Universal computing by DNA origami robots in a living animal

Biological systems are collections of discrete molecular objects that move around and collide with each other. Cells carry out elaborate processes by precisely controlling these collisions, but developing artificial machines that can interface with and control such interactions remains a significant challenge. DNA is a natural substrate for computing and has been used to implement a diverse set of mathematical problems1-3, logic circuits4-6 and robotics7-9. The molecule also naturally interfaces with living systems, and different forms of DNA-based biocomputing have previously been demonstrated10-13. Here we show that DNA origami14-16 can be used to fabricate nanoscale robots that are capable of dynamically interacting with each other17-18 in a living animal. The interactions generate logical outputs, which are relayed to switch molecular payloads on or off. As a proof-of-principle, we use the system to create architectures that emulate various logic gates (AND, OR, XOR, NAND, NOT, CNOT, and a half adder). Following an ex vivo prototyping phase, we successfully employed the DNA origami robots in living cockroaches (Blaberus discoidalis) to control a molecule that targets the cells of the animal

Barcoding cells using cell-surface programmable DNA-binding domains

We develop here a novel approach to barcode large numbers of cells through cell-surface expression of programmable zinc-finger DNA-binding domains (sZFs). We show sZFs enable double-stranded DNA to sequence-specifically label living cells, and also develop a sequential tagging approach to in situ image >3 cell types using just 3 fluorophores. Finally we demonstrate their broad versatility through ability to serve as surrogate reporters and facilitate selective cell capture and targeting

Feature selection and nearest centroid classification for protein mass spectrometry

BACKGROUND: The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard supervised classification algorithms can be employed, the "curse of dimensionality" needs to be solved. Due to the sheer amount of information contained within the mass spectra, most standard machine learning techniques cannot be directly applied. Instead, feature selection techniques are used to first reduce the dimensionality of the input space and thus enable the subsequent use of classification algorithms. This paper examines feature selection techniques for proteomic mass spectrometry. RESULTS: This study examines the performance of the nearest centroid classifier coupled with the following feature selection algorithms. Student-t test, Kolmogorov-Smirnov test, and the P-test are univariate statistics used for filter-based feature ranking. From the wrapper approaches we tested sequential forward selection and a modified version of sequential backward selection. Embedded approaches included shrunken nearest centroid and a novel version of boosting based feature selection we developed. In addition, we tested several dimensionality reduction approaches, namely principal component analysis and principal component analysis coupled with linear discriminant analysis. To fairly assess each algorithm, evaluation was done using stratified cross validation with an internal leave-one-out cross-validation loop for automated feature selection. Comprehensive experiments, conducted on five popular cancer data sets, revealed that the less advocated sequential forward selection and boosted feature selection algorithms produce the most consistent results across all data sets. In contrast, the state-of-the-art performance reported on isolated data sets for several of the studied algorithms, does not hold across all data sets. CONCLUSION: This study tested a number of popular feature selection methods using the nearest centroid classifier and found that several reportedly state-of-the-art algorithms in fact perform rather poorly when tested via stratified cross-validation. The revealed inconsistencies provide clear evidence that algorithm evaluation should be performed on several data sets using a consistent (i.e., non-randomized, stratified) cross-validation procedure in order for the conclusions to be statistically sound

A network flow algorithm for just-in-time project scheduling

We show the polynomial solvability of the PERT-COST project scheduling problem in the case of: (i) the objective being a piecewise-linear, convex (possibly, non- monotone) function of the job durations as well as of job start/finish times, and (ii) the precedence relations between jobs being presented in the form of a general (not necessary, acyclic) directed graph with arc lengths of any sign. For the latter problem, we present a network flow algorithm (of pseudo-linear complexity) which is easy to implement and which behaves well when the objective values grow slowly with the growth of the problem size while the number of breakpoints in the objective grows fast

Shape Detection, Analysis and Recognition

Technical report TR02-18. This paper surveys the various techniques for shape recognition and analysis with emphasis on robustness. Specifically, a review of boundary shape analysis methods and techniques is presented followed by description and experimental results of a new technique, based on Markov Shape Theory, that may alleviate the problems experienced by classical methods for shape analysis and boundary extraction in the presence of various kinds of noise. | TRID-ID TR02-1

Heterogeneous Stacking for Classification-Driven Watershed Segmentation

Marker-driven watershed segmentation attempts to extract seeds that indicate the presence of objects within an image. These markers are subsequently used to enforce regional minima within a topological surface used by the watershed algorithm. The classification-driven watershed segmentation (CDWS) algorithm improved the production of markers and topological surface by employing two machine-learned pixel classifiers. The probability maps produced by the two classifiers were utilized for creating markers, object boundaries, and the topological surface. This paper extends the CDWS algorithm by (i) enabling automated feature extraction via independent components analysis and (ii) improving the segmentation accuracy by introducing heterogeneous stacking. Heterogeneous stacking, an extension of stacked generalization for object delineation, improves pixel labeling and segmentation by training base classifiers on multiple target concepts extracted from the original ground truth, which are subsequently fused by the second set of classifiers. Experimental results demonstrate the effectiveness of the proposed system on real world images, and indicate significant improvement in segmentation quality over the base system

Proteomic Pattern Recognition

Technical report TR04-10. This report overviews the Mass Spectrometry Data Classification and Feature Extraction problem. After reviewing previous research new classification and feature extraction techniques are presented and empirically evaluated on three data sets. One of the key points made in this work, is that feature extraction techniques are composed of dimensionality reduction and feature selection methods. However, the two notions are quite different. The need for dimensionality reduction stems from the fact that classification algorithms cannot cope with the large number of input variables. On the other hand, feature selection techniques attempt to remove irrelevant and/or redundant features. Often classification algorithms cannot handle both a large number of variables and irrelevant variables that are not needed or even worse are misleading. In order to evaluate the dimensionality reduction and feature selection techniques, we use a simple classifier to evaluate performance. This makes the approach tractable. The experiments indicate that feature selection algorithms tend to both reduce data dimensionality and increase classification accuracy, while the studied dimensionality reduction technique sacrifices performance as a result of lowering the number of features a learning algorithm needs to deal with. | TRID-ID TR04-1