Having chosen biologically relevant motif MHG p-value thresholds, we next compared the KMM motifs found by our motif finder for the TF-condition sets with the motifs published by MacIsaac et al. . For each TF-condition set, we allowed the motif finder to output up to five unique motifs. Only those that passed their sets’ bin threshold were considered. We compared our KMM motifs to the MacIsaac et al. PSSMs by learning a PSSM representation of each KMM, and comparing this PSSM with the respective MacIsaac et al. PSSM, relying on a method previously used by Narlikar et al. . (For a complete description of the motif comparison method, see Protocol S1.) A summary of this comparison is shown in Figure 5B. For 66% of the sets we found motifs similar to those found by MacIsaac et al. These results, although they can only be approximately compared with recently published results by Narlikar et al.  and Eden et al. , show that our motif finder does not fall behind state of the art motif finders, and is at least comparable to other methods. In Protocol S2, we further compare our motif finder to other motif finders, demonstrating that our motif finder has advantages over other Figure 4. Evaluating our approach on real TFBSs from human. (A) Train (green points) and test log-likelihood (blue bars), shown as the mean and standard deviation improvements in the average log-likelihood per instance compared to a PSSM for the datasets of NRSF, CTCF predicted sites, and CTCF predicted conserved sites. (B) and (C) show the PSSM and FMM features expectations logo for CTCF predicted conserved sites respectively. (D) and (E) show the same for NRSF sites. Each feature in the FMM feature expectation logo ((B) and (E)) is represented by a box. The horizontal position and the letters in the box define the feature. For example, the feature in the purple dashed box in (C) represent the feature ‘‘T at position 2 and A at position 7.’’ The height of the feature is linear with respect to its expectation in the probability distribution defined by the model. Gray background marks a double position feature.
The analysis of the consistency in the density of states shows that we achieved convergence at minimal distances lower than 4 Å, but the quality of the sampling decreases at high minimal distances (S6 Fig). This is expected, since the volume of the conformational space increases sig- nificantly with the protein-DNA separation distance and is therefore a common problem in this type of calculations . A confinement scheme in which conformational, positional and rotational restraints are used and removed after the induced dissociation has successfully been used to alleviate such issues in protein-ligand systems [30,31,32]. However, the very high de- generacy of RMSD-based conformational restraints makes this approach unlikely to converge in our case, for which both partners are very large and flexible. The imperfect convergence at higher separation as well as the presence of the second domain bound to the DNA may explain the inaccuracy in the estimation of binding free energies. However, our estimates for the OCT4-SOX2 cooperativity as well for the difference in the affinities of the POU S and POU HD
Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II), flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 A˚ interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ,75% of the successfully predicted cases. This unified conformational selection and induced fit approachtoprotein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking.
Structural information related toprotein–peptide complexes can be very useful for novel drug discovery and design. The computational docking of protein and peptide can supplement the structural information available on protein–peptide interactions explored by experimental ways. Protein–peptide docking of this paper can be described as three processes that occur in parallel: ab-initio peptide folding, peptide docking with its receptor, and refinement of some flexible areas of the receptor as the peptide is approaching. Several existing methods have been used to sample the degrees of freedom in the three processes, which are usually triggered in an organized sequential scheme. In this paper, we proposed a parallel approach that combines all the three processes during the docking of a folding peptide with a flexible receptor. This approach mimics the actual protein–peptide docking process in parallel way, and is expected to deliver better performance than sequential approaches. We used 22 unbound protein–peptide docking examples to evaluate our method. Our analysis of the results showed that the explicit refinement of the flexible areas of the receptor facilitated more accurate modeling of the interfaces of the complexes, while combining all of the moves in parallel helped the constructing of energy funnels for predictions.
To identify novel human proteins that facilitate the early steps of HCV infection, we devised a novel approach that exploited information on HCV–human proteininteractions, co-expression with human genes related to HCV infection, and association with the protein interaction network of the tight junction-tetraspanin web. In this approach, three features were evaluated and candidate genes were prioritized by multiple lines of features. We demonstrated that incorporating these three features signifi- cantly enhanced the prediction performance compared with any single feature, and we observed robust prediction performance irrespective of the coverage of protein interaction network (Figure S7). Moreover, we could assign ranks to the predicted proteins based on the number of protein–proteininteractions within a feature and on the number of query genes co-expressed with the gene. This approach gave high ranks to some of proteins known to participate in HCV infection. However, this approach gave low ranks to some genes known to participate in the early steps of HCV infection such as CLTC and LDLR (ranking 827 and 1659, respectively) due to the lack of interaction with tight junction or tetraspanin web proteins. This indicates that our integrative approach may not cover all of the genes involved in HCV infection. Nevertheless, we consider our method is useful to find the novel human genes involved in HCV infection. We also anticipate that a similar approach could be applied to identify proteins associated with the key pathways of other viral infections. We evaluated the reliability of the prediction by empirically analyzing the effects of knocking down the predicted proteins on HCV infection. Four proteins–TJP1, 14-3-3 b, GLUT4 and CD63 were shown to be involved in the early steps of HCV infection (Figure 4). Among these proteins, we further analyzed the role of
It is possible to note that most of the rolling slopes shrink towards zero. Rolling slopes are relatively stable, changing mainly in magnitude but not in sign. Therefore, they lie mostly in the same side of the horizontal axis over the period, while appearing less volatile at the end. Turnover and last 12 month returns seem to be the major exceptions. The former changes sign several times, but it seems more stable and closer to zero at the end of the sample. The latter presents a significant drop in 2008 turning into a negative value in 2009. This can be associated to the 2008 Financial Crises which represented a colossal negative impact on most momentum strategies. It is interesting to note that the rolling slopes do not seem to be significantly affected by relevant financial events such as the Black Monday on 19 th October of 1987 and the energy
Considerable efforts have been made to build realistic simulation models of high quality, but most of these are not fully explored. Ideally, each model should be analyzed systematically to understand system behavior and to assess the impact of model assumptions and parameters on the results. The availability of simple surrogate models based on complex simulation models not only serves to understand the original complex model better, but also to emulate it. Policy makers can easily use an interactive interface, such as the ones we present in this paper, to mimic the context in which their decisions take place (e.g., transfer model outcomes between broadly comparable countries) and predict the effectiveness or cost-effectiveness of health interventions. In that sense, the use of surrogate models as emulators provides a great opportunity to enhance both the understanding of these models and improve the reliability and speed of policy making based on existing elaborate model structures. Specifically, for FluTE we could instantly formulate some insights from the emulator (Figure 6) in clear language for policy makers. First, we predict that without reactive measures 36% of the population will be infected. Second, only a few imported cases are enough to start the epidemic hence (complete) isolation may delay (prevent) the epidemic. Third, 30% vaccination coverage (percentage of the population vaccinated) may result in a 55% reduction in the number of cases and 60% coverage in a 95% reduction due to
Ncr: NCU00925: similar to ribosome biogenesis protein Pescadillo. Afm: AFUA_4G08190: ribosome biogenesis protein Pescadillo. Afv: AFLA_111610: ribosome biogenesis protein Pescadillo, putative. Act: ACLA_047550: ribosome biogenesis protein Pescadillo, putative. Nfi: NFIA_107990: ribosome biogenesis protein Pescadillo, putative. TARGETING PROTEIN FOR CARDIOVASCULAR DISEASE
Care should be taken when making conclusions based on the posterior number of partitions K, as this parameter can be sensitive to model departures . The change point model we have used assumes that an animal’s response to a covariate is constant on each of K partitions of the observation period. This model can approximate much more complicated time-varying behavior, as shown in our simulation study (Text S2), but if the true response to a covariate is not piece-wise constant, then the location data will likely drive K to be a large number to best approximate the changing behavior over time. For example, the distance to rookery covariate seems to vary smoothly over time for animal 1 in our application, while our model assumes a finite number of break points in which the animal changes behavior. If the covariate does vary smoothly, the model would tend to favor a large number of partitions in an effort to best capture the animal’s true response to the driver of movement. Stephens  also notes that inference on K can be influenced by choice of prior distribution and birth distribution. We have found this to be true, though inference on b i,t , the focus of our study, is quite resilient to
Finding new drug targets for pathogenic infections would be of great utility for humanity, as there is a large need to develop new drugs to fight infections due to the developing resistance and side effects of current treatments. Current drug targets for pathogen infections involve only a single protein. However, proteins rarely act in isolation, and the majority of biological processes occur via interactions with other proteins, so protein-proteininteractions (PPIs) offer a realm of unexplored potential drug targets and are thought to be the next-generation of drug targets. Parasitic worms were chosen for this study because they have deleterious effects on human health, livestock, and plants, costing society billions of dollars annually and many sequenced genomes are available. In this study, we present a computational approach that utilizes whole genomes of 6 parasitic and 1 free-living worm species and 2 hosts. The species were placed in orthologous groups, then binned in species-specific ortholgous groups. Proteins that are essential and conserved among species that span a phyla are of greatest value, as they provide foundations for developing broad-control strategies. Two PPI databases were used to find PPIs within the species specific bins. PPIs with unique helminth proteins and helminth proteins with unique features relative to the host, such as indels, were prioritized as drug targets. The PPIs were scored based on RNAi phenotype and homology to the PDB (Protein DataBank). EST data for the various life stages, GO annotation, and druggability were also taken into consideration. Several PPIs emerged from this study as potential drug targets. A few interactions were supported by co-localization of expression in M. incognita (plant parasite) and B. malayi (H. sapiens parasite), which have extremely different modes of parasitism. As more genomes of pathogens are sequenced and PPI databases expanded, this methodology will become increasingly applicable.
S eizure detection using brain signal (EEG) analysis is the important clinical methods in drug therapy and the decisions before brain surgery. In this paper, after signal conditioning using suitable filtering, the Gamma frequency band has been extracted and the other brain rhythms, ambient noises and the other bio-signal are canceled. Then, the wavelet transform of brain signal and the map of wavelet transform in multi levels are computed. By dividing the color map to different epochs, the histogram of each sub-image is obtained and the statistics of it based on statistical momentums and Negentropy values are calculated. Statistical feature vector using Principle Component Analysis (PCA) is reduced to one dimension. By EMD algorithm and sifting procedure for analyzing the data by Intrinsic Mode Function (IMF) and computing the residues of brain signal using spectrum of Hilbert transform and Hilbert – Huang spectrum forming, one spatial featurebased on the Euclidian distance for signal classification is obtained. By K-Nearest Neighbor (KNN) classifier and by considering the optimal neighbor parameter, EEG signals are classified in two classes, seizure and non-seizure signal, with the rate of accuracy 76.54% and with variance of error 0.3685 in the different tests.
) (note that these considerations operate under the assumption that the camera is static in the environment). The original background subtraction model upon which the proposed method builds on  uses a vector of codebooks to build a model of the scene’s background, which is iteratively updated. To avoid mistakenly learning foreground objects, the presence of motion can be used to cancel the update process, which makes the solution not ideal for dynamic environments. To overcome this limitation, a 3 × 3 regular grid superposed on the input image is applied here, with each cell being associated with an independently updated background model. This means that a moving object (e.g., a waving tree) in a given region of the scene no longer cancels the background update in other portions of the scene, which suffices for uncrowded scenes. In addition, the update process in each cell only occurs if there is no object already being tracked therein and no considerable motion is observed over a few seconds. Motion information comes from the method proposed by Collins et al. , M ( n ) , and helps to cope with moving objects that are not yet being tracked. To reduce computational load, cells are only updated on a second-wise basis or sooner if a large variation in brightness is observed. The output of the background subtraction process is a binary image representing the foreground pixels, F ( n ) .
tion process, while utilizing the existing tools and mech- anisms as much as possible. We propose to create agents using a drag-and-drop mechanism where the user can se- lect components to plug in the agent depending on the ap- plication requirement. Rather than a practical reasoning agent architecture such as BDI, we adopt a utility-driven agent architecture with quantitative reasoning capabili- ties. Our high-level design is based on roles, however, the mapping from role instances to agents in our work is different from other role assignment mechanisms  . Besides the logical reasoning on the matching of motiva- tions and the conflicts among different roles, we adapt a quantitative model of motivation named MQ framework . Based on this MQ framework, the agent can per- form a quantitative reasoning on how important a role in- stance is given its preference, its utility function and its current achievement. In the definition of a role, we intro- duce a RTÆMS language (Role-Based Task Analyzing, environment Modeling, and Simulation) to represent the domain knowledge about how to achieve a goal. RTÆMS language is an extension of TÆMS language  - a hi- erarchical task network representation language with task inter-relationships and quantitative descriptions of differ- ent alternatives to achieve a goal. When an agent takes a role instance, it has access to this RTÆMS representation of the goal. As a result, the existing planning/scheduling  and coordination  mechanisms based on TÆMS language can easily be exploited by the agent.
Knowledge Management (KM) is considered by many organizations a key aspect in sustaining competi- tive advantage. Designing appropriate KM processes and enabling technology face considerable risks, as they must be shaped to respond to specific needs of the organiza- tional environment. Thus, many systems are abandoned or fall into disuse because of inadequate understanding of the organizational context. This motivates current re- search, which tends to propose agent organizations as a useful paradigm for KM systems engineering. Following these approaches, organizations are analyzed as collec- tive systems, composed of several agents, each of them autonomously producing and managing their own local data according to their own logic, needs, and interpreta- tive schema, i.e. their goals and beliefs. These agents in- teract and coordinate for goal achievement defining a coherent local knowledge system. This paper presents a novel methodology for analyzing the requirements of a KM system based on an iterative workflow where a piv- otal role is played by agent-oriented modeling. Within this approach, the needs for KM systems are traced back to the organization stakeholders’ goals. A case study is used to illustrate the methodology. The relationship of this work with current studies in agent organizations and organizational knowledge management is also discussed. Differently from other works, this methodology aims at offering a practical guideline to the analyst, pointing out the appropriate abstractions to be used in the different phases of the analysis.
Further studies are needed to better understand the role of the calpain system in bladder carcinogenesis. If our results are validated by other studies, Capn3 will definitely appear to play an important role in the molecular pathway of bovine urinary bladder tumors. As a consequence, it may prove useful as a diagnostic biomarker for monitoring urothelial tumor develop- ment and progression and as a potential target for cancer therapy. Cattle suffering from urothelial tumors, whose incidence may be ,90% in adult animals [20,46], may serve as an animal model useful for gaining insight into new molecular pathways involved in naturally occurring bladder carcinogenesis and for evaluating in vivo potential new drugs against specific targets, or for proposing novel therapeutic strategies that are urgently needed nowadays . It is also worth to remember that the establishment of reliable and reproducible animal models for bladder cancer remains an ongoing challenge, since developing therapeutic agents requires in vivo models . Furthermore, the UICC Study Group suggested that biological models may still be trail blazing for the natural history of cancer, although molecular models have fostered an impressive progress over the last decades . Finally, it is worthwhile noting that cattle has already been investigated and, it has been found to be a good animal model for several other human diseases [49,50].
In this paper a new least-squares (LS) approach is used to model the discrete-time fractional differintegrator. This approach is based on a mismatch error between the required response and the one obtained by the difference equation deﬁning the auto-regressive, moving-average (ARMA) model. In minimizing the error power we obtain a set of suitable normal equations that allow us to obtain the ARMA parameters. This new LS is then applied to the same examples as in [R.S. Barbosa, J.A. Tenreiro Machado, I.M. Ferreira, Least-squares design of digital fractional-order operators, FDA’2004 First IFAC Workshop on Fractional Differentiation and Its Applications, Bordeaux, France, July 19–21, 2004, P. Ostalczyk, Fundamental properties of the fractional-order discrete-time integrator, Signal Processing 83 (2003) 2367–2376] so performance comparisons can be drawn. Simulation results show that both magnitude frequency responses are essentially identical. Concerning the modeling stability, both algorithms present similar limitations, although for different ARMA model orders.
This study demonstrates that hypothetical scenarios can be used to evaluate the sensitivity of catchments to droughts. The response of streamflow as well as soil moisture and groundwater storages to a continuous progression of drying was analyzed both in general as well as focusing on drought characteristics and on one historical drought event. Our anal- ysis showed that mean catchment elevation, size and slope were the main controls on the sensitivity of the catchments to drought. The results suggest that higher elevation catchments with steeper slopes were less sensitive to droughts than lower elevation catchments with less steep slopes, which could not solely be attributed to an increased snow influence. The soil moisture storage was significantly correlated to catchment size, where we found smaller catchments to be less sensi- tive to droughts than larger catchments. We did not find a clear relationship between drought sensitivity and hydroge- ology; however, another choice of the productivity classes would lead to such a relationship. Generally, for water re- sources management it is important to look at both stream- flow sensitivity and storage sensitivity to droughts. With our model-basedapproach the sensitivity of both can be easily estimated. This approach can serve as a starting point for water resources managers to understand the vulnerability of their catchments.
There are cases where the automatic treatment by tool change or parameter change cannot be successful, so the sensors still indicate a type of abnormal situation. The system then considers intervention of a human operator if, after a second tool-change or k-th parameter-changes, the fault cannot be corrected. Those conditions are verified applying a delay. The net in Figure 9 is safe, non-cyclic, and non-conflict according to BPN characteristics (Portinale, 1997). In this model, the transition firing is based on two criteria: transitions T n fire
Despite the presence of a large amount of factual material on thermodynamic parameters of com- plexation of agents in different solvents, including mixed ones, obtained knowledge is specific in nature. In order to identify more general patterns, studies are relevant that would allow to interpret the obtained data taking into account the interaction between chemical forms in solutions.