Greater Modularity Research Articles

Construction of consistent SysML models applied to the CPS

With the increasing complexity of cyber-physical systems (CPS), it is interesting to decompose a CPS into sub-systems. This provides greater modularity and flexibility so that each system can be developed independently, making it easier to maintain. Also, it can improve its fault tolerance. However, this decomposition of the system can lead to inconsistency. This paper proposes an approach for early verification of cyber-physical systems decomposition using SysML. We address the limitations of SysML as a semi-formal language by introducing syntax and static semantics for its structural diagrams. The aim is to verify structural consistency before defining behavioral aspects. For that, the proposed approach verifies a set of structural consistency rules within a refinement relation to ensure that sub-components offer at least the same services as the abstract block and require the same services. Furthermore, the sub-blocks must satisfy all the requirements that the abstract block is supposed to verify. We used the CyCab as a case study to demonstrate the effectiveness of this approach.

Rhizosphere microbial community construction during the latitudinal spread of the invader Chromolaena odorata

The colonization of alien plants in new habitats is typically facilitated by microorganisms present in the soil environment. However, the diversity and structure of the archaeal, bacterial, and fungal communities in the latitudinal spread of alien plants remain unclear. In this study, the rhizosphere and bulk soil of Chromolaena odorata were collected from five latitudes in Pu’ er city, Yunnan Province, followed by amplicon sequencing of the soil archaeal, bacterial, and fungal communities. Alpha and beta diversity results revealed that the richness indices and the structures of the archaeal, bacterial, and fungal communities significantly differed along the latitudinal gradient. Additionally, significant differences were observed in the bacterial Shannon index, as well as in the structures of the bacterial and fungal communities between the rhizosphere and bulk soils. Due to the small spatial scale, trends of latitudinal variation in the archaeal, bacterial, and fungal communities were not pronounced. Total potassium, total phosphorus, available nitrogen, available potassium and total nitrogen were the important driving factors affecting the soil microbial community structure. Compared with those in bulk soil, co-occurrence networks in rhizosphere microbial networks presented lower complexity but greater modularity and positive connections. Among the main functional fungi, arbuscular mycorrhizae and soil saprotrophs were more abundant in the bulk soil. The significant differences in the soil microbes between rhizosphere and bulk soils further underscore the impact of C. odorata invasion on soil environments. The significant differences in the soil microbiota along latitudinal gradients, along with specific driving factors, demonstrate distinct nutrient preferences among archaea, bacteria, and fungi and indicate complex microbial responses to soil nutrient elements following the invasion of C. odorata.

AGE EFFECTS ON WHITE MATTER TOPOLOGY IN OLDER ADULTS AT HIGH RISK OF ALZHEIMER’S DISEASE

Abstract White matter integrity shows age-related declines in later life. Studies have found altered topological changes of white matter networks in healthy aging populations. We investigated cross-sectional association between white matter network topology, age, education, and cognition in older adults at high-risk for Alzheimer’s disease (AD). A total of 153 cognitively healthy participants (age=67.78±5.03, 111 female, 54 APOE-4 carriers) who have at least one close family member diagnosed with AD from the Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer’s Disease (PREVENT-AD) cohort were included in the study. Whole brain diffusion images were obtained and submitted to preprocessing, fiber tracking and connectome creation in MRtrix. Test scores from the Trail Making task, Stroop task, and Rey Auditory Verbal Learning Test (RAVLT) were included as cognitive function measures. We found that older age was significantly associated with greater modularity (r2=0.48, p corrected< 0.001), lower density (r2=0.32, p corrected< 0.001), greater betweenness centrality (r2=0.39, p corrected=0.003), and greater clustering coefficient (r2=0.05, p corrected=0.042). Older age was also significantly associated with longer Trail Making time series B (r2=0.11, p corrected=0.012) and fewer recalled words (r2=0.18, p corrected=0.007). We further found that participants with higher educational attainment showed less age-related changes in modularity (t=2.42, p=0.017) and density (t=2.40, p=0.018). However, these graph theory measures were not associated with cognitive performance. In conclusion, age related to whole-brain white matter network topology in high-risk healthy older adults, and education may attenuate this relationship. The current study deepens our understanding of white matter changes in high-risk aging.

Wetland soil microbial responses to upland agricultural intensification and snail invasion

Wetland soils harbor diverse microorganisms including bacteria, archaea, and fungi, all of which play significant roles in maintaining soil health and ecosystem functions but are susceptible to influences from multiple environmental changes. Agricultural intensification and biological invasion are two fundamental drivers of environmental change for wetlands, but their interactive effects on soil microbial communities remain less well understood, particularly for seasonal subtropical wetlands. Yet such understanding is vital for wetland conservation and management. In this study, we conducted a 14-week field mesocosm experiment to investigate how agricultural intensification and invasive apple snail (Pomacea maculata) – an emerging and increasingly concerning threat to natural and agricultural systems in southeastern U.S. – altered wetland soil microbial communities and structures. We found that invasive P. maculata showed selective effects on relative abundance of specific microbial taxa (e.g., Proteobacteria, Nitrospirota), and interactive effects with upland intensification on Spirochaetota and Mortierellomycota. Upland agricultural intensification also exerted significant and consistent effects on microbial composition and diversity across microbial domains. Changes in microbial composition were partly manifested through modifications in water chemistry, such as dissolved oxygen, which acted as environmental regulators. In addition, upland intensification led to more complex but sparsely connected microbial networks, while invasive snail presence decreased network complexity and resulted in greater modularity, less edge density, and longer path length, indicative of lower molecular information exchange efficiency. Our research emphasizes the need for a comprehensive evaluation of microbial responses (i.e., composition, diversity, and co-occurrence patterns) to better understand multi-stressor impacts from human activities on wetland belowground microbial communities. By holistically characterizing microbial responses, our findings show how global change drivers may impact wetland microorganisms in subtropical biomes and infer their functional consequences. Our results have important implications for sustainable landscape management and conservation of wetlands that are experiencing escalating human-induced environmental changes.

Rich-club connectivity and structural connectome organization in youth at clinical high-risk for psychosis and individuals with early illness schizophrenia.

Brain dysconnectivity has been posited as a biological marker of schizophrenia. Emerging schizophrenia connectome research has focused on rich-club organization, a tendency for brain hubs to be highly-interconnected but disproportionately vulnerable to dysconnectivity. However, less is known about rich-club organization in individuals at clinical high-risk for psychosis (CHR-P) and how it compares with abnormalities early in schizophrenia (ESZ). Combining diffusion tensor imaging (DTI) and magnetic resonance imaging (MRI), we examined rich-club and global network organization in CHR-P (n=41) and ESZ (n=70) relative to healthy controls (HC; n=74) after accounting for normal aging. To characterize rich-club regions, we examined rich-club MRI morphometry (thickness, surface area). We also examined connectome metric associations with symptom severity, antipsychotic dosage, and in CHR-P specifically, transition to a full-blown psychotic disorder. ESZ had fewer connections among rich-club regions (ps<.024) relative to HC and CHR-P, with this reduction specific to the rich-club even after accounting for other connections in ESZ relative to HC (ps<.048). There was also cortical thinning of rich-club regions in ESZ (ps<.013). In contrast, there was no strong evidence of global network organization differences among the three groups. Although connectome abnormalities were not present in CHR-P overall, CHR-P converters to psychosis (n=9) had fewer connections among rich-club regions (ps<.037) and greater modularity (ps<.037) compared to CHR-P non-converters (n=19). Lastly, symptom severity and antipsychotic dosage were not significantly associated with connectome metrics (ps<.012). Findings suggest that rich-club and connectome organization abnormalities are present early in schizophrenia and in CHR-P individuals who subsequently transition to psychosis.

Eyes-Open and Eyes-Closed Resting State Network Connectivity Differences.

Resting state networks comprise several brain regions that exhibit complex patterns of interaction. Switching from eyes closed (EC) to eyes open (EO) during the resting state modifies these patterns of connectivity, but precisely how these change remains unclear. Here we use functional magnetic resonance imaging to scan healthy participants in two resting conditions (viz., EC and EO). Seven resting state networks were chosen for this study: salience network (SN), default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), visual network (VN), motor network (MN) and auditory network (AN). We performed functional connectivity (FC) analysis for each network, comparing the FC maps for both EC and EO. Our results show increased connectivity between most networks during EC relative to EO, thereby suggesting enhanced integration during EC and greater modularity or specialization during EO. Among these networks, SN is distinctive: during the transition from EO to EC it evinces increased connectivity with DMN and decreased connectivity with VN. This change might imply that SN functions in a manner analogous to a circuit switch, modulating resting state relations with DMN and VN, when transitioning between EO and EC.

Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach.

The perception of physical exertion is the cognitive sensation of work demands associated with voluntary muscular actions. Measurements of exerted force are crucial for avoiding the risk of overexertion and understanding human physical capability. For this purpose, various physiological measures have been used; however, the state-of-the-art in-force exertion evaluation lacks assessments of underlying neurophysiological signals. The current study applied a graph theoretical approach to investigate the topological changes in the functional brain network induced by predefined force exertion levels for twelve female participants during an isometric arm task and rated their perceived physical comfort levels. The functional connectivity under predefined force exertion levels was assessed using the coherence method for 84 anatomical brain regions of interest at the electroencephalogram (EEG) source level. Then, graph measures were calculated to quantify the network topology for two frequency bands. The results showed that high-level force exertions are associated with brain networks characterized by more significant clustering coefficients (6%), greater modularity (5%), higher global efficiency (9%), and less distance synchronization (25%) under alpha coherence. This study on the neurophysiological basis of physical exertions with various force levels suggests that brain regions communicate and cooperate higher when muscle force exertions increase to meet the demands of physically challenging tasks.

Improving commuting zones using the Louvain community detection algorithm

Well-defined commuting zones are essential for accurate research on US local labor markets. To develop commuting zones, one must construct edge weights – a measure of commuting flows between counties – and then use the edge weights to partition counties into clusters. I improve upon currently used “ERS” commuting zones in two ways. First, it is unclear if ERS commuting zones use the best edge weights. Therefore, I test multiple edge weights. Second, the algorithm to produce ERS commuting zones requires specifying a theoretically-unguided cutoff parameter; results may be sensitive to the parameter choice. Instead, I use the Louvain algorithm, which optimizes for “modularity”, a graph-intrinsic parameter that is greater when there is higher intra-commuting zone flow and lower inter-commuting zone flow. I call my new delineations “TS Louvain”, which uses the ERS commuting flow definition to construct edge weights, and “Sum Louvain”, which uses the total number of commuters as edge weights. Compared to ERS, TS Louvain and Sum Louvain have 0.05 to 0.15 greater modularity, Sum Louvain has a 0.01 to 0.02 higher share of people who work and live in the same commuting zone, and in a case study, TS Louvain produces greater estimates and t-statistics. These metrics suggest that these new commuting zones improve upon the existing delineations. Researchers can access these commuting zone definitions at bit.ly/LouvainCZ . • Two new delineations of commuting zones are produced using the Louvain algorithm. • New delineations Sum Louvain and TS Louvain improve upon existing delineations. • Sum Louvain has higher share of those who work and live in the same commuting zone. • In a case study, TS Louvain produces larger estimates and t-statistics.

FCS-MPC with Nonlinear Control Applied to a Multicell AFE Rectifier.

The use of controlled power converters has been extended for high power applications, stacking off-the-shelve semiconductors, and allowing the implementation of, among others, AC drives for medium voltages of 2.3 kV to 13.8 kV. For AC drives based on power cells assembled with three-phase diode rectifiers and cascaded H-bridge inverters, a sophisticated input multipulse transformer is required to reduce the grid voltage, provide isolation among the power cells, and compensate for low-frequency current harmonics generated by the diode-based rectifiers. However, this input multipulse transformer is bulky, heavy, and expensive and must be designed according to the number of power cells, not allowing total modularity of the AC drives based on cascade H-bridges. This study proposes and evaluates a control strategy based on a finite control set-model predictive control that emulates the harmonic cancellation performed by an input multipulse transformer in a cascade H-bridge topology. Hence, the proposed method requires conventional input transformers and replaces the three-phase diode rectifiers. As a result, greater modularity than the conventional multicell converter and improved AC overall input current with a THD as low as 2% with a unitary displacement power factor are achieved. In this case, each power cell manages its own DC voltage using a nonlinear control strategy, ensuring stable system operation for passive and regenerative loads. The experimental tests demonstrated the correct performance of the proposed scheme.

Characterizing the Network Architecture of Emotion Regulation Neurodevelopment.

The ability to regulate emotions is key to goal attainment and well-being. Although much has been discovered about neurodevelopment and the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks. Here we employed a network neuroscience framework to parse the neural underpinnings of emotion regulation skill acquisition, while accounting for age, in a sample of children and adolescents (N = 70, 34 female, aged 8-17 years). Focusing on three key network metrics-network differentiation, modularity, and community number differences between active regulation and a passive emotional baseline-we found that the control network, the default mode network, and limbic network were each related to emotion regulation ability while controlling for age. Greater network differentiation in the control and limbic networks was related to better emotion regulation ability. With regards to network community structure (modularity and community number), more communities and more crosstalk between modules (i.e., less modularity) in the control network were associated with better regulatory ability. By contrast, less crosstalk (i.e., greater modularity) between modules in the default mode network was associated with better regulatory ability. Together, these findings highlight whole-brain connectome features that support the acquisition of emotion regulation in youth.

From structure to function in mutualistic interaction networks: Topologically important frugivores have greater potential as seed dispersers.

Networks of mutualistic interactions between animals and plants are considered a pivotal part of ecological communities. However, mutualistic networks are rarely studied from the perspective of species-specific roles, and it remains to be established whether those animal species more relevant for network structure also contribute more to the ecological functions derived from interactions. Here, we relate the contribution to seed dispersal of vertebrate species with their topological role in frugivore-plant interaction networks. For one year in two localities with remnant patches of Colombian tropical dry forest, we sampled abundance, morphology, behaviour and fruit consumption from fleshy-fruited plants of various frugivore species. We assessed the network topological role of each frugivore species by integrating their degree of generalization in interactions with plants with their contributions to network nestedness and modularity. We estimated the potential contribution of each frugivore species to community-wide seed dispersal, on the basis of a set of frugivore ecological, morphological and behavioural characteristics important for seed dispersal, together with frugivore abundance and frugivory degree. The various frugivore species showed strong differences in their network structural roles, with generalist species contributing the most to network modularity and nestedness. Frugivores also showed strong variability in terms of potential contribution to seed dispersal, depending on the specific combinations of frugivore abundance, frugivory degree and the different traits and behaviours. For both localities, the seed dispersal potential of a frugivore species responded positively to its contribution to network structure, evidencing that the most important frugivore species in the network topology were also those making the strongest contribution as seed dispersers. Contribution to network structure was correlated with frugivore abundance, diet and behavioural characteristics. This suggests that the species-level link between structure and function is due to the fact that the occurrence of frugivore-plant interactions depends largely on the characteristics of the frugivore involved, which also condition its ultimate role in seed dispersal.

Integration and Segregation of the Brain Relate to Stability of Performance in Children and Adolescents with Varied Levels of Inattention and Impulsivity.

It is common to find that individuals with attention-deficit/hyperactivity disorder (ADHD) produce more variable responses when performing cognitive tasks. The neural mechanism associated with heightened response time variability (RTV) is not well understood in ADHD nor in typically developing individuals. One potential mechanism that might be associated with increased RTV is functional connectivity of the brain, and specifically inefficient connections. This study examined the relationships among functional connectivity of the brain, RTV, and levels of ADHD symptoms, using a cross-sectional developmental design. Twenty children aged 9-12 years and 49 adolescents aged 15-18 years completed the Sustained Attention to Response Task with flanker interference while electroencephalography (EEG) was recorded. The Conners 3 questionnaire was used to measure the participants' levels of ADHD symptoms. Parameters reflecting different aspects of RTV were computed using ex-Gaussian and fast Fourier transform techniques. Functional connectivity between 64 electrodes was computed for the task period, and global efficiency reflecting functional integration and modularity reflecting strength of functional segregation were computed. Greater global efficiency in the theta band was associated with decreased RTV. Increased integration during the task may help to combine information more efficiently and produce stable responses. When congruent flankers were present, children with greater modularity in the beta band showed greater tau, which is thought to reflect attentional lapses. This association was not observed when incongruent flankers were present. Brains with increased strength of segregated activity might be more prone to attentional lapses, especially during simpler tasks.

A multi-shell multi-tissue diffusion study of brain connectivity in early multiple sclerosis

Background: The potential of multi-shell diffusion imaging to produce accurate brain connectivity metrics able to unravel key pathophysiological processes in multiple sclerosis (MS) has scarcely been investigated. Objective: To test, in patients with a clinically isolated syndrome (CIS), whether multi-shell imaging-derived connectivity metrics can differentiate patients from controls, correlate with clinical measures, and perform better than metrics obtained with conventional single-shell protocols. Methods: Nineteen patients within 3 months from the CIS and 12 healthy controls underwent anatomical and 53-direction multi-shell diffusion-weighted 3T images. Patients were cognitively assessed. Voxel-wise fibre orientation distribution functions were estimated and used to obtain network metrics. These were also calculated using a conventional single-shell diffusion protocol. Through linear regression, we obtained effect sizes and standardised regression coefficients. Results: Patients had lower mean nodal strength (p = 0.003) and greater network modularity than controls (p = 0.045). Greater modularity was associated with worse cognitive performance in patients, even after accounting for lesion load (p = 0.002). Multi-shell-derived metrics outperformed single-shell-derived ones. Conclusion: Connectivity-based nodal strength and network modularity are abnormal in the CIS. Furthermore, the increased network modularity observed in patients, indicating microstructural damage, is clinically relevant. Connectivity analyses based on multi-shell imaging can detect potentially relevant network changes in early MS.

Partner Type Diversity in Alliance Portfolios: Multiple Dimensions, Boundary Conditions and Firm Innovation Performance

AbstractOur research extends the current knowledge based view on the configuration of alliance portfolios and their deployment in different external knowledge environments. We study these alliance portfolios in a longitudinal sample (1996–2010) for over three thousand firms that operate in a large number of industries in the Netherlands. Our findings indicate that partner type variety and partner type relevance, as different dimensions of partner diversity in alliance portfolios, both have an inverted U‐shaped association with firm innovation performance. However, alliance portfolios characterized by both high partner type variety and high relevance cause inferior innovation performance. Different external knowledge environments, characterized by different levels of industry modularity and scope of knowledge distribution, moderate the inverted U‐shaped associations of partner type variety and relevance in alliance portfolios with firm innovation performance in opposing directions. While for partner type variety, a high level is found to be optimal in environments with greater modularity or broader scope of knowledge distribution, for partner type relevance it turns out that a low level is optimal under more modular industry conditions.

Global patterns of interaction specialization in bird–flower networks

AbstractAimAmong the world's three major nectar‐feeding bird taxa, hummingbirds are the most phenotypically specialized for nectarivory, followed by sunbirds, while the honeyeaters are the least phenotypically specialized taxa. We tested whether this phenotypic specialization gradient is also found in the interaction patterns with their floral resources.LocationAmericas, Africa, Asia and Oceania/Australia.MethodsWe compiled interaction networks between birds and floral resources for 79 hummingbird, nine sunbird and 33 honeyeater communities. Interaction specialization was quantified through connectance (C), complementary specialization (H2′), binary (QB) and weighted modularity (Q), with both observed and null‐model corrected values. We compared interaction specialization among the three types of bird–flower communities, both independently and while controlling for potential confounding variables, such as plant species richness, asymmetry, latitude, insularity, topography, sampling methods and intensity.ResultsHummingbird–flower networks were more specialized than honeyeater–flower networks. Specifically, hummingbird–flower networks had a lower proportion of realized interactions (lower C), decreased niche overlap (greater H2′) and greater modularity (greater QB). However, we found no significant differences between hummingbird– and sunbird–flower networks, nor between sunbird– and honeyeater–flower networks.Main conclusionsAs expected, hummingbirds and their floral resources have greater interaction specialization than honeyeaters, possibly because of greater phenotypic specialization and greater floral resource richness in the New World. Interaction specialization in sunbird–flower communities was similar to both hummingbird–flower and honeyeater–flower communities. This may either be due to the relatively small number of sunbird–flower networks available, or because sunbird–flower communities share features of both hummingbird–flower communities (specialized floral shapes) and honeyeater–flower communities (fewer floral resources). These results suggest a link between interaction specialization and both phenotypic specialization and floral resource richness within bird–flower communities at a global scale.

PLUMED 2: New feathers for an old bird

Enhancing sampling and analyzing simulations are central issues in molecular simulation. Recently, we introduced PLUMED, an open-source plug-in that provides some of the most popular molecular dynamics (MD) codes with implementations of a variety of different enhanced sampling algorithms and collective variables (CVs). The rapid changes in this field, in particular new directions in enhanced sampling and dimensionality reduction together with new hardware, require a code that is more flexible and more efficient. We therefore present PLUMED 2 here—a complete rewrite of the code in an object-oriented programming language (C++). This new version introduces greater flexibility and greater modularity, which both extends its core capabilities and makes it far easier to add new methods and CVs. It also has a simpler interface with the MD engines and provides a single software library containing both tools and core facilities. Ultimately, the new code better serves the ever-growing community of users and contributors in coping with the new challenges arising in the field. Program summaryProgram title: PLUMED 2Catalogue identifier: AEEE_v2_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEEE_v2_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: YesNo. of lines in distributed program, including test data, etc.: 700646No. of bytes in distributed program, including test data, etc.: 6618136Distribution format: tar.gzProgramming language: ANSI-C++.Computer: Any computer capable of running an executable produced by a C++ compiler.Operating system: Linux operating system, Unix OSs.Has the code been vectorized or parallelized?: Yes, parallelized using MPI.RAM: Depends on the number of atoms, the method chosen and the collective variables used.Classification: 3, 7.7, 23.Catalogue identifier of previous version: AEEE_v1_0.Journal reference of previous version: Comput. Phys. Comm. 180 (2009) 1961.External routines: GNU libmatheval, Lapack, Blas, MPI.Does the new version supersede the previous version?: This version supersedes the previous version for the most part. There are a small number of very specific situations where the previous version is better, due to performance or to non-ported features. We are actively working on porting these last few features into the new code.Nature of problem:Calculation of free-energy surfaces for molecular systems of interest in biology, chemistry and materials science, on the fly and a posteriori analysis of molecular dynamics trajectories using advanced collective variables.Solution method:Implementations of various collective variables and enhanced sampling techniques.Reasons for new version:The old version was difficult to maintain and its design was not as flexible as this new version. This lack of flexibility made it difficult to implement a number of novel methods that have emerged since the release of the original code.Summary of revisions:The new version of the code has a completely redesigned architecture, which allows for several important enhancements. This allows for a much simpler and robust input syntax and for improved performance. In addition, it provides several, more-complex collective variables which could not have been written using the previous implementation. Furthermore, the entire code is fully documented so it is easier to extend. Finally, the code is designed so that users can implement new variables directly in the input files and thus develop bespoke applications of these powerful algorithms.Unusual features:PLUMED 2 can be used either as a standalone program, e.g. for a posteriori analysis of trajectories, or as a library embedded in a molecular dynamics code (such as GROMACS, NAMD, Quantum ESPRESSO, and LAMMPS). Interfaces with these particular codes are provided in patches, which a simple script will insert into the underlying molecular dynamics codes source code files. For other molecular dynamics codes there is extensive documentation on how to add PLUMED in our manual.Additional comments:The distribution file contains a test suite, user and developer documentation and a collection of patches and utilities.Running time:Depends on the number of atoms, the method chosen and the collective variables used. The regression test suite provided takes approximately 1 min to run.

Direct vs 2-stage approaches to structured motif finding

BackgroundThe notion of DNA motif is a mathematical abstraction used to model regions of the DNA (known as Transcription Factor Binding Sites, or TFBSs) that are bound by a given Transcription Factor to regulate gene expression or repression. In turn, DNA structured motifs are a mathematical counterpart that models sets of TFBSs that work in concert in the gene regulations processes of higher eukaryotic organisms. Typically, a structured motif is composed of an ordered set of isolated (or simple) motifs, separated by a variable, but somewhat constrained number of “irrelevant” base-pairs. Discovering structured motifs in a set of DNA sequences is a computationally hard problem that has been addressed by a number of authors using either a direct approach, or via the preliminary identification and successive combination of simple motifs.ResultsWe describe a computational tool, named SISMA, for the de-novo discovery of structured motifs in a set of DNA sequences. SISMA is an exact, enumerative algorithm, meaning that it finds all the motifs conforming to the specifications. It does so in two stages: first it discovers all the possible component simple motifs, then combines them in a way that respects the given constraints. We developed SISMA mainly with the aim of understanding the potential benefits of such a 2-stage approach w.r.t. direct methods. In fact, no 2-stage software was available for the general problem of structured motif discovery, but only a few tools that solved restricted versions of the problem. We evaluated SISMA against other published tools on a comprehensive benchmark made of both synthetic and real biological datasets. In a significant number of cases, SISMA outperformed the competitors, exhibiting a good performance also in most of the cases in which it was inferior.ConclusionsA reflection on the results obtained lead us to conclude that a 2-stage approach can be implemented with many advantages over direct approaches. Some of these have to do with greater modularity, ease of parallelization, and the possibility to perform adaptive searches of structured motifs. As another consideration, we noted that most hard instances for SISMA were easy to detect in advance. In these cases one may initially opt for a direct method; or, as a viable alternative in most laboratories, one could run both direct and 2-stage tools in parallel, halting the computations when the first halts.

A big-world network in ASD: Dynamical connectivity analysis reflects a deficit in long-range connections and an excess of short-range connections

Over the last years, increasing evidence has fuelled the hypothesis that Autism Spectrum Disorder (ASD) is a condition of altered brain functional connectivity. The great majority of these empirical studies relies on functional magnetic resonance imaging (fMRI) which has a relatively poor temporal resolution. Only a handful of studies has examined networks emerging from dynamic coherence at the millisecond resolution and there are no investigations of coherence at the lowest frequencies in the power spectrum—which has recently been shown to reflect long-range cortico-cortical connections. Here we used electroencephalography (EEG) to assess dynamic brain connectivity in ASD focusing in the low-frequency (delta) range. We found that connectivity patterns were distinct in ASD and control populations and reflected a double dissociation: ASD subjects lacked long-range connections, with a most prominent deficit in fronto-occipital connections. Conversely, individuals with ASD showed increased short-range connections in lateral–frontal electrodes. This effect between categories showed a consistent parametric dependency: as ASD severity increased, short-range coherence was more pronounced and long-range coherence decreased. Theoretical arguments have been proposed arguing that distinct patterns of connectivity may result in networks with different efficiency in transmission of information. We show that the networks in ASD subjects have less Clustering coefficient, greater Characteristic Path Length than controls – indicating that the topology of the network departs from small-world behaviour – and greater modularity. Together these results show that delta-band coherence reveal qualitative and quantitative aspects associated with ASD pathology.

SANDS: A service-oriented architecture for clinical decision support in a National Health Information Network

In this paper, we describe and evaluate a new distributed architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support), which leverages current health information exchange efforts and is based on the principles of a service-oriented architecture. The architecture allows disparate clinical information systems and clinical decision support systems to be seamlessly integrated over a network according to a set of interfaces and protocols described in this paper. The architecture described is fully defined and developed, and six use cases have been developed and tested using a prototype electronic health record which links to one of the existing prototype National Health Information Networks (NHIN): drug interaction checking, syndromic surveillance, diagnostic decision support, inappropriate prescribing in older adults, information at the point of care and a simple personal health record. Some of these use cases utilize existing decision support systems, which are either commercially or freely available at present, and developed outside of the SANDS project, while other use cases are based on decision support systems developed specifically for the project. Open source code for many of these components is available, and an open source reference parser is also available for comparison and testing of other clinical information systems and clinical decision support systems that wish to implement the SANDS architecture. The SANDS architecture for decision support has several significant advantages over other architectures for clinical decision support. The most salient of these are: 1. Greater modularity than other architectures, allowing for work to be distributed. 2. The potential for creating and sustaining a commercial market for clinical decision support. 3. Reduced cost and risk of trying new decision support systems because of its ability to easily integrate a variety of decision support services, and to easily remove them, if desired, as well. 4. Significant freedom for developers of clinical decision support systems to choose the way they represent knowledge and internally implement their system, in comparison to other approaches which constrain such developers to a particular knowledge representation formalism. 5. Unification of the direction and agenda of decision support research and development with promising near-term efforts to improve interoperability of clinical systems.

Efficient heterogeneous execution of Monte Carlo shielding calculations on a Beowulf cluster

Recent work has been done in using a high-performance 'Beowulf' cluster computer system for the efficient distribution of Monte Carlo shielding calculations. This has enabled the rapid solution of complex shielding problems at low cost and with greater modularity and scalability than traditional platforms. The work has shown that a simple approach to distributing the workload is as efficient as using more traditional techniques such as PVM (Parallel Virtual Machine). In addition, when used in an operational setting this technique is fairer with the use of resources than traditional methods, in that it does not tie up a single computing resource but instead shares the capacity with other tasks. These developments in computing technology have enabled shielding problems to be solved that would have taken an unacceptably long time to run on traditional platforms. This paper discusses the BNFL Beowulf cluster and a number of tests that have recently been run to demonstrate the efficiency of the asynchronous technique in running the MCBEND program. The BNFL Beowulf currently consists of 84 standard PCs running RedHat Linux. Current performance of the machine has been estimated to be between 40 and 100 Gflop s(-1). When the whole system is employed on one problem up to four million particles can be tracked per second. There are plans to review its size in line with future business needs.