Intermixing-Driven Surface and Bulk Ferromagnetism in the Quantum Anomalous Hall Candidate MnBi6Te10

The recent realizations of the quantum anomalous Hall effect (QAHE) in MnBi2Te4 and MnBi4Te7 benchmark the (MnBi2Te4)(Bi2Te3)n family as a promising hotbed for further QAHE improvements. The family owes its potential to its ferromagnetically (FM) ordered MnBi2Te4 septuple layers (SLs). However, the QAHE realization is complicated in MnBi2Te4 and MnBi4Te7 due to the substantial antiferromagnetic (AFM) coupling between the SLs. An FM state, advantageous for the QAHE, can be stabilized by interlacing the SLs with an increasing number n of Bi2Te3 quintuple layers (QLs). However, the mechanisms driving the FM state and the number of necessary QLs are not understood, and the surface magnetism remains obscure. Here, robust FM properties in MnBi6Te10 (n = 2) with Tc ≈ 12 K are demonstrated and their origin is established in the Mn/Bi intermixing phenomenon by a combined experimental and theoretical study. The measurements reveal a magnetically intact surface with a large magnetic moment, and with FM properties similar to the bulk. This investigation thus consolidates the MnBi6Te10 system as perspective for the QAHE at elevated temperatures

Birth Order and Family Size as Indicators of Social Competence

(Statement of Responsibility) by Anna Mary Folkers(Thesis) Thesis (B.A.) -- New College of Florida, 2010(Electronic Access) RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE(Bibliography) Includes bibliographical references.(Source of Description) This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.(Local) Faculty Sponsor: Graham, Steve

Antagonists of LHRH

LHRH analogs and congeners with high water solubility have been synthesized. These new analogs had 0%-100% antiovulatory activity at a 0.5 .mu.g dosage and 0%-80% at 0.25 .mu.g. The ED.sub.50 for histamine release was 30.5 .mu.g/ml->300 .mu.g/ml.Board of Regents, University of Texas Syste

Antagonists of LHRH

LHRH antagonists similar to antide and congeners with higher water solubility have been synthesized by substitutions in positions 1, 5 or 6 with hydrophilic residues. These peptides have antiovulatory activity with minimal histamine releasing effect.Board of Regents, University of Texas Syste

iSEE : Interface Structure, Evolution and Energy-based machine learning predictor of binding affinity changes upon mutations

Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution and Energy-based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal mol-1 on a diverse training dataset consisting of 1102 mutations in 57 protein-protein complexes. It competes with existing state-of-the-art methods on two blind test datasets. Predictions for a new dataset of 540 mutations in 58 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC<0.4), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2-p53 complex. This article is protected by copyright. All rights reserved

iSEE: Interface Structure, Evolution and Energy-based machine learning predictor of binding affinity changes upon mutations

Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution and Energy-based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal mol-1 on a diverse training dataset consisting of 1102 mutations in 57 protein-protein complexes. It competes with existing state-of-the-art methods on two blind test datasets. Predictions for a new dataset of 540 mutations in 58 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC<0.4), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2-p53 complex. This article is protected by copyright. All rights reserved

Architecture of the best-performing cascaded machine-learning model based on six first stage classifiers originating from three differing descriptor sets and two learning schemes (ANNs, SVMs) and a jury neural network containing three hidden neurons.

<p>The model delivers a prediction score from the interval [0,1[, with high values indicating MHC-I H-2K^b binding.</p

Figure 6

<p>(<b>a</b>) Melting curves of peptide-MHC-I (H-2K^b:IgG fusion protein) complexes depicting the normalized fluorescence <i>F</i> in relative fluorescence units (RFU) for SIINFEKL and <i>NoLigand</i> as positive (red line) and negative (black line) controls, an exemplary epitope fragment (INFE) showing no melting point shift (grey line) and all epitope fragments (IINFEKL, SIINF, INFEKL, SIINFEK, SIINFE) leading to a significant melting point shift (green lines). (<b>b</b>) Analogously the first derivative of <i>F</i> (d<i>F</i>) reveals the melting points as local minima, with <i>T</i>_m denoting the presumable MHC-I heavy chain melting point in the absence of peptide ligand <i>(NoLigand)</i>.</p

Workflow for the development of the cascaded machine-learning model.

<p>ANN: feed-forward artificial neural network, SVM: support vector machine. AAFREQ, BINAATYPE, BINPEP, PEPCATS, PPCA and PPCALI correspond to the utilized peptide descriptors (<i>cf.</i> Methods). TP, FP, FN and TN correspond to entities of a confusion table with true-positives, false-positives, false-negatives and true-negatives.</p