Phylotastic! Making tree-of-life knowledge accessible, reusable and convenient.

BACKGROUND: Scientists rarely reuse expert knowledge of phylogeny, in spite of years of effort to assemble a great "Tree of Life" (ToL). A notable exception involves the use of Phylomatic, which provides tools to generate custom phylogenies from a large, pre-computed, expert phylogeny of plant taxa. This suggests great potential for a more generalized system that, starting with a query consisting of a list of any known species, would rectify non-standard names, identify expert phylogenies containing the implicated taxa, prune away unneeded parts, and supply branch lengths and annotations, resulting in a custom phylogeny suited to the user's needs. Such a system could become a sustainable community resource if implemented as a distributed system of loosely coupled parts that interact through clearly defined interfaces. RESULTS: With the aim of building such a "phylotastic" system, the NESCent Hackathons, Interoperability, Phylogenies (HIP) working group recruited 2 dozen scientist-programmers to a weeklong programming hackathon in June 2012. During the hackathon (and a three-month follow-up period), 5 teams produced designs, implementations, documentation, presentations, and tests including: (1) a generalized scheme for integrating components; (2) proof-of-concept pruners and controllers; (3) a meta-API for taxonomic name resolution services; (4) a system for storing, finding, and retrieving phylogenies using semantic web technologies for data exchange, storage, and querying; (5) an innovative new service, DateLife.org, which synthesizes pre-computed, time-calibrated phylogenies to assign ages to nodes; and (6) demonstration projects. These outcomes are accessible via a public code repository (GitHub.com), a website (http://www.phylotastic.org), and a server image. CONCLUSIONS: Approximately 9 person-months of effort (centered on a software development hackathon) resulted in the design and implementation of proof-of-concept software for 4 core phylotastic components, 3 controllers, and 3 end-user demonstration tools. While these products have substantial limitations, they suggest considerable potential for a distributed system that makes phylogenetic knowledge readily accessible in computable form. Widespread use of phylotastic systems will create an electronic marketplace for sharing phylogenetic knowledge that will spur innovation in other areas of the ToL enterprise, such as annotation of sources and methods and third-party methods of quality assessment.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Not published, not indexed: issues in generating and finding hospice and palliative care literature.

INTRODUCTION: Accessing new knowledge as the evidence base for hospice and palliative care grows has specific challenges for the discipline. This study aimed to describe conversion rates of palliative and hospice care conference abstracts to journal articles and to highlight that some palliative care literature may not be retrievable because it is not indexed on bibliographic databases. METHODS: Substudy A tracked the journal publication of conference abstracts selected for inclusion in a gray literature database on www.caresearch.com.au . Abstracts were included in the gray literature database following handsearching of proceedings of over 100 Australian conferences likely to have some hospice or palliative care content that were held between 1980 and 1999. Substudy B looked at indexing from first publication until 2001 of three international hospice and palliative care journals in four widely available bibliographic databases through systematic tracing of all original papers in the journals. RESULTS: Substudy A showed that for the 1338 abstracts identified only 15.9% were published (compared to an average in health of 45%). Published abstracts were found in 78 different journals. Multiauthor abstracts and oral presentations had higher rates of conversion. Substudy B demonstrated lag time between first publication and bibliographic indexing. Even after listing, idiosyncratic noninclusions were identified. DISCUSSION: There are limitations to retrieval of all possible literature through electronic searching of bibliographic databases. Encouraging publication in indexed journals of studies presented at conferences, promoting selection of palliative care journals for database indexing, and searching more than one bibliographic database will improve the accessibility of existing and new knowledge in hospice and palliative care.

Facilitators and barriers to hypertension self-management in urban African Americans: perspectives of patients and family members.

INTRODUCTION: We aimed to inform the design of behavioral interventions by identifying patients' and their family members' perceived facilitators and barriers to hypertension self-management. MATERIALS AND METHODS: We conducted focus groups of African American patients with hypertension and their family members to elicit their views about factors influencing patients' hypertension self-management. We recruited African American patients with hypertension (n = 18) and their family members (n = 12) from an urban, community-based clinical practice in Baltimore, Maryland. We conducted four separate 90-minute focus groups among patients with controlled (one group) and uncontrolled (one group) hypertension, as well as their family members (two groups). Trained moderators used open-ended questions to assess participants' perceptions regarding patient, family, clinic, and community-level factors influencing patients' effective hypertension self-management. RESULTS: Patient participants identified several facilitators (including family members' support and positive relationships with doctors) and barriers (including competing health priorities, lack of knowledge about hypertension, and poor access to community resources) that influence their hypertension self-management. Family members also identified several facilitators (including their participation in patients' doctor's visits and discussions with patients' doctors outside of visits) and barriers (including their own limited health knowledge and patients' lack of motivation to sustain hypertension self-management behaviors) that affect their efforts to support patients' hypertension self-management. CONCLUSION: African American patients with hypertension and their family members reported numerous patient, family, clinic, and community-level facilitators and barriers to patients' hypertension self-management. Patients' and their family members' views may help guide efforts to tailor behavioral interventions designed to improve hypertension self-management behaviors and hypertension control in minority populations.

Informatic Opacity: Biometric Facial Recognition and the Aesthetics and Politics of Defacement

Confronting the rapidly increasing, worldwide reliance on biometric technologies to surveil, manage, and police human beings, my dissertation Informatic Opacity: Biometric Facial Recognition and the Aesthetics and Politics of Defacement charts a series of queer, feminist, and anti-racist concepts and artworks that favor opacity as a means of political struggle against surveillance and capture technologies in the 21st century. Utilizing biometric facial recognition as a paradigmatic example, I argue that today's surveillance requires persons to be informatically visible in order to control them, and such visibility relies upon the production of technical standardizations of identification to operate globally, which most vehemently impact non- normative, minoritarian populations. Thus, as biometric technologies turn exposures of the face into sites of governance, activists and artists strive to make the face biometrically illegible and refuse the political recognition biometrics promises through acts of masking, escape, and imperceptibility. Although I specifically describe tactics of making the face unrecognizable as "defacement," I broadly theorize refusals to visually cohere to digital surveillance and capture technologies' gaze as "informatic opacity," an aesthetic-political theory and practice of anti- normativity at a global, technical scale whose goal is maintaining the autonomous determination of alterity and difference by evading the quantification, standardization, and regulation of identity imposed by biometrics and the state. My dissertation also features two artworks: Facial Weaponization Suite, a series of masks and public actions, and Face Cages, a critical, dystopic installation that investigates the abstract violence of biometric facial diagramming and analysis. I develop an interdisciplinary, practice-based method that pulls from contemporary art and aesthetic theory, media theory and surveillance studies, political and continental philosophy, queer and feminist theory, transgender studies, postcolonial theory, and critical race studies.