The day-night differences in cognitive and anxiety-like behaviors of mice after chronic sleep restriction.

Animal behavioral tests are often conducted during the day. However, rodents are nocturnal animals and are primarily active at night. The aim of this study was to determine whether there are diurnal changes in cognitive and anxiety-like performance of mice following chronic sleep restriction (SR). We also investigated whether this phenotypic difference is related to the diurnal variation of glymphatic clearance of metabolic wastes. Mice received 9-day SR by the use of the modified rotating rod method, followed by the open field, elevated plus maze, and Y-maze tests conducted during the day and at night, respectively. Brain ß-amyloid (Aß) and tau protein levels, the polarity of aquaporin4 (AQP4), a functional marker of the glymphatic system, and glymphatic transport ability were also analyzed. SR mice exhibited cognitive impairment and anxiety-like behaviors during the day, but not at night. AQP4 polarity and glymphatic transport ability were higher during the day, with lower Aß1-42 , Aß1-40 , and P-Tau levels in the frontal cortex. These day-night differences were totally disrupted after SR. These results reveal the diurnal changes in behavioral performance after chronic SR, which may be related to circadian control of AQP4-mediated glymphatic clearance of toxic macromolecules from the brain.

Atroposelective Synthesis of Heterobiaryls through Ring Formation.

Atropisomeric heterobiaryls play a vital role in natural products, chiral ligands, organocatalysts, and other research fields, which have aroused great interest from chemists in recent years. Until now, a growing number of optically active heterobiaryls based on indole, quinoline, isoquinoline, pyridine, pyrrole, azole, and benzofuran skeletons have been successfully synthesized through metal or organic catalytic cross-coupling, functionalization of prochiral or racemic heterobiaryls, and ring formation. Among different strategies for the atroposelective synthesis of heterobiaryls, the strategy of ring formation has become a vital tool toward this goal. In this review, we summarize the enantioselective synthesis of axially chiral heterobiaryls through ring formation approaches, such as cycloaddition, cyclization, and chirality conversion. Meanwhile, the reaction mechanism and the corresponding applications of the chiral heterobiaryls are also discussed.

Efficient Exploration of Chemical Compound Space Using Active Learning for Prediction of Thermodynamic Properties of Alkane Molecules.

We introduce an exploratory active learning (AL) algorithm using Gaussian process regression and marginalized graph kernel (GPR-MGK) to sample chemical compound space (CCS) at minimal cost. Targeting 251,728 enumerated alkane molecules with 4-19 carbon atoms, we applied the AL algorithm to select a diverse and representative set of molecules and then conducted high-throughput molecular simulations on these selected molecules. To demonstrate the power of the AL algorithm, we built directed message-passing neural networks (D-MPNN) using simulation data as the training set to predict liquid densities, heat capacities, and vaporization enthalpies of the CCS. Validations show that D-MPNN models built on the smallest training set considered in this work, which consists of 313 molecules or 0.124% of the original CCS, predict the properties with R2 > 0.99 against the computational data and R2 > 0.94 against the experimental data. The advantage of the presented AL algorithm is that the predicted uncertainty of GPR depends on only the molecular structures, which renders it compatible with high-throughput data generation.

Potential Energy Surfaces Sampled in Cremer-Pople Coordinates and Represented by Common Force Field Functionals for Small Cyclic Molecules.

The complex conformations of the cyclic moieties impact the physical and chemical properties of molecules. In this work, we chose 22 molecules of four-, five-, and six-membered rings and performed a thorough conformational sampling using Cremer-Pople coordinates. With consideration of symmetries, we obtained a total of 1504 conformational structures for four-membered, 5576 for five-membered, and 13509 for six-membered rings. All well-known and many less well-known conformers for each molecule were identified. We represented the potential energy surfaces (PESs) by fitting the data to common analytical force field (FF) functional forms. We found that the general features of PESs can be described by the essential FF functional forms; however, the accuracy of representation can be improved remarkably by including the torsion-bond and torsion-angle coupling terms. The best fit yields R-squared (R2) values close to 1.0 and mean absolute errors in energy less than 0.3 kcal/mol.

Modular Construction of Heterobiaryl Atropisomers and Axially Chiral Styrenes via All-Carbon Tetrasubstituted VQMs.

Here we report a new type of chiral all-carbon tetrasubstituted VQMs generated via chiral phosphoric acids catalyzed nucleophilic addition of 2-alkynylnaphthols to o-quinone methides or imines, which can be captured intramolecularly as a result of cycloaddition reaction. A new class of naphthyl-2H-chromenes bearing axially and centrally chiral elements and axially chiral quinone-naphthols were prepared efficiently with good to excellent yields, diastereoselectivities and enantioselectivities. Noteworthy, the enantioselective cycloaddition of alkynylnaphthols with o-quinone methides proceeded via a [2+2] cycloaddition, followed by a retro-4π-electrocyclization and a 6π re-cyclization. While the cycloaddition of alkynylnaphthols with imines proceeded via a sequential [2+4] cycloaddition and an auto oxidation reaction. Moreover, the obtained axially chiral naphthols can be converted into valuable phosphine ligands and other functional molecules.

Scandium Triflate Catalyzed Tandem Transfer Hydrogenation and Cyclization Reaction of o-Aminobenzaldehydes and o-Aminoacetophenone with Alcohols.

An effective Sc-catalyzed transfer hydrogenation and cyclization tandem reaction has been achieved. This process showed excellent functional group compatibility and good yields. A variety of benzoxazines were produced with primary or secondary alcohols as a hydrogen source. Furthermore, the utility of this newly developed protocol is demonstrated through scaled-up experiment, late-stage modification, and preliminary exploration of enantioselective synthesis.

A Comparative Study of Marginalized Graph Kernel and Message-Passing Neural Network.

This work proposes a state-of-the-art hybrid kernel to calculate molecular similarity. Combined with Gaussian process models, the performance of the hybrid kernel in predicting molecular properties is comparable to that of the directed message-passing neural network (D-MPNN). The hybrid kernel consists of a marginalized graph kernel (MGK) and a radial basis function (RBF) kernel that operate on molecular graphs and global molecular features, respectively. Bayesian optimization was used to obtain the optimal hyperparameters for both models. The comparisons are performed on 11 publicly available data sets. Our results show that their performances are similar, their prediction errors are correlated, and the ensemble predictions of the two models perform better than either of them. Through principal component analysis, we found that the molecular embeddings of the hybrid kernel and the D-MPNN are also similar. The advantage of D-MPNN lies in the computational efficiency and scalability of large-scale data, while the advantage of the graph kernel models lies in the accurate uncertainty quantification.

Pros and cons of the time-dependent hybrid density functional approach for calculating the optical spectra of solids: a case study of CeO2.

The prediction of optical spectra of complex solids remains a great challenge for first-principles calculations due to the huge computational cost of the state-of-the-art many-body perturbation theory based GW-Bethe Salpeter equation (BSE) approach. An alternative method is the time-dependent density-functional theory (TDDFT) based on hybrid exchange-correlation functionals, which involves the essential ingredients of electron-hole interactions in its formalism in contrast to its local/semi-local functional counterparts. In this work, we investigate the optical absorption spectra of ceria (CeO2), a prototypical lanthanide oxide with a 4f0 configuration, utilizing TDDFT based on four well-established hybrid functionals for ground state DFT calculations. All four functionals reproduce well the excitonic features of the experimental optical spectra, in spite of the significant differences in their band structures arising from different hybridization parameters (i.e. the fraction of the Hartree-Fock exchange and the screening parameter). It is demonstrated that the apparently weak dependence of the resulting optical spectra on the employed functionals is quite universal and applies to simple semiconductors such as Si and GaAs and insulator LiF as well. This study highlights the feasibility of TDDFT based on existing hybrids to describe optical spectra of solids, and also, points out the difficulty of obtaining accurate exciton binding energies using these hybrid functionals due to the strong functional dependence of quasi-particle band structures.

Predicting Single-Substance Phase Diagrams: A Kernel Approach on Graph Representations of Molecules.

This work presents a Gaussian process regression (GPR) model on top of a novel graph representation of chemical molecules that predicts thermodynamic properties of pure substances in single, double, and triple phases. A transferable molecular graph representation is proposed as the input for a marginalized graph kernel, which is the major component of the covariance function in our GPR models. Radial basis function kernels of temperature and pressure are also incorporated into the covariance function when necessary. We predicted three types of representative properties of pure substances in single, double, and triple phases, i.e., critical temperature, vapor-liquid equilibrium (VLE) density, and pressure-temperature density. The accuracy of the models is nearly identical to the precision of the experimental measurements. Moreover, the reliability of our predictions can be quantified on a per-sample basis using the posterior uncertainty of the GPR model. We compare our model against Morgan fingerprints and a graph neural network to further demonstrate the advantage of the proposed method.

[A Preliminary Study of Applying Geometric Deep Learning in Brain Morphometry for Diagnosis of Alzheimer's Disease].

OBJECTIVE: A predictive model of Alzheimer's disease (AD) was established based on brain surface meshes and geometric deep learning, and its performance was evaluated. METHODS: Seventy-six clinically diagnosed AD patients and 83 healthy older adults were enrolled and randomly assigned to the training set and the test set according to a 4-to-1 ratio. Brain surface mesh was constructed from 3-D T1-weighted high-resolution structural MR volumes of each participant. After applying a series of simplification to the surface meshes, the training set was fed into the geometric deep neural network for training. The performance of the prediction model was evaluated with the test set, and the evaluation metrics included accuracy, sensitivity and specificity. RESULTS: The prediction model trained on the right brain surface meshes with 6 000 faces achieved the best performance, with accuracy reaching 93.8%, sensitivity, 91.7%, and specificity, 94.1%. The evolution of the brain surface meshes during convolution and pooling revealed that AD patients had diffuse brain tissue loss compared with healthy older adults. CONCLUSION: Morphological brain analysis based on mesh data and geometric deep learning has great potential in the differential diagnosis of AD.

Extracting the mechanisms and kinetic models of complex reactions from atomistic simulation data.

Determining reaction mechanisms and kinetic models, which can be used for chemical reaction engineering and design, from atomistic simulation is highly challenging. In this study, we develop a novel methodology to solve this problem. Our approach has three components: (1) a procedure for precisely identifying chemical species and elementary reactions and statistically calculating the reaction rate constants; (2) a reduction method to simplify the complex reaction network into a skeletal network which can be used directly for kinetic modeling; and (3) a deterministic method for validating the derived full and skeletal kinetic models. The methodology is demonstrated by analyzing simulation data of hydrogen combustion. The full reaction network comprises 69 species and 256 reactions, which is reduced into a skeletal network of 9 species and 30 reactions. The kinetic models of both the full and skeletal networks represent the simulation data well. In addition, the essential elementary reactions and their rate constants agree favorably with those obtained experimentally. © 2019 Wiley Periodicals, Inc.

Single-Crystalline MFI Zeolite with Sheet-Like Mesopores Layered along the a Axis.

Designing a templating strategy for directing mesopore growth along different crystallographic directions is essential for fabricating two- or three-dimensional single-crystalline mesoporous zeolites. However, so far, mesopores formed in MFI zeolites by soft templates have mostly been generated by disrupting growth along the b axis; generating mesopores by disrupting growth along the a axis is rare. Herein, a single-crystalline mesoporous MFI zeolite (SCMMZ) with sheet-like mesopores layered along the a and b axes was synthesized using a triply branched surfactant with diquaternary ammonium groups connected to 1,3,5-triphenylbenzene by a six- and eight-carbon alkyl chain (TPB-6 and 8). The sheet-like mesopores were embedded in the MFI framework and were retained even after calcination. Molecular mechanics calculations provided evidence of low binding energy configurations of the surfactant that directed the growth of straight and zigzag channels along the b and a axes, respectively. The formation of nanosheets was attributed to the geometric matching of the arrangement of the aromatic groups to the zeolite framework.

Purple Dragons and Yellow Toadstools a Versatile Exercise for Introducing Students to Negotiated Consensus.

An activity called Purple Dragons and Yellow Toadstools, originally reported in 1987 as a training activity for jurors, was adapted as a priming exercise for a unit on teaching research ethics with undergraduate students. In this activity, learners develop skills for building negotiated consensus. The procedure involves individuals' ranking 10-15 moral transgressions and/or legal violations followed by a small group discussion in order to arrive at an agreed-upon ranking by the team. The framework has proved to be quite flexible, adaptable to different subject areas and with different populations of students.

[Myocardial Magnetic Resonance Texture Characteristics of Healthy Volunteers].

OBJECTIVE: To determine the myocardial texture features of cardiac magnetic resonance (CMR) in healthy adult Han populations. METHODS: 59 healthy Han volunteers were recruited for this study from May 2016 to November 2017. CMR examinations were performed on the participants with a 3.0T scanner (Tim Trio, Siemens Medical Solution) to estimate the functional parameters, Native T1 value and ECV. Texture analysis (TA) was performed on the region of interest (ROI) in the left ventricle myocardium on T1 mapping images, with 40 myocardial texture features being extracted. Differences in the myocardial texture features across gender and age groups were analyzed through Student's t-tests or Wilcoxon signed-rank tests. Spearman correlations were analyzed between the myocardial texture features and age, native T1 value and extracellular volume (ECV). RESULTS: Of the 59 participants, 28 were women and 29 were in the younger age group (< 45 years old). The male participants had higher left ventricular mass index (Lvmassi) and lower native T1 than their female counterparts (P < 0.01). No gender differences in blood pressure, heart rate, left ventricular ejection fraction (LVEF) and ECV values were found. Ten of the forty myocardial texture features showed gender differences, including two first order features and eight Grey-level co-occurrence matrix (GLCM) features. Gender differences appeared in five first order features and eight GLCM features in the younger group (< 45 years old), but not in the older group (≥45 years old). Eight myocardial texture features were correlated with age, including five first order features and three GLCM features (all P < 0.01). Six first-order texture features were correlated with Native T1 values of the left ventricle middle myocardium. Three first-order texture features were correlated with ECV. CONCLUSION: Myocardial texture features in T1 mapping images vary by gender and age in healthy Han populations.

Hierarchical MFI Zeolites with a Single-Crystalline Sponge-Like Mesostructure.

Single-crystalline sponge-like MFI mesoporous zeolites (SSMZs) have been synthesized by using bolaform surfactants with an axial chiral binaphthyl core in the hydrophobic tail and triquaternary ammonium head groups, as bifunctional organic structure-directing agents (OSDAs). By changing the length of alkyl chain between a triquaternary ammonium head group and a binaphthyl group from 4 to 10 carbons, SSMZs with high specific surface area (382-434 m2 g-1 ), abundant micropore-mesopore connectivity, and uniform mesopore diameter (4-10 nm) were obtained. OSDAs with an alkyl chain length of 11 and 12 carbons led to the formation of nanorod-constructed mesoporous MFI zeolites. A geometrical matching between the cylindrical arrangement of the binaphthyl groups and the zeolitic framework is speculated to be the key factor for the formation of mesoporous zeolites. The SSMZ zeolites, with abundant mesopores beneficial for the diffusion of reactants, exhibited significantly higher catalytic efficiencies than those of the conventional ZSM-5 with a microcrystal morphology (≈1.5 µm).

Cholesteric ordering predicted using a coarse-grained polymeric model with helical interactions.

The understanding of cholesteric liquid crystals at a molecular level is challenging. Limited insights are available to bridge between molecular structures and macroscopic chiral organization. In the present study, we introduce a novel coarse-grained (CG) molecular model, which is represented by flexible chain particles with helical interactions (FCh), to study the liquid crystalline phase behavior of cholesteric molecules such as double strand DNA and α-helix polypeptides using molecular dynamics (MD) simulations. The isotropic-cholesteric phase transitions of FCh molecules were simulated for varying chain flexibilities. A wall confinement was used to break the periodicity along the cholesteric helix director in order to predict the equilibrium cholesteric pitch. The left-handed cholesteric phase was shown for FCh molecules with right-handed chiral interactions, and a spatially inhomogeneous distribution of the nematic order parameter profile was observed in cholesteric phases. It was found that the chain flexibility plays an important role in determining the macroscopic cholesteric pitch and the structure of the cholesteric liquid crystal phase. The simulations provide insight into the relationship between microscopic molecular characteristics and the macroscopic phase behavior.

Predicting Thermodynamic Properties of Alkanes by High-Throughput Force Field Simulation and Machine Learning.

Knowledge of the thermodynamic properties of molecules is essential for chemical process design and the development of new materials. Experimental measurements are often expensive and not environmentally friendly. In the past, studies using molecular simulations have focused on a specific class of molecules, owing to the lack of a consistent force field and simulation protocol. To solve this problem, we have developed a high-throughput force field simulation (HT-FFS) procedure by combining a recently developed general force field with a validated simulation protocol to calculate thermodynamic properties for large number of molecules. This procedure is applied to calculate liquid densities, heats of vaporization, heat capacities, vapor-liquid equilibrium curves, critical temperatures, critical densities and surface tensions for a wide range of alkanes. The predictions agree well with available experimental data in terms of accuracy and precision, demonstrating that HT-FFS is a valid approach to supplementing experimental measurements. Furthermore, the large amount of data generated by HT-FFS lays a foundation for machine learning. We have developed an artificial neural network that demonstrates the feasibility of expanding predictions beyond simulation using a machine learning model.

A Coarse-Grained Force Field Parameterized for MgCl2 and CaCl2 Aqueous Solutions.

Calcium and magnesium ions play important roles in many physicochemical processes. To facilitate the investigation of phenomena related to these ions that occur over large length and time scales, a coarse-grained force field (CGFF) is developed for MgCl2 and CaCl2 aqueous solutions. The ions are modeled by CG beads with characteristics of hydration shells. To accurately describe the nonideal behavior of the solutions, osmotic coefficients in a wide range of concentrations were used as guidance for parametrization. The osmotic coefficients were obtained from the chemical potential increments of water calculated using the Bennett acceptance ratio (BAR) method. The result CGFF accurately reproduces experimental osmotic coefficients, densities, surface tensions, and cation-anion separations of calcium chloride and magnesium chloride solutions at molalities up to 3.0 mol/kg. As a preliminary application, the force field is applied to simulate aggregations of sodium dodecyl sulfate (SDS) in calcium chloride solution, and the simulation results are consistent with experimental observations.

Free Energy-Based Coarse-Grained Force Field for Binary Mixtures of Hydrocarbons, Nitrogen, Oxygen, and Carbon Dioxide.

The free energy based Lennard-Jones 12-6 (FE-12-6) coarse-grained (CG) force field developed for alkanes1 has been extended to model small molecules of light hydrocarbons (methane, ethane, propane, butane, and isobutane), nitrogen, oxygen, and carbon dioxide. The adjustable parameters of the FE-12-6 potential are determined by fitting against experimental vapor-liquid equilibrium (VLE) curves and heat of vaporization (HOV) data for pure substance liquids. Simulations using the optimized FE-12-6 parameters correctly reproduced experimental measures of the VLE, HOV, density, vapor pressure, compressibility, critical point, and surface tension for pure substances over a wide range of thermodynamic states. The force field parameters optimized for pure substances were tested on methane/butane, nitrogen/decane, and carbon dioxide/decane binary mixtures to predict their vapor-liquid equilibrium phase diagrams. It is found that for nonpolar molecules represented by different sized beads, a common scaling factor (0.08) that reduces the strength of the interaction potential between unlike beads, generated using Lorentz-Berthelot (LB) combination rules, is required to predict vapor-liquid phase equilibria accurately.

Hierarchical atom type definitions and extensible all-atom force fields.

The extensibility of force field is a key to solve the missing parameter problem commonly found in force field applications. The extensibility of conventional force fields is traditionally managed in the parameterization procedure, which becomes impractical as the coverage of the force field increases above a threshold. A hierarchical atom-type definition (HAD) scheme is proposed to make extensible atom type definitions, which ensures that the force field developed based on the definitions are extensible. To demonstrate how HAD works and to prepare a foundation for future developments, two general force fields based on AMBER and DFF functional forms are parameterized for common organic molecules. The force field parameters are derived from the same set of quantum mechanical data and experimental liquid data using an automated parameterization tool, and validated by calculating molecular and liquid properties. The hydration free energies are calculated successfully by introducing a polarization scaling factor to the dispersion term between the solvent and solute molecules. © 2015 Wiley Periodicals, Inc.