Optimizing Stimulation and Analysis Protocols for Neonatal fMRI

The development of brain function in young infants is poorly understood. The core challenge is that infants have a limited behavioral repertoire through which brain function can be expressed. Neuroimaging with fMRI has great potential as a way of characterizing typical development, and detecting abnormal development early. But, a number of methodological challenges must first be tackled to improve the robustness and sensitivity of neonatal fMRI. A critical one of these, addressed here, is that the hemodynamic response function (HRF) in pre-term and term neonates differs from that in adults, which has a number of implications for fMRI. We created a realistic model of noise in fMRI data, using resting-state fMRI data from infants and adults, and then conducted simulations to assess the effect of HRF of the power of different stimulation protocols and analysis assumptions (HRF modeling). We found that neonatal fMRI is most powerful if block-durations are kept at the lower range of those typically used in adults (full on/off cycle duration 25-30s). Furthermore, we show that it is important to use the age-appropriate HRF during analysis, as mismatches can lead to reduced power or even inverted signal. Where the appropriate HRF is not known (for example due to potential developmental delay), a flexible basis set performs well, and allows accurate post-hoc estimation of the HRF

Thermoelectric spin voltage in graphene

In recent years, new spin-dependent thermal effects have been discovered in ferromagnets, stimulating a growing interest in spin caloritronics, a field that exploits the interaction between spin and heat currents. Amongst the most intriguing phenomena is the spin Seebeck effect, in which a thermal gradient gives rise to spin currents that are detected through the inverse spin Hall effect. Non-magnetic materials such as graphene are also relevant for spin caloritronics, thanks to efficient spin transport, energy-dependent carrier mobility and unique density of states. Here, we propose and demonstrate that a carrier thermal gradient in a graphene lateral spin valve can lead to a large increase of the spin voltage near to the graphene charge neutrality point. Such an increase results from a thermoelectric spin voltage, which is analogous to the voltage in a thermocouple and that can be enhanced by the presence of hot carriers generated by an applied current. These results could prove crucial to drive graphene spintronic devices and, in particular, to sustain pure spin signals with thermal gradients and to tune the remote spin accumulation by varying the spin-injection bias

Preparation of Ni–YSZ thin and thick films on metallic interconnects as cell supports. Applications as anode for SOFC

In this work, we propose the preparation of a duplex anodic layer composed of both a thin (100 nm) and a thick film (10 lm) with Ni–YSZ material. The support of this anode is a metallic substrate, which is the interconnect of the SOFC unit cell. The metallic support limits the temperature of thermal treatment at 800 C to keep a good interconnect mechanical behaviour and to reduce corrosion. We have chosen to elaborate anodic coatings by sol–gel route coupled with dip-coating process, which are low cost techniques and allow working with moderate temperatures. Thin films are obtained by dipping interconnect substrate into a sol, and thick films into an optimized slurry. After thermal treatment at only 800 C, anodic coatings are adherent and homogeneous. Thin films have compact microstructures that confer ceramic protective barrier on metal surface. Further coatings of 10 lm thick are porous and constitute the active anodic material

The LHC Phenomenology of Vectorlike Confinement

We investigate in detail the LHC phenomenology of "vectorlike confinement", where the Standard Model is augmented by a new confining gauge interaction and new light fermions that carry vectorlike charges under both the Standard Model and the new gauge group. If the new interaction confines at the TeV scale, this framework gives rise to a wide range of exotic collider signatures such as the production of a vector resonance that decays to a pair of collider-stable charged massive particles (a "di-CHAMP" resonance), to a pair of collider-stable massive colored particles (a "di-R-hadron resonance), to multiple photons, $W$s and $Z$s via two intermediate scalars, and/or to multi-jet final states. To study these signals at the LHC, we set up two benchmark models: one for the di-CHAMP and multi-photon signals, and the other for the di-R-hadron and multijet signals. For the di-CHAMP/multi-photon model, Standard Model backgrounds are negligible, and we show that a full reconstruction of the spectrum is possible, providing powerful evidence for vectorlike confinement. For the di-R-hadron/multijet model, we point out that in addition to the di-R-hadron signal, the rate of the production of four R-hadrons can also be sizable at the LHC. This, together with the multi-jet signals studied in earlier work, makes it possible to single out vectorlike confinement as the underlying dynamics.Comment: 32 pages, 28 figures. Several typos fixed, one paragraph added elaborating choice of benchmarks. Version accepted by JHEP

Things change: Women’s and men’s marital disruption dynamics in Italy during a time of social transformations, 1970-2003

We study women’s and men’s marital disruption in Italy between 1970 and 2003. By applying an event-history analysis to the 2003 Italian variant of the Generations and Gender Survey we found that the spread of marital disruption started among middle-highly educated women. Then in recent years it appears that less educated women have also been able to dissolve their unhappy unions. Overall we can see the beginning of a reversed educational gradient from positive to negative. In contrast the trend in men’s marital disruption risk appears as a change over time common to all educational groups, although with persisting educational differentials.determinants, educational differences, event history analysis, gender difference, Italy, marital disruption

Evidence for dark matter in the inner Milky Way

The ubiquitous presence of dark matter in the universe is today a central tenet in modern cosmology and astrophysics. Ranging from the smallest galaxies to the observable universe, the evidence for dark matter is compelling in dwarfs, spiral galaxies, galaxy clusters as well as at cosmological scales. However, it has been historically difficult to pin down the dark matter contribution to the total mass density in the Milky Way, particularly in the innermost regions of the Galaxy and in the solar neighbourhood. Here we present an up-to-date compilation of Milky Way rotation curve measurements, and compare it with state-of-the-art baryonic mass distribution models. We show that current data strongly disfavour baryons as the sole contribution to the galactic mass budget, even inside the solar circle. Our findings demonstrate the existence of dark matter in the inner Galaxy while making no assumptions on its distribution. We anticipate that this result will compel new model-independent constraints on the dark matter local density and profile, thus reducing uncertainties on direct and indirect dark matter searches, and will shed new light on the structure and evolution of the Galaxy.Comment: First submitted version of letter published in Nature Physics on Febuary 9, 2015: http://www.nature.com/nphys/journal/vaop/ncurrent/full/nphys3237.htm

MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe

Performance of Monolayer Graphene Nanomechanical Resonators with Electrical Readout

The enormous stiffness and low density of graphene make it an ideal material for nanoelectromechanical (NEMS) applications. We demonstrate fabrication and electrical readout of monolayer graphene resonators, and test their response to changes in mass and temperature. The devices show resonances in the MHz range. The strong dependence of the resonant frequency on applied gate voltage can be fit to a membrane model, which yields the mass density and built-in strain. Upon removal and addition of mass, we observe changes in both the density and the strain, indicating that adsorbates impart tension to the graphene. Upon cooling, the frequency increases; the shift rate can be used to measure the unusual negative thermal expansion coefficient of graphene. The quality factor increases with decreasing temperature, reaching ~10,000 at 5 K. By establishing many of the basic attributes of monolayer graphene resonators, these studies lay the groundwork for applications, including high-sensitivity mass detectors

Mining and analysis of audiology data to find significant factors associated with tinnitus masker

Objectives: The objective of this research is to find the factors associated with tinnitus masker from the literature, and by using the large amount of audiology data available from a large NHS (National Health Services, UK) hearing aid clinic. The factors evaluated were hearing impairment, age, gender, hearing aid type, mould and clinical comments. Design: The research includes literature survey for factors associated with tinnitus masker, and performs the analysis of audiology data using statistical and data mining techniques. Setting: This research uses a large audiology data but it also faced the problem of limited data for tinnitus. Participants: It uses 1,316 records for tinnitus and other diagnoses, and 10,437 records of clinical comments from a hearing aid clinic. Primary and secondary outcome measures: The research is looking for variables associated with tinnitus masker, and in future, these variables can be combined into a single model to develop a decision support system to predict about tinnitus masker for a patient. Results: The results demonstrated that tinnitus maskers are more likely to be fit to individuals with milder forms of hearing loss, and the factors age, gender, type of hearing aid and mould were all found significantly associated with tinnitus masker. In particular, those patients having Age<=55 years were more likely to wear a tinnitus masker, as well as those with milder forms of hearing loss. ITE (in the ear) hearing aids were also found associated with tinnitus masker. A feedback on the results of association of mould with tinnitus masker from a professional audiologist of a large NHS (National Health Services, UK) was also taken to better understand them. The results were obtained with different accuracy for different techniques. For example, the chi-squared test results were obtained with 95% accuracy, for Support and Confidence only those results were retained which had more than 1% Support and 80% Confidence. Conclusions: The variables audiograms, age, gender, hearing aid type and mould were found associated with the choice of tinnitus masker in the literature and by using statistical and data mining techniques. The further work in this research would lead to the development of a decision support system for tinnitus masker with an explanation that how that decision was obtained

Extremely stable graphene electrodes doped with macromolecular acid

Although conventional p-type doping using small molecules on graphene decreases its sheet resistance (Rsh), it increases after exposure to ambient conditions, and this problem has been considered as the biggest impediment to practical application of graphene electrodes. Here, we report an extremely stable graphene electrode doped with macromolecular acid (perfluorinated polymeric sulfonic acid (PFSA)) as a p-type dopant. The PFSA doping on graphene provides not only ultra-high ambient stability for a very long time (&gt; 64 days) but also high chemical/thermal stability, which have been unattainable by doping with conventional small-molecules. PFSA doping also greatly increases the surface potential (similar to 0.8 eV) of graphene, and reduces its Rsh by similar to 56%, which is very important for practical applications. High-efficiency phosphorescent organic light-emitting diodes are fabricated with the PFSA-doped graphene anode (similar to 98.5 cd A(-1) without out-coupling structures). This work lays a solid platform for practical application of thermally-/chemically-/air-stable graphene electrodes in various optoelectronic devices