Spin Josephson effects in Exchange coupled Anti-ferromagnets

The energy of exchange coupled antiferromagnetic insulators (AFMIs) is a periodic function of the relative in-plane orientation of the N\'{e}el vector fields. We show that this leads to oscillations in the relative magnetization of exchange coupled AFMIs separated by a thin metallic barrier. These oscillations pump a spin current ($I_{S}$) through the metallic spacer that is proportional to the rate of change of the relative in-plane orientation of the N\'{e}el vector fields. By considering spin-transfer torque induced by a spin chemical potential ($V_{S}$) at one of the interfaces, we predict non-Ohmic $I_{S}$-$V_{S}$ characteristics of AFMI exchange coupled hetero-structures, which leads to a non-local voltage across a spin-orbit coupled metallic spacer

COMPUTATIONAL METHODS IN MISSENSE MUTATION ANALYSIS: PHENOTYPES, PATHOGENICITY, AND PROTEIN ENGINEERING

Understanding the molecular, phenotypic, and pathogenic effects of mutations is of enormous importance in human disease research and protein engineering. Both create a demand for computational methods to leverage the explosion of new sequence data and to explore the vast space of possible protein modifications and designs. My study in this dissertation demonstrates the value of computational methods in these areas. First, I developed a new ensemble method to predict continuous phenotype values as well as binary pathogenicity and objectively tested it in CAGI (Critical Assessment of Genome Interpretation). In two recent CAGI challenges, the method was ranked third in predicting the enzyme activity of missense mutations in NAGLU (N-Acetyl-Alpha-Glucosaminidase) and second in predicting the relative growth rate of mutated human SUMO-ligase in a yeast complementation assay. I also demonstrated the effectiveness of the new ensemble method for addressing a key problem limiting the use of current mutation interpretation methods in the clinic – identifying which mutations can be assigned a pathogenic or benign status with high confidence. Next, I characterized and compared missense variants in monogenic disease and in cancer. The study revealed a number of properties of mutations in these two types of diseases, including: (a) methods based on sequence conservation properties are as effective for identifying cancer driver mutations as they are for monogenic disease mutations; (b) mutations in disordered regions of protein structure play a relatively small role in both classes of disease; (c) oncogenic mutations tend to be on the protein surface while tumor suppressors are concentrated in the core; (d) a large fraction of tumor suppressors act by destabilizing protein structure and (e) mutations in passenger genes display a surprisingly high level of deleteriousness. Finally, I applied computational methods to screen for appropriate mutations to enhance the thermostability of a catalytic domain of PlyC. This bacteriophage-derived endolysin has been demonstrated to have antimicrobial potential but its potential use is limited by its inherent thermosuseptibility. To prioritize stabilizing mutations, I conducted a rapid exhaustive survey of point mutations followed by validation using protein modeling and expert knowledge. The approach yielded three stabilizing mutants experimentally verified by our collaborators, with one particularly successful in terms of both thermal denaturation temperature and kinetic stability

Nucleic acid-mediated intracellular protein delivery by lipid-like nanoparticles

Intracellular protein delivery has potential biotechnological and therapeutic application, but remains technically challenging. In contrast, a plethora of nucleic acid carriers have been developed, with lipid-based nanoparticles (LNPs) among the most clinically advanced reagents for oligonucleotide delivery. Here, we validate the hypothesis that oligonucleotides can serve as packaging materials to facilitate protein entrapment within and intracellular delivery by LNPs. Using two distinct model proteins, horseradish peroxidase and NeutrAvidin, we demonstrate that LNPs can yield efficient intracellular protein delivery in vitro when one or more oligonucleotides have been conjugated to the protein cargo. Moreover, in experiments with NeutrAvidin in vivo, we show that oligonucleotide conjugation significantly enhances LNP-mediated protein uptake within various spleen cell populations, suggesting that this approach may be particularly suitable for improved delivery of protein-based vaccines to antigen-presenting cells.National Heart, Lung, and Blood Institute (Contract HHSN268201000045C)National Institutes of Health (U.S.) (Grant R01-EB000244-27)National Institutes of Health (U.S.) (Grant 5-R01-CA132091-04)National Science Foundation (U.S.)Juvenile Diabetes Research Foundation International (Grant 17–2007-1063)United States. Dept. of Defense. Congressionally Directed Medical Research Programs (Grant W81XWH-13-1-0215

Tunable Surface Wrinkling on Shape Memory Polymers with Application in Smart Micromirror

Surfaces with tunable topological features enable important applications, such as optical devices, precision metrology, adhesion, and&nbsp; wetting. In this study, we demonstrate a facile method to fabricate and control the surface morphologies by combining thin film wrinkling&nbsp; and thermal expansion. This approach utilizes self-assembled surface wrinkling induced by shape recovery of shape memory polymers and&nbsp; localized thermal expansion caused by Joule heating. Recovering the prestrain in the SMP substrate induces global wrinkling of the thin film&nbsp; on the substrate. Joule heating in the SMP by a heating wire embedded in the substrate induces thermal expansion of the substrate in a&nbsp; localized area, which leads to disappearance of the wrinkling pattern. This effect is reversed when heating is stopped, leading to reversible&nbsp; and repeatable tuning of the surface morphology in a controllable localized surface region. With metal coating, the SMP surface&nbsp; can be switched from specular to diffuse reflectance with respond to external Joule heating. Finally, we demonstrate a smart micromirror&nbsp; device with its diffuse reflectance tunable between 13.5% to 81.9% in visible light region. This approach provides a method to modulate&nbsp; surface diffusivity by controlling its surface morphologies, with potential applications in optical display and optical microelectromechanical (MEMS) devices.</p

Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation

Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences is essential for providing personalized recommendations. Session-based recommendation, which utilizes customer session data to predict their next interaction, has become increasingly popular. However, existing session datasets have limitations in terms of item attributes, user diversity, and dataset scale. As a result, they cannot comprehensively capture the spectrum of user behaviors and preferences. To bridge this gap, we present the Amazon Multilingual Multi-locale Shopping Session Dataset, namely Amazon-M2. It is the first multilingual dataset consisting of millions of user sessions from six different locales, where the major languages of products are English, German, Japanese, French, Italian, and Spanish. Remarkably, the dataset can help us enhance personalization and understanding of user preferences, which can benefit various existing tasks as well as enable new tasks. To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation. With the above tasks, we benchmark a range of algorithms on our proposed dataset, drawing new insights for further research and practice. In addition, based on the proposed dataset and tasks, we hosted a competition in the KDD CUP 2023 and have attracted thousands of users and submissions. The winning solutions and the associated workshop can be accessed at our website https://kddcup23.github.io/.Comment: Accepted by NeurIPS 2023, Track on Datasets and Benchmarks; Dataset for KDD Cup 2023, https://kddcup23.github.io

Novel Feature for Catalytic Protein Residues Reflecting Interactions with Other Residues

Owing to their potential for systematic analysis, complex networks have been widely used in proteomics. Representing a protein structure as a topology network provides novel insight into understanding protein folding mechanisms, stability and function. Here, we develop a new feature to reveal correlations between residues using a protein structure network. In an original attempt to quantify the effects of several key residues on catalytic residues, a power function was used to model interactions between residues. The results indicate that focusing on a few residues is a feasible approach to identifying catalytic residues. The spatial environment surrounding a catalytic residue was analyzed in a layered manner. We present evidence that correlation between residues is related to their distance apart most environmental parameters of the outer layer make a smaller contribution to prediction and ii catalytic residues tend to be located near key positions in enzyme folds. Feature analysis revealed satisfactory performance for our features, which were combined with several conventional features in a prediction model for catalytic residues using a comprehensive data set from the Catalytic Site Atlas. Values of 88.6 for sensitivity and 88.4 for specificity were obtained by 10fold crossvalidation. These results suggest that these features reveal the mutual dependence of residues and are promising for further study of structurefunction relationship

Predicting disease-associated substitution of a single amino acid by analyzing residue interactions

<p>Abstract</p> <p>Background</p> <p>The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.</p> <p>Results</p> <p>We found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively.</p> <p>Conclusions</p> <p>The satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.</p