MOTIVATION: Confident deconvolution of proteomic spectra is critical for several applications such as de novo sequencing, cross-linking mass spectrometry and handling chimeric mass spectra. RESULTS: In general, all deconvolution algorithms may eventually report mass peaks that are not compatible with the chemical formula of any peptide. We show how to remove these artifacts by considering their mass defects. We introduce Y.A.D.A. 3.0, a fast deconvolution algorithm that can remove peaks with unacceptable mass defects. Our approach is effective for polypeptides with less than 10 kDa, and its essence can be easily incorporated into any deconvolution algorithm. AVAILABILITY AND IMPLEMENTATION: Y.A.D.A. 3.0 is freely available for academic use at http://patternlabforproteomics.org/yada3. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

MOTIVATION: We present the first tool for unbiased quality control of top-down proteomics datasets. Our tool can select high-quality top-down proteomics spectra, serve as a gateway for building top-down spectral libraries and, ultimately, improve identification rates. RESULTS: We demonstrate that a twofold rate increase for two E. coli top-down proteomics datasets may be achievable. AVAILABILITY AND IMPLEMENTATION: http://patternlabforproteomics.org/tdgc, freely available for academic use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Around 75% of all mass spectra remain unidentified by widely adopted proteomic strategies. We present DiagnoProt, an integrated computational environment that can efficiently cluster millions of spectra and use machine learning to shortlist high-quality unidentified mass spectra that are discriminative of different biological conditions. RESULTS: We exemplify the use of DiagnoProt by shortlisting 4366 high-quality unidentified tandem mass spectra that are discriminative of different types of the Aspergillus fungus. AVAILABILITY AND IMPLEMENTATION: DiagnoProt, a demonstration video and a user tutorial are available at http://patternlabforproteomics.org/diagnoprot . CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Given a connected region in two-dimensional space where events of a certain kind occur according to a certain time-varying density, we consider the problem of setting up a network of autonomous mobile agents to detect the occurrence of those events and possibly record them in as effective a manner as possible. We assume that agents can communicate with one another wirelessly within a fixed communication radius, and moreover that initially no agent has any information regarding the event density. We introduce a new distributed algorithm for agent control based on the notion of an execution mode, which essentially lets each agent roam the target region either at random or following its local view of a density-dependent gradient. Agents can switch back and forth between the two modes, and the precise manner of such changes depends on the setting of various parameters that can be adjusted as a function of the application at hand. We provide simulation results on some synthetic applications especially designed to highlight the algorithm's behavior relative to the possible execution modes.

Abstract We revisit the deadlock‐prevention problem by focusing on priority digraphs instead of the traditional wait‐for digraphs. This has allowed us to formulate deadlock prevention in terms of prohibiting the occurrence of directed cycles even in the most general of wait models (the so‐called AND‐OR model, in which prohibiting wait‐for directed cycles is generally overly restrictive). For a particular case in which the priority digraphs are somewhat simplified, we introduce a Las Vegas probabilistic mechanism for resource granting and analyze its key aspects in detail. © 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 63(2), 203–210 2014

Given two subsets A and B of nodes in a directed graph, the conduciveness of the graph from A to B is the ratio representing how many of the edges outgoing from nodes in A are incoming to nodes in B. When the graph's nodes stand for the possible solutions to certain problems of combinatorial optimization, choosing its edges appropriately has been shown to lead to conduciveness properties that provide useful insight into the performance of algorithms to solve those problems. Here we study the conduciveness of CA-rule graphs, that is, graphs whose node set is the set of all CA rules given a cell's number of possible states and neighborhood size. We consider several different edge sets interconnecting these nodes, both deterministic and random ones, and derive analytical expressions for the resulting graph's conduciveness toward rules having a fixed number of non-quiescent entries. We demonstrate that one of the random edge sets, characterized by allowing nodes to be sparsely interconnected across any Hamming distance between the corresponding rules, has the potential of providing reasonable conduciveness toward the desired rules. We conjecture that this may lie at the bottom of the best strategies known to date for discovering complex rules to solve specific problems, all of an evolutionary nature.

SUMMARY: Protein identification by mass spectrometry is commonly accomplished using a peptide sequence matching search algorithm, whose sensitivity varies inversely with the size of the sequence database and the number of post-translational modifications considered. We present the Spectrum Identification Machine, a peptide sequence matching tool that capitalizes on the high-intensity b1-fragment ion of tandem mass spectra of peptides coupled in solution with phenylisotiocyanate to confidently sequence the first amino acid and ultimately reduce the search space. We demonstrate that in complex search spaces, a gain of some 120% in sensitivity can be achieved. AVAILABILITY: All data generated and the software are freely available for academic use at http://proteomics.fiocruz.br/software/sim. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: We present an updated version of the TFold software for pinpointing differentially expressed proteins in shotgun proteomics experiments. Given an FDR bound, the updated approach uses a theoretical FDR estimator to maximize the number of identifications that satisfy both a fold-change cutoff that varies with the t-test P-value as a power law and a stringency criterion that aims to detect lowly abundant proteins. The new version has yielded significant improvements in sensitivity over the previous one. AVAILABILITY: Freely available for academic use at http://pcarvalho.com/patternlab.

Abstract Summary: We present an approach to statistically pinpoint differentially expressed proteins that have quantitation values near the quantitation threshold and are not identified in all replicates (marginal cases). Our method uses a Bayesian strategy to combine parametric statistics with an empirical distribution built from the reproducibility quality of the technical replicates. Availability:The software is freely available for academic use at http://pcarvalho.com/patternlab. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: XDIA is a computational strategy for analyzing multiplexed spectra acquired using electron transfer dissociation and collision-activated dissociation; it significantly increases identified spectra (approximately 250%) and unique peptides (approximately 30%) when compared with the data-dependent ETCaD analysis on middle-down, single-phase shotgun proteomic analysis. Increasing identified spectra and peptides improves quantitation statistics confidence and protein coverage, respectively. AVAILABILITY: The software and data produced in this work are freely available for academic use at http://fields.scripps.edu/XDIA CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Summary: YADA can deisotope and decharge high-resolution mass spectra from large peptide molecules, link the precursor monoisotopic peak information to the corresponding tandem mass spectrum, and account for different co-fragmenting ion species (multiplexed spectra). We describe how YADA enables a pipeline consisting of ProLuCID and DTASelect for analyzing large-scale middle-down proteomics data. Availability: http://fields.scripps.edu/yada Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

We consider flocks of artificial birds and study the emergence of V-like formations during flight. We introduce a small set of fully distributed positioning rules to guide the birds' movements and demonstrate, by means of simulations, that they tend to lead to stabilization into several of the well-known V-like formations that have been observed in nature. We also provide quantitative indicators that we believe are closely related to achieving V-like formations, and study their behavior over a large set of independent simulations.

BACKGROUND: A goal of proteomics is to distinguish between states of a biological system by identifying protein expression differences. Liu et al. demonstrated a method to perform semi-relative protein quantitation in shotgun proteomics data by correlating the number of tandem mass spectra obtained for each protein, or "spectral count", with its abundance in a mixture; however, two issues have remained open: how to normalize spectral counting data and how to efficiently pinpoint differences between profiles. Moreover, Chen et al. recently showed how to increase the number of identified proteins in shotgun proteomics by analyzing samples with different MS-compatible detergents while performing proteolytic digestion. The latter introduced new challenges as seen from the data analysis perspective, since replicate readings are not acquired. RESULTS: To address the open issues above, we present a program termed PatternLab for proteomics. This program implements existing strategies and adds two new methods to pinpoint differences in protein profiles. The first method, ACFold, addresses experiments with less than three replicates from each state or having assays acquired by different protocols as described by Chen et al. ACFold uses a combined criterion based on expression fold changes, the AC test, and the false-discovery rate, and can supply a "bird's-eye view" of differentially expressed proteins. The other method addresses experimental designs having multiple readings from each state and is referred to as nSVM (natural support vector machine) because of its roots in evolutionary computing and in statistical learning theory. Our observations suggest that nSVM's niche comprises projects that select a minimum set of proteins for classification purposes; for example, the development of an early detection kit for a given pathology. We demonstrate the effectiveness of each method on experimental data and confront them with existing strategies. CONCLUSION: PatternLab offers an easy and unified access to a variety of feature selection and normalization strategies, each having its own niche. Additionally, graphing tools are available to aid in the analysis of high throughput experimental data. PatternLab is available at http://pcarvalho.com/patternlab.

When a program is loaded into memory for execution the relative position of its basic blocks is crucial, since loading basic blocks that are unlikely to be executed first places them high in the instruction-memory hierarchy only to be dislodged as the execution goes on. In this paper, we study the use of Bayesian networks as models of the input history of a program. The main point is the creation of a probabilistic model that persists as the program is run on different inputs and at each new input refines its own parameters in order to reflect the program’s input history more accurately. As the model is thus tuned, it causes basic blocks to be reordered so that, upon arrival of the next input for execution, loading the basic blocks into memory automatically takes into account the input history of the program. We report on extensive experiments, whose results demonstrate the efficacy of the overall approach in progressively lowering the execution times of a program on identical inputs placed randomly in a sequence of varied inputs. We provide results on selected SPEC CINT2000 programs and also evaluate our approach as compared with the gcc level-3 optimization and with Pettis–Hansen reordering. Keywords: Instruction memory, Code-layout optimization, Bayesian networks 1.

The gap between memory and processor speeds is responsible for the substantial amount of idle time of current processors. To reduce the impact provoked by the so-called "memory gap problem," many, software techniques (e.g., the code layout reorganization) together with hardware mechanisms (cache memory, translation look-aside buffer branch prediction, speculative execution, trace cache, instruction reuse, and so on) have been successfully implemented. In this paper we present some experiments that explain why these mechanisms and techniques are so efficient. We found that only a small fraction of the object code is actually executed: our experiments disclosed that more than 50% of the instructions remain untouched during the whole execution, and the percentages of basic blocks which remain unused are slightly greater. In addition to the usage of instructions and blocks, the paper provides further insights regarding the behavior of application programs, and gives some suggestions for extra performance gains.

The combinatorial neural model (CNM) is a type of fuzzy neural network for classification problems and, more generally, for the mapping between fuzzy multidimensional spaces. Learning in CNM is a complex task spanning the learning of input-neuron membership functions, of the network topology, and of connection weights. In this paper we are concerned with these various aspects of learning in CNM, most notably with the learning of connection weights, whose complexity comes from the existence of nondifferentiable, nonconvex error functions associated with the learning process. We introduce several algorithms for weight learning, most based on subgradient techniques borrowed from the field of nondifferentiable optimization. All algorithms are based on essentially "local" rules, and are therefore amenable to distributed/parallel implementations. Experimental results are provided on the large-scale problem of monitoring the deforestation of the Amazon region on satellite images. What these results indicate is that a hybrid CNM system outperforms previous results obtained with variations of error backpropagation techniques. In addition, this hybrid system has demonstrated robustness in the context under consideration, therefore constituting an attractive alternative.

The authors describe how a neural network can be built for the exact solution of linear programming problems by a feasible directions approach. The proposed network, when started at any interior point, continuously tracks a path of equally interior points converging to an optimal solution. The number of neurons in the network grows linearly with the problem size, and neurons are relatively sparsely connected. Initial simulation results indicate that convergence is very fast, and consequently the network can be of relevance to many application areas

Abstract Neural networks, or connectionist systems, have recently emerged as a powerful model of collective, parallel computation of great interest in artificial intelligence and combinatorial optimization. The understanding of neural networks is still largely dependent upon simulations, which in turn can be of great interest to the designer of parallel software, owing to the inherently distributed character of those systems. This paper is concerned with the simulation of one specific class of neural networks, namely those introduced by J. J. Hopfield. We discuss the design and occam implementation of a distributed parallel simulator of such networks, allowing for both binary‐ and continuous‐response neurons. A design is provided which we judge to be generic to a large extent, and then problems related to an occam implementation are discussed. One problem of particular relevance is the potential occurrence of communication deadlocks as a result of the unbuffered communication among occam processes.

The occurrence of communication deadlocks caused by the unavailability of message buffers during the execution of distributed parallel programs is investigated. Such deadlocks can occur even if the program is designed for deadlock-freedom, since they are largely dependent on the system's ability to handle message buffering space. A class of deadlock prevention strategies which require that the programmer provide upper bounds on the buffer usage in the several communication channels involved is exploited, and it is argued that such bounds are relatively simple to obtain in many cases. The proposed strategies range from those which require a minimal amount of buffers to those which ensure a reasonable level of concurrency in process execution, although at the expense of more buffering space. It is shown that in general these strategies require the solution of NP-hard optimization problems, and an efficient heuristic to tackle the concurrency-optimal strategy is suggested. Randomly generated systems are then used to show that the heuristic tends to be very successful.>

Let G be a connected undirected graph in which each node corresponds to a process and two nodes are connected by an edge if the corresponding processes share a resource. We consider distributed computations in which processes are constantly demanding all of their resources in order to operate, and in which neighboring processes may not operate concurrently. We advocate that such a system is general enough for representing a large class of resource-sharing systems under heavy load. We employ a distributed scheduling mechanism based on acyclic orientations of G and investigate the amount of concurrency that it provides. We show that this concurrency is given by a number akin to G 's chromatic and multichromatic numbers, and that, among scheduling schemes which require neighbors in G to alternate in their turns to operate, ours is the one that potentially provides the greatest concurrency. However, we also show that the decision problem corresponding to optimizing concurrency is NP -complete.