Domain-General and Domain-Specific Electrophysiological Markers of Cognitive Distance Coding for What, Where, and When Memory Retrieval.

The what, where, and when components of episodic memory can be differentiated based on their distinctive domain-specific underlying neural correlates. However, recent studies have proposed that a common neural mechanism of conceptual mapping may be involved in the coding of cognitive distance across all domains. In this study, we provide evidence that both domain-specific and domain-general processes occur simultaneously during memory retrieval by identifying distinctive and common neural representations for mapping what (i.e., semantic distance), where (i.e., spatial distance), and when (i.e., temporal distance) using scalp EEG from 47 healthy participants (age 21-30, 26 male and 21 female). First, we found that all three components commonly showed a positive correlation between cognitive distance and slow theta power (2.5-5 Hz) in parietal channels. Meanwhile, fast theta power (5-8.5 Hz) specifically represented spatial and temporal distance in occipital and parietal channels, respectively. Additionally, we identified a unique correlate of temporal distance coding in frontal/parietal slow theta power during the early phase of retrieval. All of the above neural markers of cognitive mapping, both domain-general and specific, were associated with individual differences in what, where, and when memory accuracy.SIGNIFICANCE STATEMENT The Cognitive Map Theory was originally founded to explain how we remember and organize the immense amount of spatial information that we face when we navigate. However, memory research has recently trended in the direction of emphasizing the generalizability of cognitive mapping mechanisms to information in any domain, represented as distances in an abstract conceptual space. In a single study, we show that both common and unique neural coding of semantic distance (i.e., what), spatial distance (i.e., where), and temporal distance (i.e., when) simultaneously support episodic memory retrieval. Our results suggest that our ability to accurately distinguish between memories is achieved through an integration of domain-specific and domain-general neurocognitive mechanisms that work in parallel.

Cognotyping by What-Where-When Retrieval Reveals the Potential Role of Temporal Memory and Its Neural Correlates in Understanding Individual Differences across Aging and Alzheimer Disease.

Despite distinct neural representation of what, where, and when information, studies of individual differences in episodic memory have neglected to test the three components separately. Here, we used a componential episodic memory task to measure cognitive profiles across a wide age range and in Alzheimer disease (AD) and to examine the role of theta oscillations in explaining performance. In Experiment 1, we tested a group of 47 young adults (age 21-30 years, 21 women) while recording their scalp EEG. A separate behavioral experiment (Experiment 2) was performed in 42 older adults (age 66-85 years, 29 women) and in a group of 16 AD patients (age 80-90 years, 12 women). In Experiment 1, K-means clustering based on behavioral data resulted in three "cognotypes" whose memory profiles showed corresponding differences in their EEG markers: What and where memory depended on frontal theta power and when memory depended on theta modulation by temporal distance between retrieved items. In Experiment 2, healthy older adults showed three cognotypes similar to those found in younger adults; moreover, AD patients had an overlapping profile with one specific cognotype, characterized by marked difficulties in when memory. These findings highlight the utility of componential episodic memory tests and cognotyping in understanding individual strengths and vulnerabilities in age-related neurocognitive decline.

Industrial carbon dioxide capture and utilization: state of the art and future challenges.

Dramatically increased CO2 concentration from several point sources is perceived to cause severe greenhouse effect towards the serious ongoing global warming with associated climate destabilization, inducing undesirable natural calamities, melting of glaciers, and extreme weather patterns. CO2 capture and utilization (CCU) has received tremendous attention due to its significant role in intensifying global warming. Considering the lack of a timely review on the state-of-the-art progress of promising CCU techniques, developing an appropriate and prompt summary of such advanced techniques with a comprehensive understanding is necessary. Thus, it is imperative to provide a timely review, given the fast growth of sophisticated CO2 capture and utilization materials and their implementation. In this work, we critically summarized and comprehensively reviewed the characteristics and performance of both liquid and solid CO2 adsorbents with possible schemes for the improvement of their CO2 capture ability and advances in CO2 utilization. Their industrial applications in pre- and post-combustion CO2 capture as well as utilization were systematically discussed and compared. With our great effort, this review would be of significant importance for academic researchers for obtaining an overall understanding of the current developments and future trends of CCU. This work is bound to benefit researchers in fields relating to CCU and facilitate the progress of significant breakthroughs in both fundamental research and commercial applications to deliver perspective views for future scientific and industrial advances in CCU.

Novelty of Dynamic Process in the Synthesis of Biocompatible Silica Nanotubes by Biomimetic Glycyldodecylamide as a Soft Template.

A dynamic process in the synthesis of silica nanotubes (SNTs) by utilizing glycyldodecylamide (GDA) as a soft template was thoroughly investigated. The morphological evolution from GDA to SNTs was deeply explored to elucidate the formation mechanism for optimizing the synthesis procedure. Various analytical tools, namely, XRD, FTIR, SEM, TEM, Z-potential, and N2 adsorption/desorption isotherms, were employed during the synthesis procedure. The interactive structure of GDA was also investigated using TEM-EDX as a function of aging time. These studies revealed the stepwise morphology of nanograin, nanofiber, curved plate, and nanotube in the ethanol/water solution when aged at room temperature. The supramolecular GDA molded the vesicle type nanostructure which was surrounded by silica and facilitated the formation of uniform SNTs. The stimulus for GDA to be curved into a vesicle was the intermolecular hydrogen bonding between adjacent amide groups of the template molecules. This was illustrated by FTIR spectra of GDA-silica intermediate by detecting the transition of amide I peak from 1678 to 1635 cm-1. The effect of hydrogen bonding became stronger when the sample was aged.

Digital assessment of cognitive-affective biases related to mental health.

With an increasing societal need for digital therapy solutions for poor mental health, we face a corresponding rise in demand for scientifically validated digital contents. In this study we aimed to lay a sound scientific foundation for the development of brain-based digital therapeutics to assess and monitor cognitive effects of social and emotional bias across diverse populations and age-ranges. First, we developed three computerized cognitive tasks using animated graphics: 1) an emotional flanker task designed to test attentional bias, 2) an emotional go-no-go task to measure bias in memory and executive function, and 3) an emotional social evaluation task to measure sensitivity to social judgments. Then, we confirmed the generalizability of our results in a wide range of samples (children (N = 50), young adults (N = 172), older adults (N = 39), online young adults (N=93), and depression patients (N = 41)) using touchscreen and online computer-based tasks, and devised a spontaneous thought generation task that was strongly associated with, and therefore could potentially serve as an alternative to, self-report scales. Using PCA, we extracted five components that represented different aspects of cognitive-affective function (emotional bias, emotional sensitivity, general accuracy, and general/social attention). Next, a gamified version of the above tasks was developed to test the feasibility of digital cognitive training over a 2-week period. A pilot training study utilizing this application showed decreases in emotional bias in the training group (that were not observed in the control group), which was correlated with a reduction in anxiety symptoms. Using a 2-channel wearable EEG system, we found that frontal alpha and gamma power were associated with both emotional bias and its reduction across the 2-week training period.

Encapsulation of hexahydro-1,3,5-trinitro-s-triazine (RDX) onto amino-functionalized mesoporous silica.

Amine functionalized disk type mesoporous silica was directly synthesized by co-condensation method for the encapsulation of hexahydro-1,3,5-trinitro-s-triazine (RDX), aiming for the desensitization of such high energetic material. The adsorption capacities were measured by TG analysis and pore-filling adsorption efficiencies of RDX were estimated based on sorption amounts with respect to pore volume of amino-functionalized mesoporous silica. The RDX was encapsulated as nanoparticles in the (NH2)-INC-2 due to the confinement effect within the size of mesopores and shows adsorption efficiency upto 80% at with respect to pore-filling. The confinement effect is also depicted by lowering of the T(rho) (190 degrees C) when encapsulated compared with that of free RDX. The non-neutralized amino-functionalized (cationic (NH3+)-INC-2) gave more 80% adsorption efficiency at 25% loading and the T(rho) were comparatively higher due to the ionic interaction between RDX and quaternary amine group.

Tetraethylenepentamine embedded zeolite A for carbon dioxide adsorption.

Tetraethylenepentamine (TEPA) embedded zeolite A crystals were synthesized by using TEPA and the preformed zeolite A precursor under the microwave irradiation. The presence of TEPA in zeolite A crystal was confirmed by TG analysis and FTIR, Raman spectra. The CO2 adsorptive behavior of TEPA embedded zeolite A samples was investigated by CO2 isotherms measured at 25 degrees C comparing with zeolite A. The optimum CO2 sorption capacity was found in the case of 7.5% TEPA embedded zeolite A, which showed 3.75 mmol g(-1) where as the zeolite A showed less CO2 adsorption capacity of 2.88 mmol g(-1). The adsorption capacity of TEPA embedded Zeolite A was sustained up to 90% during 4 cycles of temperature swing adsorption (TSA) from 40 degrees C to 140 degrees C, indicating that the TEPA embedded Zeolite A was found to be useful as one of the application to solid amine adsorbent for CO2.

Strong Anionic Repulsion for Fast Na Kinetics in P2-Type Layered Oxides.

An intriguing mechanism for enabling fast Na kinetics during oxygen redox (OR) is proposed to produce high-power-density cathodes for sodium-ion batteries (SIBs) based on the P2-type oxide models, Na2/3 [Mn6/9 Ni3/9 ]O2 (NMNO) and Na2/3 [Ti1/9 Mn5/9 Ni3/9 ]O2 (NTMNO) using the "potential pillar" effect. The critical structural parameter of NTMNO lowers the Na migration barrier in the desodiated state because the electrostatic repulsion of O(2p)O(2p) that occurs between transition metal layers is combined with the chemically stiff Ti4+ (3d)O(2p) bond to locally retain the strong repulsion effect. The NTMNO interlayer distance moderately decreases upon charging with oxygen oxidation, whereas that of NMNO decreases at a much faster rate, which can be explained by the dependence of OR activity on the coordination environment. Fundamental electrochemical experiments clearly indicate that the Ti doping of the bare material significantly improves its rate capability during OR, and detailed electrochemical and structural analyses show much faster Na kinetics for NTMNO than for NMNO. A systematic comparison of the two cathode oxides based on experiments and first-principles calculations establishes the "potential pillar" concept of not only improving the sluggish Na kinetics upon OR reaction but also harnessing the full potential of the anionic redox for high-power-density SIBs.

Determining Factors in Triggering Hysteretic Oxygen Capacities in Lithium-Excess Sodium Layered Oxides.

Oxygen redox (OR) reactions in sodium layered oxide cathodes have been studied intensively to harness their full potential in achieving high energy density for sodium-ion batteries (SIBs). However, OR triggers a large hysteretic voltage during discharge after the first charge process for OR-based oxides, and its intrinsic origin is unclear. Therefore, in this study, an in-depth reinvestigation on the fundamentals of the reaction mechanism in Na[Li1/3Mn2/3]O2 with a Mn/Li ratio (R) of 2 was performed to determine the factors that polarize the OR activity and to provide design rules leading to nonhysteretic oxygen capacity using first-principles calculations. Based on thermodynamic energies, the O2-/O22- and O2-/On- conditions reveal the monophasic (0.0 ≤ x ≤ 4/6) and biphasic (4/6 ≤ x ≤ 1.0) reactions in Na1-x[Li2/6Mn4/6]O2, but each stability at x = 5/6 is observed differently. The O-O bond population elucidates that the formation of an interlayer O-O dimer is a critical factor in triggering hysteretic oxygen capacity, whereas that in a mixed layer provides nonhysteretic oxygen capacity after the first charge. In addition, the migration of Li into the 4h site in the Na metallic layer contributes less to the occurrence of voltage hysteresis because of the suppression of the interlayer O-O dimer. These results are clearly elucidated using the combined-phase mixing enthalpies and chemical potentials during the biphasic reaction. To compare the Mn oxide with R = 2, Na1-x[Li1/6Mn5/6]O2 tuned with R = 5 was investigated using the same procedure, and all the impeding factors in restraining the nonhysteretic OR were not observed. Herein, we suggest two strategies based on three types of OR models: (i) exploiting the migration of Li ions for the suppression of the interlayer O-O dimer and (ii) modulating the Mn/Li ratio for controlling the OR participation, which provides an exciting direction for nonhysteretic oxygen capacities for SIBs and lithium-ion batteries.

Dynamic adsorption/desorption of p-xylene on nanomorphic MFI zeolites: Effect of zeolite crystal thickness and mesopore architecture.

Zeolites have attracted great interest as an adsorbent for the removal of volatile organic compounds. However, they suffer from low adsorption capacities due to severe diffusion limitations. Here, the effects of zeolite thickness and mesopore architecture on dynamic adsorption of p-xylene have been examined with a number of MFI-type zeolites with different crystal thicknesses and mesopore openings (i.e. open mesopore, constricted mesopore), which were prepared via hydrothermal synthesis with various organic structure-directing agents and post-synthetic desilication. The results showed that the breakthrough time of MFI zeolite could be improved by more than 2.3 times by reducing the crystal thickness of zeolite to a single-unit-cell dimension (∼2 nm). The time improvement can be attributed to the short diffusion path length that results in easy access of p-xylene to intracrystalline micropores and a large external crystal surface area. In the case of mesopore openings, the presence of constricted mesopores caused the mass transfer of p-xylene into zeolite adsorbents to slow down while open mesopores did not. Furthermore, mesopore opening is an important factor for the desorption behavior of p-xylene. Adsorbed p-xylene by mesoporous zeolites could be desorbed at lower temperatures only when facile diffusion to the exterior through mesoporous channels was possible.

Intrinsic Origin of Nonhysteretic Oxygen Capacity in Conventional Na-Excess Layered Oxides.

An intriguing redox chemistry via oxygen has emerged to achieve high-energy-density cathodes and has been intensively studied for practical use of anion-utilization oxides in A-ion batteries (A: Li or Na). However, in general, the oxygen redox disappears in the subsequent discharge with a large voltage hysteresis after the first charge process for A-excess layered oxides exhibiting anion redox. Unlike these hysteretic oxygen redox cathodes, the two Na-excess oxide models Na2IrO3 and Na2RuO3 unambiguously exhibit nonhysteretic oxygen capacities during the first cycle, with honeycomb-ordered superstructures. In this regard, the reaction mechanism in the two cathode models is elucidated to determine the origin of nonhysteretic oxygen capacities using first-principles calculations. First, the vacancy formation energies show that the thermodynamic instability in Na2IrO3 increases at a lower rate than that in Na2RuO3 upon charging. Second, considering that the strains of Ir-O and Ru-O bonding lengths are softened after the single-cation redox of Ru4+/Ru5+ and Ir4+/Ir5+, the contribution in the oxygen redox from x = 0.5 to 0.75 is larger in Na1-xRu0.5O1.5 than that in Na1-xIr0.5O1.5. Third, the charge variations indicate a dominant cation redox activity via Ir(5d)-O(2p) for x above 0.5 in Na1-xIr0.5O1.5. Its redox participation occurred with the oxygen redox, opposite to the behavior in Na1-xRu0.5O1.5. These three considerations imply that the chemical weakness of Ir(5d)-O(2p) leads to a more redox-active environment of Ir ions and reduces the oxygen redox activity, which triggers the nonhysteretic oxygen capacity during (de)intercalation. This provides a comprehensive guideline for design of reversible oxygen redox capacities in oxide cathodes for advanced A-ion batteries.

A framework for designing data-driven optimization systems for neural modulation.

Objective.Neural modulation is a fundamental tool for understanding and treating neurological and psychiatric diseases. However, due to the high-dimensional space, subject-specific responses, and variability within each subject, it is a major challenge to select the stimulation parameters that have the desired effect. Data-driven optimization provides a range of different algorithms and tools for addressing this challenge, but each of these algorithms has specific strengths and limitations, and therefore must be carefully designed for a given neural modulation problem. Here we present a framework for designing data-driven optimization algorithms for neural modulation.Approach.We develop this framework using an optogenetic medial septum stimulation model, where the goal is to find the stimulation parameters that modulate hippocampal gamma power to a desired value. This framework proceeds in four steps: (a) collecting stimulation data, (b) creating high-throughput simulation models, (c) prototyping a range of different data-driven optimization algorithms and evaluating their performance, and (d) deploying the best performing algorithmin vivo. Main results.Following this framework, we prototype and design an algorithm specifically for finding the medial septum optogenetic stimulation parameters that maximize hippocampal gamma power. Building on this, we then change our objective function to find the stimulation parameters that modulate gamma to a specific setpoint, use the framework to understand and anticipate the results before deployingin vivo. Significance.We show that this framework can be used to design an effective optimization solution for a specific neural modulation problem, and discuss how it can potentially be applied beyond the optogenetic medial septum stimulation model.

MgO encapsulated mesoporous zeolite for the side chain alkylation of toluene with methanol.

Side chain alkylation of toluene with methanol was studied over mesoporous zeolite supported MgO catalysts. MgO were supported onto the carbon templated mesoporous silicalite-1 by direct synthesis route under microwave conditions. This direct synthesis route yields the majority of MgO highly dispersed into the mesopores of the silicalite-1 crystals. The vapor phase alkylation of toluene with methanol was performed over these catalysts under vapor phase conditions at atmospheric pressure. Mesoporous silicalite-1 supported MgO catalysts gave improved yields towards side chain alkylated products compared to the bulk MgO. The higher activity exhibited by 5% MgO supported on mesoporous silicalite compared to the one with 1% MgO can be attributed to the large number of weak basic sites observed from the CO2 TPD.

Optimizing neuromodulation based on surrogate neural states for seizure suppression in a rat temporal lobe epilepsy model.

OBJECTIVE: Developing a new neuromodulation method for epilepsy treatment requires a large amount of time and resources to find effective stimulation parameters and often fails due to inter-subject variability in stimulation effect. As an alternative, we present a novel data-driven surrogate approach which can optimize the neuromodulation efficiently by investigating the stimulation effect on surrogate neural states. APPROACH: Medial septum (MS) optogenetic stimulation was applied for modulating electrophysiological activities of the hippocampus in a rat temporal lobe epilepsy model. For the new approach, we implemented machine learning techniques to describe the pathological neural states and to optimize the stimulation parameters. Specifically, first, we found neural state surrogates to estimate a seizure susceptibility based on hippocampal local field potentials. Second, we modulated the neural state surrogates in a desired way with the subject-specific optimal stimulation parameters found by in vivo Bayesian optimization. Finally, we tested whether modulating the neural state surrogates affected seizure frequency. MAIN RESULTS: We found two neural state surrogates: The first was hippocampal theta power by considering its well-known relationship with epilepsy, and the second was the output of pre-ictal state model (PriSM) which was built by characterizing the hippocampal activity during the pre-ictal period. The optimal stimulation parameters found by Bayesian optimization outperformed the other parameters in terms of modulating the surrogates toward anti-seizure neural state. When treatment efficacy was tested, the subject-specific optimal parameters for increasing theta power were more effective to suppress seizures than fixed stimulation parameter (7 Hz). However, modulation of the other neural state surrogate, PriSM, did not suppress seizures. SIGNIFICANCE: The surrogate approach can save enormous time and resources to find subject-specific optimal stimulation parameters which can effectively modulate neural states and further improve therapeutic effectiveness. This approach can also be used for improving neuromodulation treatment of other neurological or psychiatric diseases.

A characterization of epileptogenesis presented in hippocampal neural activity in a rat tetanus toxin model.

We built a regression model to describe the progress of epileptogenesis in a rat intrahippocampal tetanus toxin (TeNT) epilepsy model by identifying informative neural features from hippocampal local field potentials (LFPs). The LFPs were recorded from the awake and freely behaving animals during the latent period and the active-seizure period. Frequency domain neural features including power spectral density, coherence and phase coherence were calculated from the hippocampal LFPs. A least angle regression with elastic net regularization (LARS-ENR) model successfully predicted a relative day from the first seizure in multiple rats (R2test = 0.724±0.025). By leveraging a characteristic of LARS-ENR which reduces unnecessary features, we identified the neural features related to epileptogenesis in a TeNT model.

Learning State-Dependent Neural Modulation Policies with Bayesian Optimization.

Neural modulation is becoming a fundamental tool for understanding and treating neurological diseases and their implicated neural circuits. Given that neural modulation interventions have high dimensional parameter spaces, one of the challenges is selecting the stimulation parameters that induce the desired effect. Moreover, the effect of a given set of stimulation parameters may change depending on the underlying neural state. In this study, we investigate and address the state-dependent effect of medial septum optogenetic stimulation on the hippocampus. We found that pre-stimulation hippocampal gamma (33-50Hz) power influences the effect of medial septum optogenetic stimulation on during-stimulation hippocampal gamma power. We then construct a simulation platform that models this phenomenon for testing optimization approaches. We then compare the performance of a standard implementation of Bayesian optimization, along with an extension to the algorithm that incorporates pre-stimulation state to learn a state-dependent policy. The state-dependent algorithm outperformed the standard approach, suggesting that incorporating pre-stimulation can improve neural modulation interventions.

A Machine Learning Approach to Characterize the Modulation of the Hippocampal Rhythms Via Optogenetic Stimulation of the Medial Septum.

The medial septum (MS) is a potential target for modulating hippocampal activity. However, given the multiple cell types involved, the changes in hippocampal neural activity induced by MS stimulation have not yet been fully characterized. We combined MS optogenetic stimulation with local field potential (LFP) recordings from the hippocampus and leveraged machine learning techniques to explore how activating or inhibiting multiple MS neuronal subpopulations using different optical stimulation parameters affects hippocampal LFP biomarkers. First, of the seven different optogenetic viral vectors used for modulating different neuronal subpopulations, only two induced a substantial change in hippocampal LFP. Second, we found hippocampal low-gamma band to be most effectively modulated by the stimulation. Third, the hippocampal biomarkers were sensitive to the optogenetic virus type and the stimulation frequency, establishing those parameters as the critical ones for the regulation of hippocampal biomarker activity. Last, we built a Gaussian process regression model to describe the relationship between stimulation parameters and activity of the biomarker as well as to identify the optimal parameters for biomarker modulation. This new machine learning approach can further our understanding of the effects of neural stimulation and guide the selection of optimal parameters for neural control.

Chiral enhancement in diethyl malonate addition by morphosynthesized L-proline mesoporous silica.

l-Proline was immobilized onto mesoporous silica through direct synthesis method via morphosynthesis possessing short channels and plugs in the pore structure which provided chiral enhancement in the diethyl malonate addition reaction.

A machine learning approach to characterizing the effect of asynchronous distributed electrical stimulation on hippocampal neural dynamics in vivo.

Asynchronous distributed microelectrode theta stimulation (ADMETS) of the hippocampus has been shown to reduce seizure frequency in the tetanus toxin rat model of mesial temporal lobe epilepsy suggesting a hypothesis that ADMETS induces a seizure resistant state. Here we present a machine learning approach to characterize the nature of neural state changes induced by distributed stimulation. We applied the stimulation to two animals under sham and ADMETS conditions and used a combination of machine learning techniques on intra-hippocampal recordings of Local Field Potentials (LFPs) to characterize the difference in the neural state between sham and ADMETS. By iteratively fitting a logistic regression with data from the inter-stimulation interval under sham and ADMETS condition we found that the classification performance improves for both animals until 90s post stimulation before leveling out at AUC of 0.64 ± 0.2 and 0.67 ± 0.02 when all inter-stimulation data is included. The models for each animal were re-fit using elastic net regularization to force many of the model coefficients to 0, identifying those that do not optimally contribute to the classifier performance. We found that there is significant variation in the non-zero coefficients between animals (p <; 0.01), suggesting that the ADMETS induced state is represented differently between subject. These findings lay the foundation for using machine learning to robustly and quantitatively characterize neural state.

Amino-functionalized SBA-15 type mesoporous silica having nanostructured hexagonal platelet morphology.

Amino-functionalized SBA-15 type mesoporous silicas having unique hexagonal platelet morphologies with short channels (100-300 nm) running parallel to the thickness of the nanostructured hexagonal platelet type morphologies have been directly synthesized by co-condensation of aminopropyltriethoxysilane (APTES) and sodium metasilicate as a silica source in the presence of Pluronic P123 triblock copolymer as a structure directing agent.