Participant experiences in a breastmilk biomonitoring study: A qualitative assessment.

BACKGROUND: Biomonitoring studies can provide information about individual and population-wide exposure. However they must be designed in a way that protects the rights and welfare of participants. This descriptive qualitative study was conducted as a follow-up to a breastmilk biomonitoring study. The primary objectives were to assess participants' experiences in the study, including the report-back of individual body burden results, and to determine if participation in the study negatively affected breastfeeding rates or duration. METHODS: Participants of the Greater Boston PBDE Breastmilk Biomonitoring Study were contacted and asked about their experiences in the study: the impact of study recruitment materials on attitudes towards breastfeeding; if participants had wanted individual biomonitoring results; if the protocol by which individual results were distributed met participants' needs; and the impact of individual results on attitudes towards breastfeeding. RESULTS: No participants reported reducing the duration of breastfeeding because of the biomonitoring study, but some responses suggested that breastmilk biomonitoring studies have the potential to raise anxieties about breastfeeding. Almost all participants wished to obtain individual results. Although several reported some concern about individual body burden, none reported reducing the duration of breastfeeding because of biomonitoring results. The study literature and report-back method were found to mitigate potential negative impacts. CONCLUSION: Biomonitoring study design, including clear communication about the benefits of breastfeeding and the manner in which individual results are distributed, can prevent negative impacts of biomonitoring on breastfeeding. Adoption of more specific standards for biomonitoring studies and continued study of risk communication issues related to biomonitoring will help protect participants from harm.

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Practices of Ministerial Leadership of Korean-American United Methodist Clergywomen; Toward a Wesleyan Ecclesiology in a Global Context

This study documents, analyzes, and interprets Korean American United Methodist (KAUM) clergywomen‘s experiences in and understandings of the church. It examines contributions these (and potentially, other) clergywomen might make to Wesleyan ecclesiology generally, and particular ways United Methodists live out their faith in transitional, diverse, and global contexts. The project attempts to re-vision existing Wesleyan ecclesial discourse in the United Methodist Church (UMC) by recognizing and incorporating the contributions of racial-ethnic clergy as expressed through their leadership and practices of faith. A "practice-theory-practice" model of practical theology was used to pay systematic attention to the practical locus of the inquiries. Twenty Korean American United Methodist clergywomen were interviewed by telephone, using a voluntary sampling technique to ascertain how they both experienced the church and understood and lived out various practices of faith, including preaching, participation in and administration of the sacraments, preparation for ordained ministry, and other spiritual practices such as prayer, worship, retreats, and journaling. The dissertation summarizes those findings, provides contextual and historical interpretation, and then analyzes their responses in relation to Wesleyan theology, MinJung (mass of people) theology, and the theology of YeoSung (women who display dignity and honor as human beings). This study identifies the extraordinary call of the KAUM clergywomen interviewees to be bridge builders, strong nurturers, wounded healers, committed educators, breakers of old stereotypes, persistent seekers to fulfill God‘s call, and ecclesial leaders with ―tragic consciousness‖ who can disrupt marginality and facilitate the creative transformation of Han (a deep experience of suffering and oppression) into a constructive energy capable of shaping a new reality. According to this study, KAUM clergywomen‘s experiences and practices of faith as ecclesial leaders strengthen Wesleyan ecclesiology in terms of the UMC‘s efforts to be an inclusive church through connectionalism, and its commitment to social justice. MinJung theology and the theology of YeoSung, in their respective understandings of the church, broaden Wesleyan ecclesiology and enable the Church to be more relevant in a global context by embracing those who have not been normative theological subjects.

Large-Scale Neural Systems for Vision and Cognition

— Consideration of how people respond to the question What is this? has suggested new problem frontiers for pattern recognition and information fusion, as well as neural systems that embody the cognitive transformation of declarative information into relational knowledge. In contrast to traditional classification methods, which aim to find the single correct label for each exemplar (This is a car), the new approach discovers rules that embody coherent relationships among labels which would otherwise appear contradictory to a learning system (This is a car, that is a vehicle, over there is a sedan). This talk will describe how an individual who experiences exemplars in real time, with each exemplar trained on at most one category label, can autonomously discover a hierarchy of cognitive rules, thereby converting local information into global knowledge. Computational examples are based on the observation that sensors working at different times, locations, and spatial scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels, which are reconciled by implicit underlying relationships that the network’s learning process discovers. The ARTMAP information fusion system can, moreover, integrate multiple separate knowledge hierarchies, by fusing independent domains into a unified structure. In the process, the system discovers cross-domain rules, inferring multilevel relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, the ARTMAP information fusion network features distributed code representations which exploit the model’s intrinsic capacity for one-to-many learning (This is a car and a vehicle and a sedan) as well as many-to-one learning (Each of those vehicles is a car). Fusion system software, testbed datasets, and articles are available from http://cns.bu.edu/techlab.