Cortical Synchrony and Information Flow during Transition from Wakefulness to Light Non-Rapid Eye Movement Sleep.

Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using MEG imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 years [SD 11.8 years]). We furthermore performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in frontoparietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to Stage 1 sleep. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM compared with wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition.SIGNIFICANCE STATEMENT Our work uncovers spatiotemporal cortical structure of neural synchrony and information flow upon the transition from wakefulness to light non-rapid eye movement sleep. Mesial frontal regions were identified to receive hierarchically organized inputs from broad cortical regions, including both direct inputs from occipital regions and indirect inputs via the parieto-temporal regions within the delta frequency range. Biophysical neural mass modeling revealed a spatially heterogeneous, anterior-posterior distribution of cortical excitation-to-inhibition. Our findings shed light on the orchestration of local and long-range cortical neural structure that is fundamental to sleep onset, and support an emerging view of cortically driven regulation of sleep homeostasis.

Bayesian inference of a spectral graph model for brain oscillations.

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which cannot be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

Spectral graph theory of brain oscillations--Revisited and improved.

Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso-(regional population ensemble) and macro-scale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters and without multistable dynamics can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately. In this work, we demonstrate an improved hierarchical, linearized, and analytic spectral graph theory-based model that can capture the frequency spectra obtained from magnetoencephalography recordings of resting healthy subjects. We reformulated the spectral graph theory model in line with classical neural mass models, therefore providing more biologically interpretable parameters, especially at the local scale. We demonstrated that this model performs better than the original model when comparing the spectral correlation of modeled frequency spectra and that obtained from the magnetoencephalography recordings. This model also performs equally well in predicting the spatial patterns of the empirical alpha and beta frequency bands.

Emergence of directional bias in tau deposition from axonal transport dynamics.

Defects in axonal transport may partly underpin the differences between the observed pathophysiology of Alzheimer's disease (AD) and that of other non-amyloidogenic tauopathies. Particularly, pathological tau variants may have molecular properties that dysregulate motor proteins responsible for the anterograde-directed transport of tau in a disease-specific fashion. Here we develop the first computational model of tau-modified axonal transport that produces directional biases in the spread of tau pathology. We simulated the spatiotemporal profiles of soluble and insoluble tau species in a multicompartment, two-neuron system using biologically plausible parameters and time scales. Changes in the balance of tau transport feedback parameters can elicit anterograde and retrograde biases in the distributions of soluble and insoluble tau between compartments in the system. Aggregation and fragmentation parameters can also perturb this balance, suggesting a complex interplay between these distinct molecular processes. Critically, we show that the model faithfully recreates the characteristic network spread biases in both AD-like and non-AD-like mouse tauopathy models. Tau transport feedback may therefore help link microscopic differences in tau conformational states and the resulting variety in clinical presentations.

Visible-Light-Driven Photocatalytic CO2 Reduction to CO/CH4 Using a Metal-Organic "Soft" Coordination Polymer Gel.

The self-assembly of a well-defined and astutely designed, low-molecular weight gelator (LMWG) based linker with a suitable metal ion is a promising method for preparing photocatalytically active coordination polymer gels. Here, we report the design, synthesis, and gelation behaviour of a tetrapodal LMWG based on a porphyrin core connected to four terpyridine units (TPY-POR) through amide linkages. The self-assembly of TPY-POR LMWG with RuII ions results in a Ru-TPY-POR coordination polymer gel (CPG), with a nanoscroll morphology. Ru-TPY-POR CPG exhibits efficient CO2 photoreduction to CO (3.5 mmol g-1 h-1 ) with >99 % selectivity in the presence of triethylamine (TEA) as a sacrificial electron donor. Interestingly, in the presence of 1-benzyl-1,4-dihydronicotinamide (BNAH) with TEA as the sacrificial electron donor, the 8e- /8H+ photoreduction of CO2 to CH4 is realized with >95 % selectivity (6.7 mmol g-1 h-1 ). In CPG, porphyrin acts as a photosensitizer and covalently attached [Ru(TPY)2 ]2+ acts as a catalytic center as demonstrated by femtosecond transient absorption (TA) spectroscopy. Further, combining information from the in situ DRIFT spectroscopy and DFT calculation, a possible reaction mechanism for CO2 reduction to CO and CH4 was outlined.

Bimodal Heterogeneous Functionality in Redox-Active Conjugated Microporous Polymer toward Electrocatalytic Oxygen Reduction and Photocatalytic Hydrogen Evolution.

The designing and development of heterogeneous catalysts for conversion of renewable energy to chemical energies by electrochemical as well as photochemical processes is at the forefront of energy research. In this work, two new donor-acceptor-based redox-active conjugated microporous polymers (CMPs) (TAPA-OPE-mix and TAPA-OPE-gly) are synthesized through Schiff base condensation reaction using a microwave synthesizer. Notably, the asymmetric and symmetric bola-amphiphilic nature of the OPE struts results in distinct nanostructuring and morphologies in the CMPs. Interestingly, both CMPs show impressive heterogeneous catalytic activity toward electrochemical O2 reduction and photocatalytic H2 evolution reactions, and therefore, act as bimodal electro- and photocatalytic porous organic materials. Furthermore, the redox-active property of the CMPs is exploited for in situ generation and stabilization of platinum nanoparticles (Pt), and these Pt@CMPs exhibit significantly enhanced photocatalytic activity.

Computational analysis of a 9D model for a small DRG neuron.

Small dorsal root ganglion (DRG) neurons are primary nociceptors which are responsible for sensing pain. Elucidation of their dynamics is essential for understanding and controlling pain. To this end, we present a numerical bifurcation analysis of a small DRG neuron model in this paper. The model is of Hodgkin-Huxley type and has 9 state variables. It consists of a Nav1.7 and a Nav1.8 sodium channel, a leak channel, a delayed rectifier potassium, and an A-type transient potassium channel. The dynamics of this model strongly depend on the maximal conductances of the voltage-gated ion channels and the external current, which can be adjusted experimentally. We show that the neuron dynamics are most sensitive to the Nav1.8 channel maximal conductance ([Formula: see text]). Numerical bifurcation analysis shows that depending on [Formula: see text] and the external current, different parameter regions can be identified with stable steady states, periodic firing of action potentials, mixed-mode oscillations (MMOs), and bistability between stable steady states and stable periodic firing of action potentials. We illustrate and discuss the transitions between these different regimes. We further analyze the behavior of MMOs. As the external current is decreased, we find that MMOs appear after a cyclic limit point. Within this region, bifurcation analysis shows a sequence of isolated periodic solution branches with one large action potential and a number of small amplitude peaks per period. For decreasing external current, the number of small amplitude peaks is increasing and the distance between the large amplitude action potentials is growing, finally tending to infinity and thereby leading to a stable steady state. A closer inspection reveals more complex concatenated MMOs in between these periodic MMO branches, forming Farey sequences. Lastly, we also find small solution windows with aperiodic oscillations which seem to be chaotic. The dynamical patterns found here-as consequences of bifurcation points regulated by different parameters-have potential translational significance as repetitive firing of action potentials imply pain of some form and intensity; manipulating these patterns by regulating the different parameters could aid in investigating pain dynamics.

Regulating Charge-Transfer in Conjugated Microporous Polymers for Photocatalytic Hydrogen Evolution.

Bandgap engineering in donor-acceptor conjugated microporous polymers (CMPs) is a potential way to increase the solar-energy harvesting towards photochemical water splitting. Here, the design and synthesis of a series of donor-acceptor CMPs [tetraphenylethylene (TPE) and 9-fluorenone (F) as the donor and the acceptor, respectively], F0.1 CMP, F0.5 CMP, and F2.0 CMP, are reported. These CMPs exhibited tunable bandgaps and photocatalytic hydrogen evolution from water. The donor-acceptor CMPs exhibited also intramolecular charge-transfer (ICT) absorption in the visible region (λmax =480 nm) and their bandgap was finely tuned from 2.8 to 2.1 eV by increasing the 9-fluorenone content. Interestingly, they also showed emissions in the 540-580 nm range assisted by the energy transfer from the other TPE segments (not involved in charge-transfer interactions), as evidenced from fluorescence lifetime decay analysis. By increasing the 9-fluorenone content the emission color of the polymer was also tuned from green to red. Photocatalytic activities of the donor-acceptor CMPs (F0.1 CMP, F0.5 CMP, and F2.0 CMP) are greatly enhanced compared to the 9-fluorenone free polymer (F0.0 CMP), which is essentially due to improved visible-light absorption and low bandgap of donor-acceptor CMPs. Among all the polymers F0.5 CMP with an optimum bandgap (2.3 eV) showed the highest H2 evolution under visible-light irradiation. Moreover, all polymers showed excellent dispersibility in organic solvents and easy coated on the solid substrates.

Photoswitchable J-Aggregated Processable Organogel by Integrating a Photochromic Acceptor.

A novel π-chromophoric 1,4-bis(anthracenylethynyl)benzene (BAB)-based highly emissive J-aggregated organogel has been synthesized and characterized. Single-crystal structure determination of asymmetric π-chromophoric bola-amphiphilic BAB1 (dodecyl and triethyleneglycolmonomethylether containing side chains of bis(anthracenylethynyl)benzene) supports J-aggregation. Further, a photochromic acceptor chromophore, 4,4'-(perfluorocyclopent-1-ene-1,2-diyl)bis(5-methylthiophene-2-carbaldehyde), is noncovalently encapsulated in the gel and photoswitching studies have been performed based on photochromic Förster resonance energy transfer. The modulated emission of the processable soft material is further exploited for rewritable display. However, BAB2 (dodecyl side chain on both sides) does not show gelation property due to its low solubility.

Evaluation of 4% Sodium Hypochlorite in eliminating Enterococcus faecalis from the Root Canal when Used with Three Irrigation Methods: An in vitro Study.

INTRODUCTION: Endodontic treatment removes all pathogens, such as Enterococcus faecalis from pulp and root canals. The aim of this study is to assess the usefulness of sodium hypo-chlorite (NaOCl) in removing E. faecalis from the root canal used with three different irrigation methods. MATERIALS AND METHODS: This study was conducted on freshly extracted maxillary incisors. After biomechanical preparation, root canals were injected with E. faecalis. Three groups were made which contained 30 teeth in each group; 2 mL of NaOCl solution was used for irrigation followed by agitation with K-files in group I; 2 mL of NaOCl solution was used for irrigation and ultrasonic agitation was done in group II. In group III, an alternate irrigation with NaOCl and 3% hydrogen peroxide was done. The fourth group (control) was irrigated with sterile saline solution. E. fae-calis bacteria were sampled to the root canals with paper points and were transferred to tubes that contained 5 mL of brain heart infusion broth. Tubes were incubated and the presence of broth turbidity was suggestive of bacteria remaining in the root canal. RESULTS: All three groups showed no statistically significant difference. However, difference existed between experimental groups and control groups. CONCLUSION: The author concluded that all three methods of application of NaOCl were effective in disinfecting the root canal than the saline solution. CLINICAL SIGNIFICANCE: No single irrigant has 100% efficiency. Thus by this study, a best irrigating solution with maximum properties can be established.

Spectral graph model for fMRI: a biophysical, connectivity-based generative model for the analysis of frequency-resolved resting state fMRI.

Resting state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brain's functional organization and to examine if it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being under-utilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysical signal generation model followed by graph spectral (i.e. eigen) decomposition. We call this model a Spectral Graph Model (SGM) for fMRI, using which we can not only quantify the structure-function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signal's spectral and spatial features into a small number of biophysically-interpretable parameters. We expect this model-based inference of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure-function relations in a variety of brain disorders.

Reporting Pancreatic FNAC using the Papanicolaou System: Still a Diagnostic Challenge.

Introduction: The Papanicolaou Society of Cytopathology System for reporting Pancreaticobiliary Cytology (PSCPC) is a reliable method to classify pancreatic fine needle aspiration cytology (FNAC) smears. However, it is not without practical problems which can diminish the diagnostic accuracy of the cytological diagnosis. Aims and Objectives: To determine the diagnostic pitfalls while reporting cytomorphology of pancreatic lesions according to PSCPC on correlating FNAC findings with histopathology. Materials and Methods: Retrospective analysis of pancreatic FNAC smears received in the Department of Pathology of our tertiary care institute over a period of 2 years was done. The cytological diagnoses were classified according to the Papanicolaou Society of Cytopathology system of reporting pancreaticobiliary cytology and correlated with histopathology. The reasons of cyto-histological discordance were analyzed. Results: Out of 50 cases in which both FNAC and biopsy of pancreatic lesions were done, 34 cases were positive/malignant (Category VI), eight cases were suspicious for malignancy (Category V), three cases were neoplastic (Category IV), two cases were atypical (Category III), two cases were negative for malignancy (Category II), and one case was non-diagnostic (Category I). Out of 50 cases, histopathology was non-diagnostic due to inadequate material in six cases. The cytological diagnoses were compared with histopathology in the remaining 44 cases. Categories III, IV V, and VI were considered as positive for neoplastic pathology. The sensitivity of FNAC to predict neoplastic pathology was 97.5%, while the specificity was 25%. The positive predictive value was 92.9%. Two cases reported as atypical (Category III) turned out to be adenocarcinoma on histopathology. One case reported as neuroendocrine tumor and two cases reported as adenocarcinoma on cytology displayed features of chronic pancreatitis on histology. One case reported as neoplastic mucinous cyst (Category IV) turned out to be adenocarcinoma on histology (limited concordance). Conclusion: The cytopathologist needs to be wary of the potential pitfalls to improve the diagnostic accuracy of FNACs.

Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM). METHODS: SGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years). RESULTS: Patients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition. CONCLUSIONS: These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

Parthenium dermatitis.

Allergic contact dermatitis (ACD) to Parthenium hysterophorus is the most common cause of plant dermatitis in India. Parthenium dermatitis is caused by dry powder of leaves and flowers and hair-like structures (trichomes). Sesquiterpene lactones (SQLs) are the most important allergens responsible for ACD to parthenium. The different patterns include classical airborne contact dermatitis, chronic actinic dermatitis (CAD), exfoliative and widespread dermatitis. There is a definite trend towards a change from an airborne pattern to a CAD pattern in the natural history of parthenium dermatitis. In CAD, there is a reported increased sensitivity to UVB, UVA and even visible light. However, SQLs including parthenin, the major allergen in the Parthenium hysterophorus, has neither documented photoallergic nor phototoxic properties. Recently, the high photoreactivity of α-methylene-γ-butyrolactone ring toward thymidine and resulting photoadducts has been proposed as an explanation of the progressive evolution of allergic contact dermatitis toward chronic actinic dermatitis. However, more data is required to reach a conclusion on the mechanism of photosensitivity in parthenium dermatitis. Sunlight, especially UV radiation, may have a role in increasing the germination capacity and the amount of allergens in the Compositae family, especially in parthenium plants under appropriate conditions like summer and spring, which may contribute to high prevalence of parthenium dermatitis especially in northern India.

Stability and dynamics of a spectral graph model of brain oscillations.

We explore the stability and dynamic properties of a hierarchical, linearized, and analytic spectral graph model for neural oscillations that integrates the structural wiring of the brain. Previously, we have shown that this model can accurately capture the frequency spectra and the spatial patterns of the alpha and beta frequency bands obtained from magnetoencephalography recordings without regionally varying parameters. Here, we show that this macroscopic model based on long-range excitatory connections exhibits dynamic oscillations with a frequency in the alpha band even without any oscillations implemented at the mesoscopic level. We show that depending on the parameters, the model can exhibit combinations of damped oscillations, limit cycles, or unstable oscillations. We determined bounds on model parameters that ensure stability of the oscillations simulated by the model. Finally, we estimated time-varying model parameters to capture the temporal fluctuations in magnetoencephalography activity. We show that a dynamic spectral graph modeling framework with a parsimonious set of biophysically interpretable model parameters can thereby be employed to capture oscillatory fluctuations observed in electrophysiological data in various brain states and diseases.

Connectome-based biophysics models of Alzheimer's disease diagnosis and prognosis.

With the increasing prevalence of Alzheimer's disease (AD) among aging populations and the limited therapeutic options available to slow or reverse its progression, the need has never been greater for improved diagnostic tools for identifying patients in the preclinical and prodomal phases of AD. Biophysics models of the connectome-based spread of amyloid-beta (Aß) and microtubule-associated protein tau (τ) have enjoyed recent success as tools for predicting the time course of AD-related pathological changes. However, given the complex etiology of AD, which involves not only connectome-based spread of protein pathology but also the interactions of many molecular and cellular players over multiple spatiotemporal scales, more robust, complete biophysics models are needed to better understand AD pathophysiology and ultimately provide accurate patient-specific diagnoses and prognoses. Here we discuss several areas of active research in AD whose insights can be used to enhance the mathematical modeling of AD pathology as well as recent attempts at developing improved connectome-based biophysics models. These efforts toward a comprehensive yet parsimonious mathematical description of AD hold great promise for improving both the diagnosis of patients at risk for AD and our mechanistic understanding of how AD progresses.

An overview of mycetoma and its diagnostic dilemma: Time to move on to advanced techniques.

The neglected tropical disease mycetoma can become extremely devastating, and can be caused both by fungi and bacteria; these are popularly known as eumycetoma and actinomycetoma respectively. The classical triad of the disease is subcutaneous swelling, multiple discharging sinuses and the presence of macroscopic granules. The present study aims to highlight the existing diagnostic modalities and the need to incorporate newer and more advanced laboratory techniques like pan fungal/pan bacterial 16S rRNA gene polymerase chain reaction (PCR) and sequencing, Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), rolling circle amplification (RCA), loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA). It is important for the medical team to be aware of the various diagnostic options (both existing and future), so that diagnosis of such a debilitating disease is never missed, both by clinicians and microbiologists/pathologists. The newer diagnostic methods discussed in this article will help in rapid, accurate diagnosis thus facilitating early treatment initiation, and decreasing the overall morbidity of the disease. In the Indian context, newer technologies need to be made available more widely. Making clinicians aware and promoting research and development in mycetoma diagnostics is the need of the hour.

Bayesian Inference of a Spectral Graph Model for Brain Oscillations.

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which can not be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

Biomimetic Approach toward Visible Light-Driven Hydrogen Generation Based on a Porphyrin-Based Coordination Polymer Gel.

There has been a widespread interest in developing self-assembled porphyrin nanostructures to mimic nature's light-harvesting processes. Herein, porphyrin-based coordination polymer gel (CPG) has been developed as a "soft" photocatalyst material for hydrogen (H2) production from water under visible light. The CPG offers a hierarchical nanofibrous network structure obtained through self-assembly of a terpyridine alkyl-amide appended porphyrin (TPY-POR)-based low molecular weight gelator with ruthenium ions (RuII) and produces H2 with a rate of 5.7 mmol g-1 h-1 in the presence of triethylamine (TEA) as a sacrificial electron donor. Further, the [Fe2(bdt)(CO)6] (dbt = 1,2-benzenedithiol) cocatalyst, which can mimic the activity of iron hydrogenase, is coassembled in the CPG and shows remarkable improvement in H2 evolution (catalytic activity; rate ∼10.6 mmol g-1 h-1 and turnover number ∼1287). The significant enhancement in catalytic activity was supported by several controlled experiments, including femtosecond transient absorption (TA) spectroscopy and also DFT calculation. The TA study supported the cascade electron transfer process from porphyrin core to [Ru(TPY)2]2+ center, and subsequently, the electron transfers to the cocatalyst [Fe2(bdt)(CO)6] for H2 production.