A Study of Brain Networks Associated with Swallowing Using Graph-Theoretical Approaches

Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI) was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, 23.1±1.52 years of age). To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia. © 2013 Luan et al

Computational investigation of diesel nozzle internal flow during the complete injection event

[EN] Currently, diesel engines are calibrated using more and more complex multiple injection strategies. Under these conditions, the characteristics of the flow exiting the fuel injector are strongly affected by the transient interaction between the needle, the sac volume and the orifices, which are not yet clear. In the current paper, a methodology combining a 1D injector model and 3D-CFD simulations is proposed. First, the characteristics of the nozzle flow have been experimentally assessed in transient conditions by means of injection rate and momentum flux measurements. Later, the 3D-CFD modeling approach has been validated at steady-state fixed lift conditions. Finally, a previously developed 1D injector model has been used to extract the needle lift profiles and transient pressure boundary conditions used for the full-transient 3D-CFD simulations, using adaptive mesh refinement (AMR) strategies to be able to simulate the complete injection rate starting from 1 mu m lift.This work was partly sponsored by "Ministerio de Economia y Competitividad'', of the Spanish Government, in the frame of the Project "Estudio de la interaccion chorro-pared en condiciones realistas de motor'', Reference TRA2015-67679-c2-1-R. The authors would like also to thank the computer resources, technical expertise and assistance provided by Universidad de Valencia in the use of the supercomputer "Tirant''. Mr. Jaramillo's Thesis is funded by "Conselleria d'Educacio, Cultura i Esports'' of Generalitat Valenciana in the frame of the program "Programa VALI + D para investigadores en formacion, Reference ACIF/2015/040.Salvador, FJ.; De La Morena, J.; Bracho Leon, G.; Jaramillo-Císcar, D. (2018). Computational investigation of diesel nozzle internal flow during the complete injection event. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 40(3):153-167. https://doi.org/10.1007/s40430-018-1074-zS153167403Hall CAS, Lambert JG, Balogh SB (2014) EROI of different fuels and the implications for society. Energy Policy 64:141–152. https://doi.org/10.1016/j.enpol.2013.05.049Lujan JM, Tormos B, Salvador FJ, Gargar K (2009) Comparative analysis of a DI diesel engine fuelled with biodiesel blends during the European MVEG-A cycle: preliminary study (I). 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Fuel 118:316–328. https://doi.org/10.1016/j.fuel.2013.11.001Desantes JM, Salvador FJ, Lopez JJ, De la Morena J (2011) Study of mass and momentum transfer in diesel sprays based on X-ray mass distribution measurements and on a theoretical derivation. Exp Fluids 50:233–246. https://doi.org/10.1007/s00348-010-0919-8De la Morena J, Neroorkar K, Plazas AH et al (2013) Numerical analysis of the influence of diesel nozzle design on internal flow characteristics for 2-valve diesel engine application. At Sprays 23:97–118. https://doi.org/10.1615/AtomizSpr.2013006361Duke DJ, Schmidt DP, Neroorkar K et al (2013) High-resolution large eddy simulations of cavitating gasoline-ethanol blends. Int J Engine Res 14:578–589. https://doi.org/10.1177/1468087413501824Mitroglou N, McLorn M, Gavaises M et al (2014) Instantaneous and ensemble average cavitation structures in diesel micro-channel flow orifices. 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Heritability of Attractiveness to Mosquitoes

Female mosquitoes display preferences for certain individuals over others, which is determined by differences in volatile chemicals produced by the human body and detected by mosquitoes. Body odour can be controlled genetically but the existence of a genetic basis for differential attraction to insects has never been formally demonstrated. This study investigated heritability of attractiveness to mosquitoes by evaluating the response of Aedes aegypti (=Stegomyia aegypti) mosquitoes to odours from the hands of identical and non-identical twins in a dual-choice assay. Volatiles from individuals in an identical twin pair showed a high correlation in attractiveness to mosquitoes, while non-identical twin pairs showed a significantly lower correlation. Overall, there was a strong narrow-sense heritability of 0.62 (SE 0.124) for relative attraction and 0.67 (0.354) for flight activity based on the average of ten measurements. The results demonstrate an underlying genetic component detectable by mosquitoes through olfaction. Understanding the genetic basis for attractiveness could create a more informed approach to repellent development

Clinical inertia in poorly controlled elderly hypertensive patients: a cross-sectional study in Spanish physicians to ascertain reasons for not intensifying treatment

Background Clinical inertia, the failure of physicians to initiate or intensify therapy when indicated, is a major problem in the management of hypertension and may be more prevalent in elderly patients. Overcoming clinical inertia requires understanding its causes and evaluating certain factors, particularly those related to physicians. Objective The objective of our study was to determine the rate of clinical inertia and the physician-reported rea- sons for it. Conclusion Physicians provided reasons for not intensi- fying treatment in poorly controlled patients in only 30 % of instances. Main reasons for not intensifying treatment were borderline BP values, co-morbidity, suspected white coat effect, or perceived difficulty achieving target. nJCI was associated with high borderline BP values and car- diovascular diseas

A DIGE study on the effects of salbutamol on the rat muscle proteome - an exemplar of best practice for data sharing in proteomics

BACKGROUND: Proteomic techniques allow researchers to perform detailed analyses of cellular states and many studies are published each year, which highlight large numbers of proteins quantified in different samples. However, currently few data sets make it into public databases with sufficient metadata to allow other groups to verify findings, perform data mining or integrate different data sets. The Proteomics Standards Initiative has released a series of "Minimum Information About a Proteomics Experiment" guideline documents (MIAPE modules) and accompanying data exchange formats. This article focuses on proteomic studies based on gel electrophoresis and demonstrates how the corresponding MIAPE modules can be fulfilled and data deposited in public databases, using a new experimental data set as an example. FINDINGS: We have performed a study of the effects of an anabolic agent (salbutamol) at two different time points on the protein complement of rat skeletal muscle cells, quantified by difference gel electrophoresis. In the DIGE study, a total of 31 non-redundant proteins were identified as being potentially modulated at 24 h post treatment and 110 non redundant proteins at 96 h post-treatment. Several categories of function have been highlighted as strongly enriched, providing candidate proteins for further study. We also use the study as an example of best practice for data deposition. CONCLUSIONS: We have deposited all data sets from this study in public databases for further analysis by the community. We also describe more generally how gel-based protein identification data sets can now be deposited in the PRoteomics IDEntifications database (PRIDE), using a new software tool, the PRIDESpotMapper, which we developed to work in conjunction with the PRIDE Converter application. We also demonstrate how the ProteoRed MIAPE generator tool can be used to create and share a complete and compliant set of MIAPE reports for this experiment and others

Effects of Different Correlation Metrics and Preprocessing Factors on Small-World Brain Functional Networks: A Resting-State Functional MRI Study

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01–0.027 Hz) versus slow-4 (0.027–0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the “best” network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027–0.073 Hz band exhibited greater reliability than those in the 0.01–0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies

Endurance of methanogenic archaea in anaerobic bioreactors treating oleate-based wastewater

Methanogenic archaea are reported as very sensitive to lipids and long chain fatty acids (LCFA). Therefore, in conventional anaerobic processes, methane recovery during LCFA-rich wastewater treatment is usually low. By applying a start-up strategy, based on a sequence of step feeding and reaction cycles, an oleate-rich wastewater was efficiently treated at an organic loading rate of 21 kg COD m(-3) day(-1) (50 % as oleate), showing a methane recovery of 72 %. In the present work, the archaeal community developed in that reactor is investigated using a 16S rRNA gene approach. This is the first time that methanogens present in a bioreactor converting efficiently high loads of LCFA to methane are monitored. Denaturing gradient gel electrophoresis profiling showed that major changes on the archaeal community took place during the bioreactor start-up, where phases of continuous feeding were alternated with batch phases. After the start-up, a stable archaeal community (similarity higher than 84 %) was observed and maintained throughout the continuous operation. This community exhibited high LCFA tolerance and high acetoclastic and hydrogenotrophic activity. Cloning and sequencing results showed that Methanobacterium- and Methanosaeta-like microorganisms prevailed in the system and were able to tolerate and endure during prolonged exposure to high LCFA loads, despite the previously reported LCFA sensitivity of methanogens.This study has been financially supported by FEDER funds through the Operational Competitiveness Programme (COMPETE) and by national funds through the Portuguese Foundation for Science and Technology (FCT) in the frame of the projects FCOMP-01-0124-FEDER-007087 and FCOMP-01-0124-FEDER-014784. Financial support from FCT and the European Social Fund (ESF) through PhD grants SFRH/BD/48960/2008 and SFRH/BD/24256/2005 attributed to Andreia Salvador and Ana Julia Cavaleiro is also acknowledged

Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease

Functional brain networks detected in task-free (“resting-state”) functional magnetic resonance imaging (fMRI) have a small-world architecture that reflects a robust functional organization of the brain. Here, we examined whether this functional organization is disrupted in Alzheimer's disease (AD). Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to the fMRI data to compute frequency-dependent correlation matrices. Correlation matrices were thresholded to create 90-node undirected-graphs of functional brain networks. Small-world metrics (characteristic path length and clustering coefficient) were computed using graph analytical methods. In the low frequency interval 0.01 to 0.05 Hz, functional brain networks in controls showed small-world organization of brain activity, characterized by a high clustering coefficient and a low characteristic path length. In contrast, functional brain networks in AD showed loss of small-world properties, characterized by a significantly lower clustering coefficient (p<0.01), indicative of disrupted local connectivity. Clustering coefficients for the left and right hippocampus were significantly lower (p<0.01) in the AD group compared to the control group. Furthermore, the clustering coefficient distinguished AD participants from the controls with a sensitivity of 72% and specificity of 78%. Our study provides new evidence that there is disrupted organization of functional brain networks in AD. Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging

Functional Brain Networks Develop from a “Local to Distributed” Organization

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways

The graduation performance of technology business incubators in China's three tier cities: the role of incubator funding, technical support, and entrepreneurial mentoring

This study examines the effects of technology business incubator (TBI)’s funding, technical support and entrepreneurial mentoring on the graduation performance of new technology-based firms in China’s three tier cities. Using new dataset on all TBIs and incubated new technology-based firms from government surveys conducted over five consecutive years from 2009 to 2013 combined with archival and hand-collected data, we find the effects of incubator services on the early growth of new technology-based firms vary according to the local context. Technical support facilities and entrepreneurial mentoring from TBIs are found to have significantly and positively influenced the early development of the firms in the four most affluent tier 1 cities, whilst these effects become less pronounced for the tier 2 and tier 3 cities. These two services are also found to influence graduation performance in the government and university types of TBI respectively. Results support the notion that the effectiveness of an incubators services is shaped by the level of a city’s socio-economic development and that the city location of a TBI does impact the graduation performance of its incubatees