Orthogonal orientation control of carbon nanotube growth.

Carbon nanotubes (CNTs) have attracted attention for their remarkable electrical properties and have being explored as one of the best building blocks in nano-electronics. A key challenge to realize such potential is the control of the nanotube growth directions. Even though both vertical growth and controlled horizontal growth of carbon nanotubes have been realized before, the growth of complex nanotube structures with both vertical and horizontal orientation control on the same substrate has never been achieved. Here, we report a method to grow three-dimensional (3D) complex nanotube structures made of vertical nanotube forests and horizontal nanotube arrays on a single substrate and from the same catalyst pattern by an orthogonally directed nanotube growth method using chemical vapor deposition (CVD). More importantly, such a capability represents a major advance in controlled growth of carbon nanotubes. It enables researchers to control the growth directions of nanotubes by simply changing the reaction conditions. The high degree of control represented in these experiments will surely make the fabrication of complex nanotube devices a possibility.

Ras controls melanocyte expansion during zebrafish fin stripe regeneration.

Regenerative medicine for complex tissues like limbs will require the provision or activation of precursors for different cell types, in the correct number, and with the appropriate instructions. These strategies can be guided by what is learned from spectacular events of natural limb or fin regeneration in urodele amphibians and teleost fish. Following zebrafish fin amputation, melanocyte stripes faithfully regenerate in tandem with complex fin structures. Distinct populations of melanocyte precursors emerge and differentiate to pigment regenerating fins, yet the regulation of their proliferation and patterning is incompletely understood. Here, we found that transgenic increases in active Ras dose-dependently hyperpigmented regenerating zebrafish fins. Lineage tracing and marker analysis indicated that increases in active Ras stimulated the in situ amplification of undifferentiated melanocyte precursors expressing mitfa and kita. Active Ras also hyperpigmented early fin regenerates of kita mutants, which are normally devoid of primary regeneration melanocytes, suppressing defects in precursor function and survival. By contrast, this protocol had no noticeable impact on pigmentation by secondary regulatory melanocyte precursors in late-stage kita regenerates. Our results provide evidence that Ras activity levels control the repopulation and expansion of adult melanocyte precursors after tissue loss, enabling the recovery of patterned melanocyte stripes during zebrafish appendage regeneration.

Structural Basis of RNA Recognition by Mycobacterium tuberculosis MazF Homologues

The MazEF toxin-antitoxin (TA) system consists of the antitoxin MazE and the toxin MazF. MazF is a sequence-specific endoribonuclease that upon activation causes cellular growth arrest and increass the level of persisters. Moreover, MazF-induced cells are in a quasi-dormant state that cells remain metabolically active while stop dividing. The quasi-dormancy is similar to the nonreplicating state of M. tuberculosis during latent tuberculosis, thus suggesting the role of mazEF in M. tuberculosis dormancy and persistence. M. tuberculosis has nine mazEF TA modules, each with different RNA cleavage specificities and implicated in selective gene expression during stress conditions. To date only the Bacillus subtilis MazF-RNA complex structure has been determined. As M. tuberculosis MazF homologues recognize distinct RNA sequences, their molecular mechanisms of substrate specificity remain unclear. By taking advantage of X-ray crystallography, we have determined structures of two M. tuberculosis MazF-RNA complexes, MazF-mt1 (Rv2801c) and MazF-mt3 (Rv1991c) in complex with an uncleavable RNA substrate. These structures have provided the molecular basis of sequence-specific RNA recognition and cleavage by MazF toxins.

Both MazF-mt1-RNA and MazF-mt3-RNA complexes showed similar structural organization with one molecule of RNA bound to a MazF-mt1 or MazF-mt3 dimer and occupying the same pocket within the MazF dimer interface. Similar to B. subtilis MazF-RNA complex, MazF-mt1 and MazF-mt3 displayed a conserved active site architecture, where two highly conserved residues, Arg and Thr, form hydrogen bonds with the scissile phosphate group in the cleavage site of the bound RNA. The MazF-mt1-RNA complex also showed specific interactions with its three-base RNA recognition element. Compared with the B. subtilis MazF-RNA complex, our structures showed that residues involved in sequence-specific recognition of target RNA vary between the MazF homologues, therefore explaining the molecular basis for their different RNA recognition sequences. In addition, local conformational changes of the loops in the RNA binding site of MazF-mt1 appear to play a role in MazF targeting different RNA lengths and sequences. In contrast, the MazF-mt3-RNA complex is in a non-optimal RNA binding state with a symmetry-related MazF-mt3 molecule found to make interactions with the bound RNA in the crystal. The crystal-packing interactions were further examined by isothermal titration calorimetry (ITC) studies on selected MazF-mt3 mutants. Our attempts to utilize a MazF-mt3 mutant bearing mutations involved in crystal contacts all crystallized with few nucleotides, which are still found to interact with a symmetry mate. However, these different crystal forms revealed the conformational flexibility of loops in the RNA binding interface of MazF-mt3, suggesting their role in RNA binding and recognition, which will require further studies on additional MazF-mt3-RNA complex interactions.

In conclusion, the structures of the MazF-mt1-RNA and MazF-mt3-RNA complexes provide the first structural information on any M. tuberculosis MazF homologues. Supplemented with structure-guided mutational studies on MazF toxicity in vivo, this study has addressed the structural basis of different RNA cleavage specificities among MazF homologues. Our work will guide future studies on the function of other M. tuberculosis MazF and MazE-MazF homologues, and will help delineate their physiological roles in M. tuberculosis stress responses and pathogenesis.

Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas

Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden variables and share statistical strength across heterogeneous sources. In the first part of this dissertation, we develop two dependent variational inference methods for full posterior approximation in non-conjugate Bayesian models through hierarchical mixture- and copula-based variational proposals, respectively. The proposed methods move beyond the widely used factorized approximation to the posterior and provide generic applicability to a broad class of probabilistic models with minimal model-specific derivations. In the second part of this dissertation, we design probabilistic graphical models to accommodate multimodal data, describe dynamical behaviors and account for task heterogeneity. In particular, the sparse latent factor model is able to reveal common low-dimensional structures from high-dimensional data. We demonstrate the effectiveness of the proposed statistical learning methods on both synthetic and real-world data.

Vascular structures for volumetric cooling and mechanical strength

When solid material is removed in order to create flow channels in a load carrying structure, the strength of the structure decreases. On the other hand, a structure with channels is lighter and easier to transport as part of a vehicle. Here, we show that this trade off can be used for benefit, to design a vascular mechanical structure. When the total amount of solid is fixed and the sizes, shapes, and positions of the channels can vary, it is possible to morph the flow architecture such that it endows the mechanical structure with maximum strength. The result is a multifunctional structure that offers not only mechanical strength but also new capabilities necessary for volumetric functionalities such as self-healing and self-cooling. We illustrate the generation of such designs for strength and fluid flow for several classes of vasculatures: parallel channels, trees with one, two, and three bifurcation levels. The flow regime in every channel is laminar and fully developed. In each case, we found that it is possible to select not only the channel dimensions but also their positions such that the entire structure offers more strength and less flow resistance when the total volume (or weight) and the total channel volume are fixed. We show that the minimized peak stress is smaller when the channel volume (φ) is smaller and the vasculature is more complex, i.e., with more levels of bifurcation. Diminishing returns are reached in both directions, decreasing φ and increasing complexity. For example, when φ=0.02 the minimized peak stress of a design with one bifurcation level is only 0.2% greater than the peak stress in the optimized vascular design with two levels of bifurcation. © 2010 American Institute of Physics.

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.

Evaluating functional network inference using simulations of complex biological systems.

MOTIVATION: Although many network inference algorithms have been presented in the bioinformatics literature, no suitable approach has been formulated for evaluating their effectiveness at recovering models of complex biological systems from limited data. To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated data. RESULTS: We designed a simulator to generate data representing a complex biological system at multiple levels of organization: behaviour, neural anatomy, brain electrophysiology, and gene expression of songbirds. About 90% of the simulated variables are unregulated by other variables in the system and are included simply as distracters. We sampled the simulated data at intervals as one would sample from a biological system in practice, and then used the sampled data to evaluate the effectiveness of an algorithm we developed for functional network inference. We found that our algorithm is highly effective at recovering the functional network structure of the simulated system-including the irrelevance of unregulated variables-from sampled data alone. To assess the reproducibility of these results, we tested our inference algorithm on 50 separately simulated sets of data and it consistently recovered almost perfectly the complex functional network structure underlying the simulated data. To our knowledge, this is the first approach for evaluating the effectiveness of functional network inference algorithms at recovering models from limited data. Our simulation approach also enables researchers a priori to design experiments and data-collection protocols that are amenable to functional network inference.

Peer-Informed Learning on Increasing Contraceptive Knowledge Among Women in Rural Haiti

Contraceptive prevalence in Haiti remains low despite extensive foreign aid targeted at improving family planning. [1] Earlier studies have found that peer-informed learning have been successful in promoting sexual and reproductive health. [2-5] This pilot project was implemented as a three-month, community-based, educational intervention to assess the impact of peer education in increasing contraceptive knowledge among women in Fondwa, Haiti. Research investigators conducted contraceptive information trainings to pre-identified female leaders of existing women’s groups in Fondwa, who were recruited as peer educators (n=4). Later, these female leaders shared the knowledge from the training with the test participants in the women’s group (n=23) through an information session. Structured surveys measuring knowledge of contraceptives were conducted with all participants before the intervention began, at the end of the intervention, and four weeks after the intervention. The surveys measured general contraceptive knowledge, knowledge about eight selected types of modern contraceptives and contraceptive preferences and attitudes. Only test participants showed significant improvement in their general contraceptive knowledge score (p<0.001), but both test participants and peer educators showed significant improvement in overall knowledge scores for identifying the types and uses of modern contraceptive methods. Assessment for knowledge retention remained significantly higher four weeks after the intervention than prior to the intervention. Therefore, a one-time, three-hour peer-based educational intervention using existing social structures is effective, and might be valuable in a population with minimal access to education and little to no knowledge about contraceptives.

Muscle Logic: New Knowledge Resource for Anatomy Enables Comprehensive Searches of the Literature on the Feeding Muscles of Mammals.

BACKGROUND: In recent years large bibliographic databases have made much of the published literature of biology available for searches. However, the capabilities of the search engines integrated into these databases for text-based bibliographic searches are limited. To enable searches that deliver the results expected by comparative anatomists, an underlying logical structure known as an ontology is required. DEVELOPMENT AND TESTING OF THE ONTOLOGY: Here we present the Mammalian Feeding Muscle Ontology (MFMO), a multi-species ontology focused on anatomical structures that participate in feeding and other oral/pharyngeal behaviors. A unique feature of the MFMO is that a simple, computable, definition of each muscle, which includes its attachments and innervation, is true across mammals. This construction mirrors the logical foundation of comparative anatomy and permits searches using language familiar to biologists. Further, it provides a template for muscles that will be useful in extending any anatomy ontology. The MFMO is developed to support the Feeding Experiments End-User Database Project (FEED, https://feedexp.org/), a publicly-available, online repository for physiological data collected from in vivo studies of feeding (e.g., mastication, biting, swallowing) in mammals. Currently the MFMO is integrated into FEED and also into two literature-specific implementations of Textpresso, a text-mining system that facilitates powerful searches of a corpus of scientific publications. We evaluate the MFMO by asking questions that test the ability of the ontology to return appropriate answers (competency questions). We compare the results of queries of the MFMO to results from similar searches in PubMed and Google Scholar. RESULTS AND SIGNIFICANCE: Our tests demonstrate that the MFMO is competent to answer queries formed in the common language of comparative anatomy, but PubMed and Google Scholar are not. Overall, our results show that by incorporating anatomical ontologies into searches, an expanded and anatomically comprehensive set of results can be obtained. The broader scientific and publishing communities should consider taking up the challenge of semantically enabled search capabilities.