Cybernetic Modeling Revisited: A Method for Inferring the Cybernetic Variables u(i) from Experimental Data

The cybernetic modeling framework for the growth of microorganisms provides for an elegant methodology to account for the unknown regulatory phenomena through the use of cybernetic variables for enzyme induction and activity. In this paper, we revisit the assumption of limited resources for enzyme induction (Sigma u(i) = 1) used in the cybernetic modeling framework by presenting a methodology for inferring the individual cybernetic variables u(i) from experimental data. We use this methodology to infer u(i) during the simultaneous consumption of glycerol and lactose by Escherichia coli and then model the fitness trade-offs involved in the recently discovered predictive regulation strategy of microorganisms.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

Latency, Power and Performance Trade-offs in Network-on-Chips by Link Microarchitecture Exploration

This paper presents a power, latency and throughput trade-off study on NoCs by varying microarchitectural (e.g. pipelining) and circuit level (e.g. frequency and voltage) parameters. We change pipelining depth, operating frequency and supply voltage for 3 example NoCs - 16 node 2D Torus, Tree network and Reduced 2D Torus. We use an in-house NoC exploration framework capable of topology generation and comparison using parameterized models of Routers and links developed in SystemC. The framework utilizes interconnect power and delay models from a low-level modelling tool called Intacte[1]1. We find that increased pipelining can actually reduce latency. We also find that there exists an optimal degree of pipelining which is the most energy efficient in terms of minimizing energy-delay product.

Dry sliding wear behaviour of magnesium alloy based hybrid composites in the longitudinal direction

In the present investigation, the wear behaviour of a creep-resistant AE42 magnesium alloy and its composites reinforced with Saffil short fibres and SiC particles in various combinations is examined in the longitudinal direction i.e., the plane containing random fibre orientation is perpendicular to the steel counter-face. Wear tests are conducted on a pin-on-disc set-up under dry sliding condition having a constant sliding velocity of 0.837 m/s for a constant sliding distance of 2.5 km in the load range of 10-40 N. It is observed that the wear rate increases with increase in load for the alloy and the composites, as expected. Wear rate of the composites is lower than the alloy and the hybrid composites exhibit a lower wear rate than the Saffil short fibres reinforced composite at all the loads. Therefore, the partial replacement of Saffil short fibres by an equal volume fraction of SiC particles not only reduces the cost but also improves the wear resistance of the composite. Microstructural investigation of the surface and subsurface of the worn pin and wear debris is carried out to explain the observed results and to understand the wear mechanisms. It is concluded that the presence of SiC particles in the hybrid composites improves the wear resistance because these particles remain intact and retain their load bearing capacity even at the highest load employed, they promote the formation of iron-rich transfer layer and they also delay the fracture of Saffil short fibres to higher loads. Under the experimental conditions used in the present investigation, the dominant wear mechanism is found to be abrasion for the AE42 alloy and its composites. It is accompanied by severe plastic deformation of surface layers in case of alloy and by the fracture of Saffil short fibres as well as the formation of iron-rich transfer layer in case of composites.

Impression creep behaviour of magnesium alloy-based hybrid composites in the transverse direction

The creep behaviour of a creep-resistant AE42 magnesium alloy reinforced with Saffil short fibres and SiC particulates in various combinations has been investigated in the transverse direction, i.e., the plane containing random fibre orientation was perpendicular to the loading direction, in the temperature range of 175-300 degrees C at the stress levels ranging from 60 to 140 MPa using impression creep test technique. Normal creep behaviour, i.e., strain rate decreasing with strain and then reaching a steady state, is observed at 175 degrees C at all the stresses employed, and up to 80 MPa stress at 240 degrees C. A reverse creep behaviour, i.e., strain rate increasing with strain, then reaching a steady state and then decreasing, is observed above 80 MPa stress at 240 degrees C and at all the stress levels at 300 degrees C. This pattern remains the same for all the composites employed. The reverse creep behaviour is found to be associated with fibre breakage. The apparent stress exponent is found to be very high for all the composites. However, after taking the threshold stress into account, the true stress exponent is found to range between 4 and 7, which suggests viscous glide and dislocation climb being the dominant creep mechanisms. The apparent activation energy Q(C) was not calculated due to insufficient data at any stress level either for normal or reverse creep behaviour. The creep resistance of the hybrid composites is found to be comparable to that of the composite reinforced with 20% Saffil short fibres alone at all the temperatures and stress levels investigated. The creep rate of the composites in the transverse direction is found to be higher than the creep rate in the longitudinal direction reported in a previous paper.

On trade-off solution pairs in a special type of transportation problem

Abstract is not available.

Inferring neuronal network connectivity from spike data: A temporal data mining approach

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent d evelopments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.

Inferring molecular function: contributions from functional linkages

In the current era of high-throughput sequencing and structure determination, functional annotation has become a bottleneck in biomedical science. Here, we show that automated inference of molecular function using functional linkages among genes increases the accuracy of functional assignments by >= 8% and enriches functional descriptions in >= 34% of top assignments. Furthermore, biochemical literature supports >80% of automated inferences for previously unannotated proteins. These results emphasize the benefit of incorporating functional linkages in protein annotation.

Exploring energy-performance trade-offs for heterogeneous interconnect clustered VLIW processors

Clustered architecture processors are preferred for embedded systems because centralized register file architectures scale poorly in terms of clock rate, chip area, and power consumption. Although clustering helps by improving clock speed, reducing energy consumption of the logic, and making the design simpler, it introduces extra overheads by way of inter-cluster communication. This communication happens over long global wires which leads to delay in execution and significantly high energy consumption.In this paper, we propose a new instruction scheduling algorithm that exploits scheduling slacks of instructions and communication slacks of data values together to achieve better energy-performance trade-offs for clustered architectures with heterogeneous interconnect. Our instruction scheduling algorithm achieves 35% and 40% reduction in communication energy, whereas the overall energy-delay product improves by 4.5% and 6.5% respectively for 2 cluster and 4 cluster machines with marginal increase (1.6% and 1.1%) in execution time. Our test bed uses the Trimaran compiler infrastructure.

A suprathreshold stochastic resonance based pre-processor for direction-of-arrival estimation

In this paper we propose a nonlinear preprocessor for enhancing the performance of processors used for direction-of-arrival (DOA) estimation in heavy-tailed non-Gaussian noise. The preprocessor based on the phenomenon of suprathreshold stochastic resonance (SSR), provides SNR gain. The preprocessed data is used for DOA estimation by the MUSIC algorithm. Simulation results are presented to show that the SSR preprocessor provides a significant improvement in the performance of MUSIC in heavy-tailed noise at low SNR.

An unusual C-H center dot center dot center dot O hydrogen bond mediated reversal of polypeptide chain direction in a synthetic peptide helix

An unusual C-terminal conformation has been detected in a synthetic decapeptide designed to analyze the stereochemistry of helix termination in polypeptides. The crystal structure of the decapeptide Boc-Leu-Aib-Val-Ala-Leu-Aib-Val-(D)Ala-(D)Leu-Aib-OMe reveals a helical segment spanning residues 1-7 and helix termination by formation of a Schellman motif, generated by (D)Ala(8) adopting the left-handed helical (alpha(L)) conformation. The extended conformation at (D)Leu(9) results in a compact folded structure, stabilized by a potentially strong C-H ... O hydrogen bond between Ala(4) (CH)-H-alpha and (D)Leu(9)CO. The parameters for C-H ... O interaction are Ala(4) (CH)-H-alpha .. O=C (D)Leu(9) distance 3.27 Angstrom C-alpha-H .. O angle 176 degrees, and O .. H-alpha distance 2.29 Angstrom. This structure suggests that insertion of contiguous D-residues may provide a handle for the generation of designed structures containing more than one helical segment folded in a compact manner. (C) 2000 Academic Press.

Direction dependence in supernova data: constraining isotropy

We revise and extend the extreme value statistic, introduced in Gupta et al., to study direction dependence in the high-redshift supernova data, arising either from departures, from the cosmological principle or due to direction-dependent statistical systematics in the data. We introduce a likelihood function that analytically marginalizes over the,Hubble constant and use it to extend our previous statistic. We also introduce a new statistic that is sensitive to direction dependence arising from living off-centre inside a large void as well as from previously mentioned reasons for anisotropy. We show that for large data sets, this statistic has a limiting form that can be computed analytically. We apply our statistics to the gold data sets from Riess et al., as in our previous work. Our revision and extension of the previous statistic show that the effect of marginalizing over the Hubble constant instead of using its best-fitting value on our results is only marginal. However, correction of errors in our previous work reduces the level of non-Gaussianity in the 2004 gold data that were found in our earlier work. The revised results for the 2007 gold data show that the data are consistent with isotropy and Gaussianity. Our second statistic confirms these results.

The Relay Selection and Transmission Trade-off in Cooperative Communication Systems

A common and practical paradigm in cooperative communication systems is the use of a dynamically selected `best' relay to decode and forward information from a source to a destination. Such systems use two phases - a relay selection phase, in which the system uses transmission time and energy to select the best relay, and a data transmission phase, in which it uses the spatial diversity benefits of selection to transmit data. In this paper, we derive closed-form expressions for the overall throughput and energy consumption, and study the time and energy trade-off between the selection and data transmission phases. To this end, we analyze a baseline non-adaptive system and several adaptive systems that adapt the selection phase, relay transmission power, or transmission time. Our results show that while selection yields significant benefits, the selection phase's time and energy overhead can be significant. In fact, at the optimal point, the selection can be far from perfect, and depends on the number of relays and the mode of adaptation. The results also provide guidelines about the optimal system operating point for different modes of adaptation. The analysis also sheds new insights on the fast splitting-based algorithm considered in this paper for relay selection.

Comment on “Southward magnetic field in the neutral sheet produced by wavy motions Propagating in the dawn‐dusk direction”

In a recent paper Nakagawa and Nishida [1989] have suggested that wavy motions of the neutral sheet can be generated by the Kelvin‐Helmholtz instability if the dawn‐dusk flow of only several tens of km/s is present. However, their mathematical analysis is based on the choice of particular magnetic field directions in the three regions consisting of north, south lobes and the neutral sheet. In an earlier paper Uberoi [1986] discussed the Kelvin‐Helmholtz instability of a similar structured plasma layer without any assumptions either on velocity field directions or on the magnetic field directions, thus pointing out the angle effect due to variation in magnetic field directions on the instability criterion. The relevance of these results to the problem of wavy motions of the neutral sheet are pointed out. In particular it is found that when the y‐component of the magnetic field in each lobe is taken into consideration the Kelvin‐Helmholtz instability can be exicted only when the dawn‐dusk flow is of several hundreds of km/s a order of ten higher than that arrived in the analysis by Nakagawa and Nishida [1989].

Processing and fiber content effects on the machinability of compression moulded random direction short GFRP composites

The random direction short Glass Fiber Reinforced Plastics (GFRP) have been prepared by two compression moulding processes, namely the Preform and Sheet Moulding Compound (SMC) processes. Cutting force analysis and surface characterization are conducted on the random direction short GFRPs with varying fiber contents (25 similar to 40%). Edge trimming experiments are preformed using carbide inserts with varing the depth of cut and cutting speed. Machining characteristics of the Preform and SMC processed random direction short GFRPs are evaluated in terms of cutting forces, surface quality, and tool wear. It is found that composite primary processing and fiber contents are major contributing factors influencing the cutting force magnitudes and surface textures. The SMC composites show better surface finish over the Preform composites due to less delamination and fiber pullouts. Moreover, matrix damage and fiber protrusions at the machined edge are reduced by increasing fiber content in the random direction short GFRP composites.