Journal d'informatique et de biologie des systèmes

In computational neuroscience, receptors, channels and more generally signaling pathways are often modeled with Markov state models to represent biochemical reactions, which are then implemented with bilinear equations. One of the goals of these models, once calibrated with experimental results is to predict the dynamics of the biological system they represent in response to molecular perturbations and therefore facilitate and enhance the success rate of drug discovery and development. To model receptors under both pathological and physiological conditions, modelers usually modify the ligand association and dissociation parameters in the kinetic model during the optimization phase of model development. However, some parameter values may lead to unstable models, making calibration very difficult, time-consuming and inefficient before performing predictive in silico studies. In order to guarantee model stability during the parameter optimization phase, we propose to linearize bilinear kinetic models around an operating point. Considering the model input as piecewise constant, we propose an algorithm based on the Routh-Hurwitz criterion to generate stability constraints on model parameters. As an example, we apply this algorithm to the gamma-aminobutyric acid (GABA) receptor subtype A (GABAA receptor) model, as developed by Pugh and Raman (2005). The results obtained with the Routh-Hurwitz criterion provide constraint equations. These equations, once integrated into the parameter optimization process, guarantee the stability of the model and thus the success of the optimization process. An additional benefit is that the constraint equations allow determining the boundaries of the stability domain of the model. In the example provided, the Routh-Hurwitz criterion indicates that the model with the chosen parameters becomes unstable if GABA concentration rises above 6.54 mM. The proposed algorithm has also the advantage of being fast and easy to implement.

A collision avoidance system for a manipulator arm such as a robot arm includes a plurality of infrared sources and sensors distributed about the periphery of a mounting block coupled with the assembly. Baffles prevent the sensors from receiving infrared light directly from the sources. The sensors are operable to produce sensor signals, received from an object in the field of view of the sensors, representative of source-derived reflected infrared light impinging thereon. A controller coupled with the sensors determines whether the sensor signals exceed a predetermined level indicative of an object in the field of view of the corresponding sensor and prevents movement toward the object in order to avoid a collision. It has been noted that today’s pharmacist is required to spend more time counseling customers as opposed to filling prescriptions. To offset this increase in consultation time, a technician is usually hired to reduce some of the pharmacist’s tasks. This is likely to result in higher prescription drug prices for the consumer. There is, however, another option – automation! Automation equipment can reduce the pharmacist’s work load, consequently freeing up time for counseling.

The Com-Com is the user-centric environment which provides researchers with tailored frameworks to support their computational needs. It addresses existing and new user communities in both research and commercial fields. Technically the Com-Com provides dynamic infrastructure, dynamic service provision and user-driven application development across the domains. End users can create new applications for solving their computational tasks easily by combining ready-made interdisciplinary services available in the networked Repository and incorporate their own functionalities. Since services may be offered by different enterprises and communicate over the network, they provide an advanced distributed computing infrastructure for both intra- and cross-enterprise application integration and collaboration. The approach in hands potentially opens a door to rapid creating applied software for Exaflops HPC and Exabytes data. Nowadays the Com-Com can provide applications developing in the life science, environment, engineering, physics, computational chemistry, medicine, data mining research by collecting already existing web-services been developed by different research communities EGI, Flatworld, FI-WARE, SAP, ESRC. The goal of the Com-Com is to present an open environment of applied computing services and to encourage researchers across Europe to participate in its extending, interchanging or improving. The Com-Com stack presents flexibility enabling users to form dynamic teams, dynamic collections of cross domain services and dynamic infrastructure to run the services on. The Com-Com may enhance the capabilities of research organizations who lack resource both in human and technical terms by better integrating researches across international scientific communities with the final aim to strengthen the EU research base.

ZigBee’s feature is low power utilization, approved specifications make it very suitable to be used with application sensor devices. Also features are short distance communication and low cost. ZigBee technology makes that difficult wire repeat can be shunned in the area of home, manufacturing plant, infirmary, etc. This paper compares the cluster tree topology with the star topology of ZigBee in terms of the amount of received data, throughput and delay, it shows that cluster tree topology is more salutary than star topology.

We propose a new scheme for speaker-dependent silent speech recognition systems (SSRSs) using both single-trial electroencephalograms (EEGs) scalp-recorded and speech signals measured during overtly and covertly speaking “janken” and “season” in Japanese. This scheme consists of two phases. The learning phase specifies a Kalman filter using spectrograms of the speech signals and independent components (ICs), whose equivalent current dipole source localization (ECDL) solutions were located mainly at the Broca’s area, of the EEGs during the actual speech. In case of the “season” task, the speech signals were transformed into vowel and consonant sequences, and these relationships were learned by hidden Markov model (HMM) with Gaussian mixture densities. The decoding phase predicts spectrograms for the silent “janken” and “season” using the Kalman filter with the EEGs during the silent speech. For the silent “season”, the predicted spectrograms were inputted to the HMM, and which “season” was silently spoken was determined by the maximal log-likelihood among each HMM. Our preliminary results as training steps are as follows: the silent “jankens” were correctly discriminated; the silent “season”-HMMs worked well, suggesting that this scheme might be applied to the discrimination between all the pairs of the hiraganas

Staphylococcus aureus (S. aureus) is a Gram-positive bacterium that occurs in microscopic bunches similar to grapes. In general, S. aureus shows two different colonies; on agar dishes S. aureus forms huge golden yellow colonies, while on blood agar dishes it has haemolytic properties. Recently, different major studies have discovered CO2-dependent S. aureus strains that can be found as an inhabitant of small colony variants (SCV) S. aureus, which can cause recurrent and persistent infections. Although it has been investigated the mechanism of this CO2 dependence, this paper aims to use different bioinformatics software tools, such as MUMmer, BLAST, Jalview and ClustalW, in order to compare and search for the differences in the enzymes involved in pathways that can be altered in the CO2-dependent S. aureus strain. That is, the entire genome of CO2-dependent S. aureus strain was compared against another strain, not CO2-dependent using the software tool MUMmer. Based on this tool, the results show that the plots of the CO2-dependent strain against the COL and Mu50 strains did not show any gaps, while the connected thick diagonal line reveals strong similarities between the sequences in comparison to the thin diagonal line. Surprisingly, the results of using BLAST alignment tool did not illustrate any obvious mutations. Besides, the comparison between a number of strains and the CO2-dependent S. aureus strain was carried out using the ClustalW alignment tool and Jalview software; consequently it did not show any observable mutations, as well.

In this paper, we compare and contrast five models of dengue fever, a serious illness that affects tropical and subtropical areas around the world. We evaluate each model using different scenarios and identify the strengths and weakness of each of the models. The goal of our analysis is to indicate the strengths and weaknesses of current mathematical models of dengue fever that should assist future researchers in forming models that accurately measure the variables they are studying that affect the spread and progression of the disease.

To find the minimum Hamiltonian cycle is the objective of traveling salesman problem (TSP) whereas it has been proven to be NP-complete. To select the right edges in the minimum Hamiltonian cycle, the frequency graph is computed with a set of optimal i-vertex paths. The number of optimal i-vertex paths is formulated according to i. In these frequency graphs, the frequencies of the edges in the minimum Hamiltonian cycle are generally bigger than the average frequency and those of most of the other edges. Before i ≤ n/2+1 for even number n and i ≤ (n ± 1)/2+1 for odd number n, the frequencies of the edges in the minimum Hamiltonian cycle increase faster than the average frequency does according to i. The frequencies on the edges and their change can be taken as the heuristic information to select the edges in the minimum Hamiltonian cycle. Two simple examples are given to show the change of the frequencies of the edges in the minimum Hamiltonian cycle. The results show that they are different from those of the other edges according to i. The results can be used as the basis to design algorithms for TSP

Neuroscience is a challenging field of science; it is a hybrid sphere of technology, medicine and psychology that will necessarily lay the foundations of the next big technological revolution. Neuroscience will tear down the borders between mind and computer, enabling a two-way interaction that will provide humans with the power of computers, and computers with the creativity and decision-making of human minds. This paper proposes two new techniques for electromagnetic measurement and induction, Multiple MEG and Multiple TMS, which may lead to a new way of interaction between brain and chips. The paper also proposes a theoretical overview in “Computer Neuroscience”, focusing on non-invasive synapse measurement and induction, and higher-level signals

Analyzing student web browsing behavior is a challenging task. This paper mainly focuses on a methodology to identify the influencing factor that has driven a student towards navigating a particular web site. Most of the research in this direction is untouched to estimate the influence of faculty on the student’s behavioral patterns. In this work we focus on a novel statistical approach based on adaptive Gaussian mixture model, where the data clustered is given as input to the model to classify the student navigating pattern. The concept of regression analysis is used to find the relationship between student’s navigational behavior and faculty’s experience and rating. This article considers a real time dataset of GITAM University for experimentation.