Focus: Personalized Medicine: The Sociology of the Deceased Harvard Medical Unit at Boston City Hospital

Many graduates of the Harvard Medical Unit (HMU) at Boston City Hospital, in either the clinical training/residency program or the research program at the Thorndike Memorial Laboratory, contributed in major ways to the HMU and constantly relived their HMU experiences. The HMU staff physicians, descending from founder and mentor physicians Francis W. Peabody, Soma Weiss, and George R. Minot, were dedicated to the teaching, development, and leadership of its clinical and research trainees, whose confidence and dedication to patient care as a result of their mentorship led many to lifelong achievements as clinicians, teachers, and mentors. Their experience also led to a lifelong love of the HMU (despite its loss), camaraderie, happiness, and intense friendships with their associates.     

Multiple Manifold Clustering Using Curvature Constrained Path

The problem of multiple surface clustering is a challenging task, particularly when the surfaces intersect. Available methods such as Isomap fail to capture the true shape of the surface near by the intersection and result in incorrect clustering. The Isomap algorithm uses shortest path between points. The main draw back of the shortest path algorithm is due to the lack of curvature constrained where causes to have a path between points on different surfaces. In this paper we tackle this problem by imposing a curvature constraint to the shortest path algorithm used in Isomap. The algorithm chooses several landmark nodes at random and then checks whether there is a curvature constrained path between each landmark node and every other node in the neighborhood graph. We build a binary feature vector for each point where each entry represents the connectivity of that point to a particular landmark. Then the binary feature vectors could be used as a input of conventional clustering algorithm such as hierarchical clustering. We apply our method to simulated and some real datasets and show, it performs comparably to the best methods such as K-manifold and spectral multi-manifold clustering.     

The practical use of the A* algorithm for exact multiple sequence alignment.

Multiple alignment is an important problem in computational biology. It is well known that it can be solved exactly by a dynamic programming algorithm which in turn can be interpreted as a shortest path computation in a directed acyclic graph. The A* algorithm (or goal-directed unidirectional search) is a technique that speeds up the computation of a shortest path by transforming the edge lengths without losing the optimality of the shortest path. We implemented the A* algorithm in a computer program similar to MSA (Gupta et al., 1995) and FMA (Shibuya and Imai, 1997). We incorporated in this program new bounding strategies for both lower and upper bounds and show that the A* algorithm, together with our improvements, can speed up computations considerably. Additionally, we show that the A* algorithm together with a standard bounding technique is superior to the well-known Carrillo-Lipman bounding since it excludes more nodes from consideration.     

Optimal strip sequencing strategies for flexible manufacturing operations in two and three dimensions

In this paper, optimal strip strategies are developed for a variety of two-dimensional and three-dimensional sequencing problems arising in flexible manufacturing. These strategies are appropriate for CNC drilling operations, NC punching operations, and circuit board population, for example. Seven different metrics are considered.     

Real-time software methodologies: Are they suitable for developing Manufacturing control software?

Computer-Integrated Manufacturing (CIM) systems may be classified as real-time systems. Hence, the applicability of methodologies that are developed for specifying, designing, implementing, testing, and evolving real-time software is investigated in this article. The paper highlights the activities of the software development process. Among these activities, a great emphasis is placed on automating the software requirements specification activity, and a set of formal models and languages for specifying these requirements is presented. Moreover, a synopsis of the real-time software methodologies that have been implemented by the academic and industrial communities is presented together with a critique of the strengths and weaknesses of these methodologies. The possible use of the real-time methodologies in developing the control software of efficient and dependable manufacturing systems is explored. In these systems, efficiency is achieved by increasing the level of concurrency of the operations of a plan, and by scheduling the execution of these operations with the intent of maximizing the utilization of the devices of their systems. On the other hand, dependability requires monitoring the operations of these systems. This monitoring activity facilitates the detection of faults that may occur when executing the scheduled operations of a plan, recovering from these faults, and, whenever feasible, resuming the original schedule of the system. The paper concludes that the set of surveyed methodologies may be used to develop the real-time control software of efficient and dependable manufacturing systems. However, an integrated approach to planning, scheduling, and monitoring the operations of these systems will significantly enhance their utility, and no such approach is supported by any of these methodologies.     

DNA computing the Hamiltonian path problem

The directed Hamiltonian path (DHP) problem is one of the hard computational problems for which there is no practical algorithm on a conventional computer available. Many problems, including the traveling sales person problem and the longest path problem, can be translated into the DHP problem, which implies that an algorithm for DHP can also solve all the translated problems. To study the robustness of the laboratory protocol of the pioneering DNA computing for the DHP problem performed by Leonard Adleman (1994), we investigated how the graph size, multiplicity of the Hamiltonian paths, and the size of oligonucleotides that encode the vertices would affect the laboratory procedures. We applied Adleman's protocol with 18-mer oligonucleotide per node to a graph with 8 vertices and 14 edges containing two Hamiltonian paths (Adleman used 20-mer oligonucleotides for a graph with 7 nodes, 14 edges and one Hamiltonian path). We found that depending on the graph characteristics such as the number of short cycles, the oligonucleotide size, and the hybridization conditions that used to encode the graph, the protocol should be executed with different parameters from Adleman's.     

Integration of multi-objective PSO based feature selection and node centrality for medical datasets

In the past decades, the rapid growth of computer and database technologies has led to the rapid growth of large-scale medical datasets. On the other, medical applications with high dimensional datasets that require high speed and accuracy are rapidly increasing. One of the dimensionality reduction approaches is feature selection that can increase the accuracy of the disease diagnosis and reduce its computational complexity. In this paper, a novel PSO-based multi objective feature selection method is proposed. The proposed method consists of three main phases. In the first phase, the original features are showed as a graph representation model. In the next phase, feature centralities for all nodes in the graph are calculated, and finally, in the third phase, an improved PSO-based search process is utilized to final feature selection. The results on five medical datasets indicate that the proposed method improves previous related methods in terms of efficiency and effectiveness.     

Basic networks: Definition and applications

We define basic networks as the undirected subgraphs with minimal number of units in which the distances (geodesics, minimal path lengths) among a set of selected nodes, which we call seeds, in the original graph are conserved. The additional nodes required to draw the basic network are called connectors. We describe a heuristic strategy to find the basic networks of complex graphs. We also show how the characterization of these networks may help to obtain relevant biological information from highly complex protein-protein interaction data.     

Reduced 5-hydroxymethyluracil-DNA glycosylase activity in Werner's syndrome cells.

Werner's syndrome (WS) is an autosomal recessive disease marked by early symptoms of accelerated aging. There is evidence indicating accumulation of oxidized DNA bases to be a major factor in cellular aging. The first step of excision repair of such bases in human cells is their removal from DNA by glycosylases. 5-Hydroxymethyluracil (HMU)-DNA glycosylase excises HMU from DNA; another glycosylase removes many non-aromatic pyrimidine derivatives. Levels of glycosylases that excise oxidized pyrimidines from DNA were compared between confluent and proliferating populations of WS cells, age-matched controls, and young control cells. They were assayed by measurements of direct release of free bases from their respective DNA substrates. Specific activities of the glycosylase that releases various modified pyrimidines and of uracil-DNA glycosylase (which removes uracil from DNA) were essentially the same in all cell lines. Cell cycle variations of these enzymes also did not differ between WS and control cells. HMU-DNA glycosylase specific activity was reduced in WS cells. Reduction of HMU-DNA glycosylase has been described in senescent human WI-38 cells. Therefore, while neither WS nor senescent cells have overall deficiencies of DNA glycosylase activities, they both might have reduced excision of HMU from DNA. This indicates a possible role of HMU accumulation in the aging process.     

Biological Activity of 5-Hydroxymethyluracil and Its Deoxynucleoside in Noninfected and Phageinfected Bacillus subtilis

(14)C-hydroxymethyldeoxyuridine (dHMU) is specifically incorporated into the deoxyribonucleic acid (DNA) of bacteriophage SP8. Incorporation experiments demonstrate that the initiation of phage SP8 DNA synthesis occurs between 12.5 to 15 min after infection. Incorporation into host DNA does not occur. (14)C-dHMU can be used as an analytical tool for screening conditionally lethal phage mutants containing hydroxymethyluracil in their DNA to select those that are defective in DNA synthesis under restrictive conditions. The pyrimidine, (14)C-hydroxymethyluracil (HMU), is not incorporated into bacterial or phage DNA. Neither HMU nor dHMU can replace thymine as a growth requirement for Bacillus subtilis 168 Ind(-) Thy(-). HMU does not inhibit the utilization of thymine. Although dHMU inhibits deoxythymidine utilization, the inhibition is not competitive.     

Frequency Response and Gap Tuning for Nonlinear Electrical Oscillator Networks

We study nonlinear electrical oscillator networks, the smallest example of which consists of a voltage-dependent capacitor, an inductor, and a resistor driven by a pure tone source. By allowing the network topology to be that of any connected graph, such circuits generalize spatially discrete nonlinear transmission lines/lattices that have proven useful in high-frequency analog devices. For such networks, we develop two algorithms to compute the steady-state response when a subset of nodes are driven at the same fixed frequency. The algorithms we devise are orders of magnitude more accurate and efficient than stepping towards the steady-state using a standard numerical integrator. We seek to enhance a given network''s nonlinear behavior by altering the eigenvalues of the graph Laplacian, i.e., the resonances of the linearized system. We develop a Newton-type method that solves for the network inductances such that the graph Laplacian achieves a desired set of eigenvalues; this method enables one to move the eigenvalues while keeping the network topology fixed. Running numerical experiments using three different random graph models, we show that shrinking the gap between the graph Laplacian''s first two eigenvalues dramatically improves a network''s ability to (i) transfer energy to higher harmonics, and (ii) generate large-amplitude signals. Our results shed light on the relationship between a network''s structure, encoded by the graph Laplacian, and its function, defined in this case by the presence of strongly nonlinear effects in the frequency response.     

Comparing advanced graph-theoretical parameters of the connectomes of the lobes of the human brain

Deep, classical graph-theoretical parameters, like the size of the minimum vertex cover, the chromatic number, or the eigengap of the adjacency matrix of the graph were studied widely by mathematicians in the last century. Most researchers today study much simpler parameters of braingraphs or connectomes which were defined in the last twenty years for enormous networks—like the graph of the World Wide Web—with hundreds of millions of nodes. Since the connectomes, describing the connections of the human brain, typically contain several hundred vertices today, one can compute and analyze the much deeper, harder-to-compute classical graph parameters for these, relatively small graphs of the brain. This deeper approach has proven to be very successful in the comparison of the connectomes of the sexes in our earlier works: we have shown that graph parameters, deeply characterizing the graph connectivity are significantly better in women’s connectomes than in men’s. In the present contribution we compare numerous graph parameters in the three largest lobes—frontal, parietal, temporal—and in both hemispheres of the human brain. We apply the diffusion weighted imaging data of 423 subjects of the NIH-funded Human Connectome Project, and present some findings, never described before, including that the right parietal lobe contains significantly more edges, has higher average degree, density, larger minimum vertex cover and Hoffman bound than the left parietal lobe. Similar advantages in the deep graph connectivity properties are held for the left frontal versus the right frontal and the right temporal versus the left temporal lobes.     

Functional essentiality from topology features in metabolic networks: a case study in yeast

The relation between the position of mutations in Saccharomyces cerevisiae metabolic network and their lethality is the subject of this work. We represent the topology of the network by a directed graph: nodes are metabolites and arcs represent the reactions; a mutation corresponds to the removal of all the arcs referring to the deleted enzyme. Using publicly available knock-out data, we show that lethality corresponds to the lack of alternative paths in the perturbed network linking the nodes affected by the enzyme deletion. Such feature is at the basis of the recently recognized importance of 'marginal' arcs of metabolic networks.     

Amplification of Nucleic Acids by Polymerase Chain Reaction (PCR) and Other Methods and their Applications

Abstract

The in vitro replication of DNA, principally using the polymerase chain reaction (PCR), permits the amplification of defined sequences of DNA. By exponentially amplifying a target sequence, PCR significantly enhances the probability of detecting target gene sequences in complex mixtures of DNA. It also facilitates the cloning and sequencing of genes. Amplification of DNA by PCR and other newly developed methods has been applied in many areas of biological research, including molecular biology, biotechnology, and medicine, permitting studies that were not possible before. Nucleic acid amplification has added a new and revolutionary dimension to molecular biology. This review examines PCR and other in vitro nucleic acid amplification methodologies—examining the critical parameters and variations and their widespread applications—giving the strengths and limitations of these methodologies.     

Computer-assisted sequencing,interval graphs,and molecular evolution

In 1945, Fox developed the strategy for sequencing long proteins by using overlapping fragments. We show how the formal mathematical technique for the construction of interval graphs (Gilmore and Hoffman, 1964) is useful both pedagogically for understanding the underlying logic of sequencing linear molecules and is more amenable to automation because of its algorithmic nature. We also present a computer program, that employs the interval graph algorithm, which can be used to sequence proteins when given digest data. An example is given to illustrate all the steps involved in the algorithmic processing of the data. The need for such developments with respect to molecular evolution is discussed.     

The hippocampus as a cognitive graph

A theory of cognitive mapping is developed that depends only on accepted properties of hippocampal function, namely, long-term potentiation, the place cell phenomenon, and the associative or recurrent connections made among CA3 pyramidal cells. It is proposed that the distance between the firing fields of connected pairs of CA3 place cells is encoded as synaptic resistance (reciprocal synaptic strength). The encoding occurs because pairs of cells with coincident or overlapping fields will tend to fire together in time, thereby causing a decrease in synaptic resistance via long-term potentiation; in contrast, cells with widely separated fields will tend never to fire together, causing no change or perhaps (via long-term depression) an increase in synaptic resistance. A network whose connection pattern mimics that of CA3 and whose connection weights are proportional to synaptic resistance can be formally treated as a weighted, directed graph. In such a graph, a "node" is assigned to each CA3 cell and two nodes are connected by a "directed edge" if and only if the two corresponding cells are connected by a synapse. Weighted, directed graphs can be searched for an optimal path between any pair of nodes with standard algorithms. Here, we are interested in finding the path along which the sum of the synaptic resistances from one cell to another is minimal. Since each cell is a place cell, such a path also corresponds to a path in two-dimensional space. Our basic finding is that minimizing the sum of the synaptic resistances along a path in neural space yields the shortest (optimal) path in unobstructed two-dimensional space, so long as the connectivity of the network is great enough. In addition to being able to find geodesics in unobstructed space, the same network enables solutions to the "detour" and "shortcut" problems, in which it is necessary to find an optimal path around a newly introduced barrier and to take a shorter path through a hole opened up in a preexisting barrier, respectively. We argue that the ability to solve such problems qualifies the proposed hippocampal object as a cognitive map. Graph theory thus provides a sort of existence proof demonstrating that the hippocampus contains the necessary information to function as a map, in the sense postulated by others (O'Keefe, J., and L. Nadel. 1978. The Hippocampus as a Cognitive Map. Clarendon Press, Oxford, UK). It is also possible that the cognitive mapping functions of the hippocampus are carried out by parallel graph searching algorithms implemented as neural processes. This possibility has the great attraction that the hippocampus could then operate in much the same way to find paths in general problem space; it would only be necessary for pyramidal cells to exhibit a strong nonpositional firing correlate.     

A graph-theoretic approach to comparing and integrating genetic, physical and sequence-based maps

For many species, multiple maps are available, often constructed independently by different research groups using different sets of markers and different source material. Integration of these maps provides a higher density of markers and greater genome coverage than is possible using a single study. In this article, we describe a novel approach to comparing and integrating maps by using abstract graphs. A map is modeled as a directed graph in which nodes represent mapped markers and edges define the order of adjacent markers. Independently constructed graphs representing corresponding maps from different studies are merged on the basis of their common loci. Absence of a path between two nodes indicates that their order is undetermined. A cycle indicates inconsistency among the mapping studies with regard to the order of the loci involved. The integrated graph thus produced represents a complete picture of all of the mapping studies that comprise it, including all of the ambiguities and inconsistencies among them. The objective of this representation is to guide additional research aimed at interpreting these ambiguities and inconsistencies in locus order rather than presenting a "consensus order" that ignores these problems.     

Use of techniques derived from graph theory to compare secondary structure motifs in proteins

A substructure matching algorithm is described that can be used for the automatic identification of secondary structural motifs in three-dimensional protein structures from the Protein Data Bank. The proteins and motifs are stored for searching as labelled graphs, with the nodes of a graph corresponding to linear representations of helices and strands and the edges to the inter-line angles and distances. A modification of Ullman's subgraph isomorphism algorithm is described that can be used to search these graph representations. Tests with patterns from the protein structure literature demonstrate both the efficiency and the effectiveness of the search procedure, which has been implemented in FORTRAN 77 on a MicroVAX-II system, coupled to the molecular fitting program FRODO on an Evans and Sutherland PS300 graphics system.