Multiresolution molecular mechanics: surface effects and iso-parametric analysis

Within the generalized framework of the newly presented energy-based concurrent atomistic/continuum method Multiresolution Molecular Mechanics (MMM) [1], two proposed summation rules, the bulk summation and the edge summation rule to efficiently determine the bulk and surface energy distribution respectively, consists the optimal MMM summation rule. In Ref. [1], the bulk summation rule has been verified and proved to outperform the widely used Gauss quadrature. In this study, the edge summation rule will be employed to specifically capture surface effects. This is achieved in three steps: (i) use the edge summation rule to determine the surface energy distribution for any given finite element method (FEM) shape function analytically; (ii) select the optimal number of surface primary sampling atoms on the surfaces, and (iii) determine the weight for each surface primary sampling atoms using the edge summation rule and then use their energies to sampling the surface energy. In particular, the effect of the selection of surface primary sampling atoms on the accuracy of capturing surface effect will be studied. In addition, the sampling errors introduced by employing the edge summation rule will be determined through error structure analysis. Then iso-parametric analysis within the generalized framework of MMM will be performed to standardize the implementation procedure of MMM, as is widely employed in conventional FEM. The iso-parametric analysis is achieved by performing the surface summation rule and the bulk summation rule within some specifically designed surface parent elements and bulk parent elements. In particular, the iso-parametric analysis will be performed with respect to linear, bilinear, and quadratic elements undergoing tensile, shear, and bending deformations and will be compared against full atomistic to show the effectiveness of MMM. REFERENCES [1] Yang, Q., To, A.C. Multiresolution molecular mechanics: a unified and consistent framework for general finite element shape functions. Comp. Meth. Appl. Mech. Eng. (under review). [2] Yang, Q., Biyikli, E., To, A.C. Multiresolution molecular mechanics: statics. Comp. Meth. Appl. Mechanics Eng. 2013, 258, 26‑38. [3] Yang, Q., Biyikli, E., To, A.C. Multiresolution molecular mechanics: convergence and error structure analysis. Comp. Meth. Appl. Mech. Eng. 2014, 269, 20‑45 . [4] Biyikli, E., Yang, Q., To, A.C. Multiresolution molecular mechanics: dynamics. Comp. Meth. Appl. Mech. Eng. 2014, 274, 42‑55

Avalanche-Induced Current Enhancement in Semiconducting Carbon Nanotubes

Semiconducting carbon nanotubes under high electric field stress (~10 V/um) display a striking, exponential current increase due to avalanche generation of free electrons and holes. Unlike in other materials, the avalanche process in such 1D quantum wires involves access to the third sub-band, is insensitive to temperature, but strongly dependent on diameter ~exp(-1/d^2). Comparison with a theoretical model yields a novel approach to obtain the inelastic optical phonon emission length, L_OP,ems ~ 15d nm. The combined results underscore the importance of multi-band transport in 1D molecular wires

Drinfeld Twists and Symmetric Bethe Vectors of Supersymmetric Fermion Models

We construct the Drinfeld twists (factorizing $F$-matrices) of the $gl(m|n)$-invariant fermion model. Completely symmetric representation of the pseudo-particle creation operators of the model are obtained in the basis provided by the $F$-matrix (the $F$-basis). We resolve the hierarchy of the nested Bethe vectors in the $F$-basis for the $gl(m|n)$ supersymmetric model.Comment: Latex File, 24 pages, no figure, some misprints are correcte

Donor-Acceptor Oligorotaxanes Made to Order

Five donor–acceptor oligorotaxanes made up of dumbbells composed of tetraethylene glycol chains, interspersed with three and five 1,5-dioxynaphthalene units, and terminated by 2,6-diisopropylphenoxy stoppers, have been prepared by the threading of discrete numbers of cyclobis(paraquat-p-phenylene) rings, followed by a kinetically controlled stoppering protocol that relies on click chemistry. The well-known copper(I)-catalyzed alkyne–azide cycloaddition between azide functions placed at the ends of the polyether chains and alkyne-bearing stopper precursors was employed during the final kinetically controlled template-directed synthesis of the five oligorotaxanes, which were characterized subsequently by ^1H NMR spectroscopy at low temperature (233 K) in deuterated acetonitrile. The secondary structures, as well as the conformations, of the five oligorotaxanes were unraveled by spectroscopic comparison with the dumbbell and ring components. By focusing attention on the changes in chemical shifts of some key probe protons, obtained from a wide range of low-temperature spectra, a picture emerges of a high degree of folding within the thread protons of the dumbbells of four of the five oligorotaxanes—the fifth oligorotaxane represents a control compound in effect— brought about by a combination of C-H···O and π–π stacking interactions between the p-electron-deficient bipyridinium units in the rings and the π-electron-rich 1,5-dioxynaphthalene units and polyether chains in the dumbbells. The secondary structures of a foldamer-like nature have received further support from a solid-state superstructure of a related [3]pseudorotaxane and density functional calculations performed thereon

Do Seasons Have an Influence on the Incidence of Depression? The Use of an Internet Search Engine Query Data as a Proxy of Human Affect

Background: Seasonal depression has generated considerable clinical interest in recent years. Despite a common belief that people in higher latitudes are more vulnerable to low mood during the winter, it has never been demonstrated that human's moods are subject to seasonal change on a global scale. The aim of this study was to investigate large-scale seasonal patterns of depression using Internet search query data as a signature and proxy of human affect. Methodology/Principal Findings: Our study was based on a publicly available search engine database, Google Insights for Search, which provides time series data of weekly search trends from January 1, 2004 to June 30, 2009. We applied an empirical mode decomposition method to isolate seasonal components of health-related search trends of depression in 54 geographic areas worldwide. We identified a seasonal trend of depression that was opposite between the northern and southern hemispheres; this trend was significantly correlated with seasonal oscillations of temperature (USA: r = −0.872, <0.001; Australia: r = −0.656, <0.001). Based on analyses of search trends over 54 geological locations worldwide, we found that the degree of correlation between searching for depression and temperature was latitude-dependent (northern hemisphere: r = −0.686; <0.001; southern hemisphere: r = 0.871; <0.0001). Conclusions/Significance: Our findings indicate that Internet searches for depression from people in higher latitudes are more vulnerable to seasonal change, whereas this phenomenon is obscured in tropical areas. This phenomenon exists universally across countries, regardless of language. This study provides novel, Internet-based evidence for the epidemiology of seasonal depression

From Human Grading to Machine Grading: Automatic Diagnosis of e-Book Text Marking Skills in Precision Education

Precision education is a new challenge in leveraging artificial intelligence, machine learning, and learning analytics to enhance teaching quality and learning performance. To facilitate precision education, text marking skills can be used to determine students’ learning process. Text marking is an essential learning skill in reading. In this study, we proposed a model that leverages the state-of-the-art text summarization technique, Bidirectional Encoder Representations from Transformers (BERT), to calculate the marking score for 130 graduate students enrolled in an accounting course. Then, we applied learning analytics to analyze the correlation between their marking scores and learning performance. We measured students’ self-regulated learning (SRL) and clustered them into four groups based on their marking scores and marking frequencies to examine whether differences in reading skills and text marking influence students’ learning performance and awareness of self-regulation. Consistent with past research, our results did not indicate a strong relationship between marking scores and learning performance. However, high-skill readers who use more marking strategies perform better in learning performance, task strategies, and time management than high-skill readers who use fewer marking strategies. Furthermore, high-skill readers who actively employ marking strategies also achieve superior scores of environment structure, and task strategies in SRL than low-skill readers who are inactive in marking. The findings of this research provide evidence supporting the importance of monitoring and training students’ text marking skill and facilitating precision education

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1–3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy

Topological Pattern Recognition of Severe Alzheimer's Disease via Regularized Supervised Learning of EEG Complexity

Alzheimer's disease (AD) is a progressive brain disorder with gradual memory loss that correlates to cognitive deficits in the elderly population. Recent studies have shown the potentials of machine learning algorithms to identify biomarkers and functional brain activity patterns across various AD stages using electroencephalography (EEG). In this study, we aim to discover the altered spatio-temporal patterns of EEG complexity associated with AD pathology in different severity levels. We employed the multiscale entropy (MSE), a complexity measure of time series signals, as the biomarkers to characterize the nonlinear complexity at multiple temporal scales. Two regularized logistic regression methods were applied to extracted MSE features to capture the topographic pattern of MSEs of AD cohorts compared to healthy baseline. Furthermore, canonical correlation analysis was performed to evaluate the multivariate correlation between EEG complexity and cognitive dysfunction measured by the Neuropsychiatric Inventory scores. 123 participants were recruited and each participant was examined in three sessions (length = 10 seconds) to collect resting-state EEG signals. MSE features were extracted across 20 time scale factors with pre-determined parameters (m = 2, r = 0.15). The results showed that comparing to logistic regression model, the regularized learning methods performed better for discriminating severe AD cohort from normal control, very mild and mild cohorts (test accuracy ~ 80%), as well as for selecting significant biomarkers arcoss the brain regions. It was found that temporal and occipitoparietal brain regions were more discriminative in regard to classifying severe AD cohort vs. normal controls, but more diverse and distributed patterns of EEG complexity in the brain were exhibited across individuals in early stages of AD