Regulating Information Flows, Regulating Conflict: An Analysis of United States Conflict Minerals Legislation

The connection between conflict and commercial activity is the focus of this paper. In particular, it focuses on the ongoing conflict in the Eastern Democratic Republic of Congo (DRC) that is funded, in large part, by the sale of conflict commodities – minerals, metals and petroleum that fund violent groups at their source and then enters legitimate markets and products around the world. Recently, attention has turned to how to regulate conflict commerce as a tool for divesting from violent conflict. In the United States, for example, the recently-adopted Dodd-Frank Wall Street Reform and Consumer Protection Act include a provision addressing conflict minerals originating from this region. The violent and secretive nature of conflict minerals transactions makes crafting effective regulation and policing strategies challenging. As a result the Dodd-Frank Act, like other domestic and international efforts, is designed in large part to discover, gather and disseminate information about the nature and scale of conflict commodities emanating from the DRC. This paper analyzes this legislation while also discussing a number of other current conflict commerce governance efforts. It observes the difficulty of regulating in the context of conflict and corruption and analyses the use of regulation as a tool for information-extraction, information-forcing and information-dissemination, as opposed to its use as a tool for directly proscribing undesirable behavior

Age-related relationships among peripheral B lymphocyte subpopulations

An immunological data-driven model is proposed, for age related changes in the network of relationships among cell quantities of eight peripheral B lymphocyte subpopulations, that is, cells exhibiting all combinations of three specific receptor clusters (CD27, CD23, CD5). The model is based on immunological data (quantities of cells exhibiting CD19, characterizing B lymphocytes) from about six thousands patients, having an age ranging between one day and ninety-five years, by means of a suitably combination of data analysis methods, such as piecewise linear regression models. With relaxed values for statistically significant models (coefficient p-values bounded by 0.05), we found a network holding for all ages, that likely represents the general assessment of adaptive immune system for healthy human beings. When statistical validation comes to be more restrictive, we found that some of these interactions are lost with aging, as widely observed in medical literature. Namely, interesting (inverse or directed) proportions are highlighted among mutual quantities of a partition of peripheral B lymphocytes

Conceptualization of Computational Modeling Approaches and Interpretation of the Role of Neuroimaging Indices in Pathomechanisms for Pre-Clinical Detection of Alzheimer Disease

With swift advancements in next-generation sequencing technologies alongside the voluminous growth of biological data, a diversity of various data resources such as databases and web services have been created to facilitate data management, accessibility, and analysis. However, the burden of interoperability between dynamically growing data resources is an increasingly rate-limiting step in biomedicine, specifically concerning neurodegeneration. Over the years, massive investments and technological advancements for dementia research have resulted in large proportions of unmined data. Accordingly, there is an essential need for intelligent as well as integrative approaches to mine available data and substantiate novel research outcomes. Semantic frameworks provide a unique possibility to integrate multiple heterogeneous, high-resolution data resources with semantic integrity using standardized ontologies and vocabularies for context- specific domains. In this current work, (i) the functionality of a semantically structured terminology for mining pathway relevant knowledge from the literature, called Pathway Terminology System, is demonstrated and (ii) a context-specific high granularity semantic framework for neurodegenerative diseases, known as NeuroRDF, is presented. Neurodegenerative disorders are especially complex as they are characterized by widespread manifestations and the potential for dramatic alterations in disease progression over time. Early detection and prediction strategies through clinical pointers can provide promising solutions for effective treatment of AD. In the current work, we have presented the importance of bridging the gap between clinical and molecular biomarkers to effectively contribute to dementia research. Moreover, we address the need for a formalized framework called NIFT to automatically mine relevant clinical knowledge from the literature for substantiating high-resolution cause-and-effect models

Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases

Non-coding RNA regulatory networks

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks

Shared Value in Chile: Increasing Private Sector Competitiveness by Solving Social Problems

Over the last few decades, Chile has experienced rapid and sustained economic, social, and institutional development. Crucial challenges remain, however, in the form of social inequity, lack of opportunity, mistrust, and social unrest. The Chilean private sector is at an inflection point in its relationship with society. The corporate sector has both contributed to and benefited from the growth and development of the last decades, but remaining social challenges pose significant constraints to the continued growth of the private sector. High levels of mistrust regarding the role of business in society reflect a widespread belief that profit making activities are merely a demonstration of corporate greed. The Chilean private sector faces a frequently antagonistic relationship with government and civil society that will likely worsen unless companies are able to find ways to authentically link their businesses to efforts to solve Chile's social problems. On the other hand, if government and civil society conclude that the private sector has no contribution to make to the country's social and economic development strategy, Chile will squander an important engine for creating shared prosperity. The good news is that there does not need to be a trade-off between private sector competitiveness and greater prosperity for all Chileans. Shared value, a concept explained in Harvard Professor Michael Porter and Mark Kramer's Harvard Business Review articles, suggests an approach for companies to increase their competitiveness and profitability by helping to solve social problems. The public sector and civil society can increase the social benefits from shared value by thoughtfully partnering with the private secto

Legal Protection of Workers’ Human Rights: Regulatory Changes and Challenges in the United States

[Excerpt] In a 2002 study, the US Government Accountability Office reported that more than 32 million workers in the United States lack protection of the right to organise and to bargain collectively. But since then, the situation has worsened. A series of decisions by the federal authorities under President George Bush has stripped many more workers of organising and bargaining rights. The administration took away bargaining rights for hundreds of thousands of employees in the new Department of Homeland Security and the Defense Department.18 In the years before the 2009 change of administration, a controlling majority of the five-member National Labor Relations Board (NLRB), appointed by President Bush, denied protection to graduate student employees, disabled employees, temporary employees and other categories of workers. An October 2006, a NLRB decision was especially alarming for labour advocates. The NLRB set out a new, expanded definition of \u27supervisor\u27 under the section of US labour law that excludes supervisors from protection of the right to organise and bargain collectively. This exclusion has enormous repercussions for millions of workers who might now become \u27supervisors\u27 and lose protection of their organising and bargaining rights.21 This case is discussed in more detail below in connection with a complaint to the International Labour Organisation (ILO) Committee on Freedom of Association