backward stochastic differential equations (FBSDE) together with machine learning-based methods. Through numerical experiments, we demonstrate that our algorithm can effectively draw samples from distributions that conventional methods struggle to handle.
Military intervention for humanitarian purposes: does the Responsibility to Protect doctrine advance the legality of the use of force for humanitarian ends?

AbstractThe Responsibility to Protect is being touted as a new approach to protecting populations from mass atrocities. Certainly it would be encouraging to believe that an end to genocides, large scale ethnic cleansing and large scale loss of life were within humanity's reach. However, whilst the holistic approach of the doctrine is to be commended, the legality of the proposal requires further analysis. This paper specifically addresses the evolution of the legality of humanitarian intervention and looks at whether the Responsibility to Protect doctrine advances the legality of the use of force for humanitarian ends.
Sampling of Stochastic Differential Equations using the Karhunen-Lo\`eve Expansion and Matrix Functions

We consider linearizations of stochastic differential equations with additive noise using the Karhunen-Lo\`eve expansion. We obtain our linearizations by truncating the expansion and writing the solution as a series of matrix-vector products using the theory of matrix functions. Moreover, we restate the solution as the solution of a system of linear differential equations. We obtain strong and weak error bounds for the truncation procedure and show that, under suitable conditions, the mean square error has order of convergence $\mathcal{O}(\frac{1}{m})$ and the second moment has a weak order of convergence $\mathcal{O}(\frac{1}{m})$, where $m$ denotes the size of the expansion. We also discuss efficient numerical linear algebraic techniques to approximate the series of matrix functions and the linearized system of differential equations. These theoretical results are supported by experiments showing the effectiveness of our algorithms when compared to standard methods such as the Euler-Maruyama scheme.
Optimal control for a nonlinear stochastic PDE model of cancer growth

We study an optimal control problem for a stochastic model of tumour growth with drug application. This model consists of three stochastic hyperbolic equations describing the evolution of tumour cells. It also includes two stochastic parabolic equations describing the diffusions of nutrient and drug concentrations. Since all systems are subject to many uncertainties, we have added stochastic terms to the deterministic model to consider the random perturbations. Then, we have added control variables to the model according to the medical concepts to control the concentrations of drug and nutrient. In the optimal control problem, we have defined the stochastic and deterministic cost functions and we have proved that the problems have unique optimal controls. For deriving the necessary conditions for optimal control variables, the stochastic adjoint equations are derived. We have proved the stochastic model of tumour growth and the stochastic adjoint equations have unique solutions. For proving the theoretical results, we have used a change of variable which changes the stochastic model and adjoint equations (a.s.) to deterministic equations. Then we have employed the techniques used for deterministic ones to prove the existence and uniqueness of optimal control.
Metrics, models and foresight for European sustainable food and nutrition security: The vision of the SUSFANS project

This paper defines the research agenda of the SUSFANS project, describes its history and its potential societal impacts. It contributes to balanced and encompassing views on how to strengthen food and nutrition security outcomes in the EU and how to improve the performance of the food system in the EU from the perspective of social, environmental and economic sustainability. The research is led by the notion that improvements in the diets of the European consumer must come from, and be supportive of, sustainable food systems. Its holistic, integrative approach builds a set of metrics, models and foresight tools, useable for navigation on sustainable food and nutrition security. This results in a coherent and supported vision on sustainable food and nutrition security in the EU and globally, and underpins a perspective on how EU policies on farming, fishing, food and nutrition could contribute to that vision with greater efficacy than today.
Research on Security Strategies for Wireless Sensor Networks in Internet of Things Applications

With the development of modern science and technology, the Internet of Things has been widely used in many fields, and it involves a wide range of technologies, among which the most important technology is the sensor, the most basic technology. The sensor network node working environment has been very complex and demanding; if slightly relaxed, it will lead to the sensor facing many security risks. Therefore, strengthening the security of wireless sensor networks has become an essential part of the Internet of Things project. This paper mainly starts with an overview of the Internet of Things and wireless sensor networks, analyzes the security challenges and impacts of wireless sensor networks, and discusses the main security threats and countermeasures. The purpose is to implement wireless sensor network security strategies for Internet of Things applications and build a comprehensive and multi-level security protection system through various means. To ensure that the sensor network can operate stably and safely in a complex environment, Internet of Things technology‘s healthy and sustainable development can be promoted.
How Are Incremental Cost-Effectiveness, Contextual Considerations, and Other Benefits Viewed in Health Technology Assessment Recommendations in the United States?

To review assessments from the Institute for Clinical and Economic Review (ICER) and describe how cost-effectiveness, other benefits or disadvantages, and contextual considerations affect Council members' assessments of value.Assessments published by the ICER between December 2014 and April 2019 were reviewed. Data on the assessment, intervention, results from cost-effectiveness analyses, and Council members' votes were extracted. Voting data were examined using bar charts and radar plots. Spearman's correlations between the number of votes for other benefits and contextual considerations were estimated. Two case studies (tisagenlecleucel and voretigene neparvovec) explored the relationship between different aspects of value and the vote.Thirty-one ICER assessments were reviewed, which included 51 value votes and 17 votes on other benefits and contextual considerations. On average, interventions with lower cost-effectiveness ratios received a higher proportion of high and intermediate value votes; however, there was heterogeneity across assessments. Of other benefits or disadvantages, having a novel mechanism of action received the most votes (n = 138), and reducing health disparities received the fewest (n = 24). Of contextual considerations, treating a condition that has a severe impact on length and quality of life received the most votes (n = 164). There was a strong positive correlation between votes for reduced caregiver/family burden and improving return to work/productivity (ρ = 0.88, P < .05). Two case studies highlighted that factors beyond cost-effectiveness can lead to lower (tisagenlecleucel) or higher (voretigene neparvovec) assessments of value.Council members' judgments about the value of interventions are influenced by other benefits or disadvantages and contextual considerations but anchored by cost-effectiveness.
Towards Demystifying Intra-Function Parallelism in Serverless Computing

International Workshop on Serverless Computing (WoSC@Middleware 2021)
The Effect of Business Ethics and Corporate Social Responsibility on Business Success: Insights from Wa Township in Ghana

Purpose: Over the years, small businesses have been the backbone of economic growth in many low-middle-income settings. Thus, small firms/small businesses have attempted to develop codes of conduct, which can be used to guide managers when making decisions. This study, therefore, explored business ethics, repurchase and corporate social responsibility within Wa Township. Design/Methodology/ Approach: The study employed the case study approach of both qualitative and quantitative methods. A total of 358 smallholder shop owners were randomly selected to participate in face-to-face interviews. The data were keyed into a Microsoft Excel sheet and cleaned. The data set was then imported into SPSS and analysed using frequencies, proportions and chi-square test of independence. Findings: The results have shown a strong statistical relationship between business ethics and purchase (p=0.000), business ethics and repurchase (p=0.000), business ethics and corporate social responsibility (p=0.000). Practical implications: Small shop owners recognise the central place of ethical behaviours and repurchase and corporate social responsibility on business growth through increased sales. This study therefore recommends investment in growing smallholder business owners’ knowledge about the necessity of business ethics and customer satisfaction.
EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. As a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK-a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks-aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed-the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. We show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.
Liquid Water on Cold Exo-Earths via Basal Melting of Ice Sheets

Liquid water is a critical component of habitability. However, the production and stability of surficial liquid water can be challenging on planets outside the Habitable Zone and devoid of adequate greenhouse warming. On such cold, icy exoEarths, basal melting of regional, global ice sheets by geothermal heat provides an alternative means of forming liquid water. Here, we model the thermophysical evolution of ice sheets to ascertain the geophysical conditions that allow liquid water to be produced and maintained at temperatures above the pressure controlled freezing point of water ice on exoEarths. We show that even with a modest, Moon like geothermal heat flow, subglacial oceans of liquid water can form at the base of and within the ice sheets on exoEarths. Furthermore, subglacial oceans may persist on exoEarths for a prolonged period due to the billion year half lives of heat producing elements responsible for geothermal heat. These subglacial oceans, often in contact with the planets crust and shielded from the high energy radiation of their parent star by thick ice layers, may provide habitable conditions for an extended period.