Multimodal integration of EEG, MEG and fMRI data for the solution of the neuroimage puzzle

In this paper, advanced methods for the modeling of human cortical activity from combined high-resolution electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) data are presented. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from magnetic resonance images, multidipole source model and regularized linear inverse source estimates of cortical current density. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) fMRI. Examples of the application of these methods to the estimation of the time varying cortical current density activity in selected region of interest (ROI) are presented for movement-related high-resolution EEG data.     

Receptor-based high-throughput screening and identification of estrogens in dietary supplements using bioaffinity liquid-chromatography ion mobility mass spectrometry

A high-throughput bioaffinity liquid chromatography-mass spectrometry (BioMS) approach was developed and applied for the screening and identification of recombinant human estrogen receptor α (ERα) ligands in dietary supplements. For screening, a semi-automated mass spectrometric ligand binding assay was developed applying ¹³C_2, ¹⁵?N-tamoxifen as non-radioactive label and fast ultra-high-performance–liquid chromatography–electrospray ionisation–triple-quadrupole-MS (UPLC-QqQ-MS), operated in the single reaction monitoring mode, as a readout system. Binding of the label to ERα-coated paramagnetic microbeads was inhibited by competing estrogens in the sample extract yielding decreased levels of the label in UPLC-QqQ-MS. The label showed high ionisation efficiency in positive electrospray ionisation (ESI) mode, so the developed BioMS approach is able to screen for estrogens in dietary supplements despite their poor ionisation efficiency in both positive and negative ESI modes. The assay was performed in a 96-well plate, and all these wells could be measured within 3 h. Estrogens in suspect extracts were identified by full-scan accurate mass and collision-cross section (CCS) values from a UPLC-ion mobility-Q-time-of-flight-MS (UPLC-IM-Q-ToF-MS) equipped with a novel atmospheric pressure ionisation source. Thanks to the novel ion source, this instrument provided picogram sensitivity for estrogens in the negative ion mode and an additional identification point (experimental CCS values) next to retention time, accurate mass and tandem mass spectrometry data. The developed combination of bioaffinity screening with UPLC-QqQ-MS and identification with UPLC-IM-Q-ToF-MS provides an extremely powerful analytical tool for early warning of ERα bioactive compounds in dietary supplements as demonstrated by analysis of selected dietary supplements in which different estrogens were identified.

Sul movimento di un punto non soggetto ad alcuna forza sopra un toro

Rendiconti del Circolo Matematico di Palermo Series 1 -     

A Green Blue LED-Driven Two-Liquid-Phase One-Pot Procedure for the Synthesis of Estrogen-Related Quinol Prodrugs

Quinol derivatives of estrogens are effective pro-drugs in steroid replacement therapy. Here, we report that these compounds can be synthesized in one-pot conditions and high yield by blue LED-driven photo-oxygenation of parent estrogens. The oxidation was performed in buffer and eco-certified 2-methyltetrahydrofuran as the two-liquid-phase reaction solvent, and in the presence of meso-tetraphenyl porphyrin as the photosensitizer. Two steroidal prodrugs 10β, 17β-dihydroxyestra-1,4-dien-3-one (DHED) and 10β-Hydroxyestra-1,4-diene-3,17-dione (HEDD) were obtained with high yield and selectivity.     

Combining social and genetic networks to study HIV transmission in mixing risk groups

Reconstruction of HIV transmission networks is important for understanding and preventing the spread of the virus and drug resistant variants. Mixing risk groups is important in network analysis of HIV in order to assess the role of transmission between risk groups in the HIV epidemic. Most of the research focuses on the transmission within HIV risk groups, while transmission between different risk groups has been less studied. We use a proposed filter-reduction method to infer hypothetical transmission networks of HIV by combining data from social and genetic scales. We modified the filtering process in order to include mixing risk groups in the model. For this, we use the information on phylogenetic clusters obtained through phylogenetic analysis. A probability matrix is also defined to specify contact rates between individuals form the same and different risk groups. The method converts the data form each scale into network forms and combines them by overlaying and computing their intersection. We apply this method to reconstruct networks of HIV infected patients in central Italy, including mixing between risk groups. Our results suggests that bisexual behavior among Italian MSM and IDU partnership are relatively important in heterosexual transmission of HIV in central Italy.     

Using mixed-integer programming to solve power grid blackout problems

We consider optimization problems related to the prevention of large-scale cascading blackouts in power transmission networks subject to multiple scenarios of externally caused damage. We present computation with networks with up to 600 nodes and 827 edges, and many thousands of damage scenarios.     

Dynamical Renormalization Group for Mode-Coupling Field Theories with Solenoidal Constraint

Journal of Statistical Physics - A correction to this paper has been published: https://doi.org/10.1007/s10955-021-02770-w