Modulation of Standing Spin Waves in Confined Rectangular Elements.

Magnonics is an emerging field within spintronics that focuses on developing novel magnetic devices capable of manipulating information through the modification of spin waves in nanostructures with submicron size. Here, we provide a confined magnetic rectangular element to modulate the standing spin waves, by changing the saturation magnetisation (MS), exchange constant (A), and the aspect ratio of rectangular magnetic elements via micromagnetic simulation. It is found that the bulk mode and the edge mode of the magnetic element form a hybrid with each other. With the decrease in MS, both the Kittel mode and the standing spin waves undergo a shift towards higher frequencies. On the contrary, as A decreases, the frequencies of standing spin waves become smaller, while the Kittel mode is almost unaffected. Moreover, when the length-to-width aspect ratio of the element is increased, standing spin waves along the width and length become split, leading to the observation of additional modes in the magnetic spectra. For each mode, the vibration style is discussed. These spin dynamic modes were further confirmed via FMR experiments, which agree well with the simulation results.

Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes.

As COVID-19 develops, dynamic changes occur in the patient's immune system. Changes in molecular levels in different immune cells can reflect the course of COVID-19. This study aims to uncover the molecular characteristics of different immune cell subpopulations at different stages of COVID-19. We designed a machine learning workflow to analyze scRNA-seq data of three immune cell types (B, T, and myeloid cells) in four levels of COVID-19 severity/outcome. The datasets for three cell types included 403,700 B-cell, 634,595 T-cell, and 346,547 myeloid cell samples. Each cell subtype was divided into four groups, control, convalescence, progression mild/moderate, and progression severe/critical, and each immune cell contained 27,943 gene features. A feature analysis procedure was applied to the data of each cell type. Irrelevant features were first excluded according to their relevance to the target variable measured by mutual information. Then, four ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and max-relevance and min-redundancy) were adopted to analyze the remaining features, resulting in four feature lists. These lists were fed into the incremental feature selection, incorporating three classification algorithms (decision tree, k-nearest neighbor, and random forest) to extract key gene features and construct classifiers with superior performance. The results confirmed that genes such as PFN1, RPS26, and FTH1 played important roles in SARS-CoV-2 infection. These findings provide a useful reference for the understanding of the ongoing effect of COVID-19 development on the immune system.

Identification of methylation signatures and rules for predicting the severity of SARS-CoV-2 infection with machine learning methods.

Patients infected with SARS-CoV-2 at various severities have different clinical manifestations and treatments. Mild or moderate patients usually recover with conventional medical treatment, but severe patients require prompt professional treatment. Thus, stratifying infected patients for targeted treatment is meaningful. A computational workflow was designed in this study to identify key blood methylation features and rules that can distinguish the severity of SARS-CoV-2 infection. First, the methylation features in the expression profile were deeply analyzed by a Monte Carlo feature selection method. A feature list was generated. Next, this ranked feature list was fed into the incremental feature selection method to determine the optimal features for different classification algorithms, thereby further building optimal classifiers. These selected key features were analyzed by functional enrichment to detect their biofunctional information. Furthermore, a set of rules were set up by a white-box algorithm, decision tree, to uncover different methylation patterns on various severity of SARS-CoV-2 infection. Some genes (PARP9, MX1, IRF7), corresponding to essential methylation sites, and rules were validated by published academic literature. Overall, this study contributes to revealing potential expression features and provides a reference for patient stratification. The physicians can prioritize and allocate health and medical resources for COVID-19 patients based on their predicted severe clinical outcomes.

A novel and effective approach to generate germline-like monoclonal antibodies by integration of phage and mammalian cell display platforms.

Phage display technology allows for rapid selection of antibodies from the large repertoire of human antibody fragments displayed on phages. However, antibody fragments should be converted to IgG for biological characterizations and affinity of antibodies obtained from phage display library is frequently not sufficient for efficient use in clinical settings. Here, we describe a new approach that combines phage and mammalian cell display, enabling simultaneous affinity screening of full-length IgG antibodies. Using this strategy, we successfully obtained a novel germline-like anti-TIM-3 monoclonal antibody named m101, which was revealed to be a potent anti-TIM-3 therapeutic monoclonal antibody via in vitro and in vivo experiments, indicating its effectiveness and power. Thus, this platform can help develop new monoclonal antibody therapeutics with high affinity and low immunogenicity.

Highly Anisotropic Magnetic Domain Wall Behavior in In-Plane Magnetic Films.

We have studied the nucleation of magnetic domains and propagation of magnetic domain walls (DWs) induced by pulsed magnetic field in a ferromagnetic film with in-plane uniaxial anisotropy. In contrast to observed behavior in films with out-of-plane anisotropy, the nucleated domains have a rectangular shape in which a pair of the opposite sides are perfectly linear DWs, while the other pair present zigzags. The field induced propagation of these two DW types are found to be different. The linear ones follow a creep law identical to what is usually observed in out-of-plane films, while the velocity of zigzag DWs depends linearly on the applied field amplitude down to very low field. This unexpected feature can be explained by the shape of the DW, and these results provide first experimental evidence of the applicability of the 1D model in two-dimensional ferromagnetic thin films.

Ferromagnet/Two-Dimensional Semiconducting Transition-Metal Dichalcogenide Interface with Perpendicular Magnetic Anisotropy.

Ferromagnet/two-dimensional transition-metal dichalcogenide (FM/2D TMD) interfaces provide attractive opportunities to push magnetic information storage to the atomically thin limit. Existing work has focused on FMs contacted with mechanically exfoliated or chemically vapor-deposition-grown TMDs, where clean interfaces cannot be guaranteed. Here, we report a reliable way to achieve contamination-free interfaces between ferromagnetic CoFeB and molecular-beam epitaxial MoSe2. We show a spin reorientation arising from the interface, leading to a perpendicular magnetic anisotropy (PMA), and reveal the CoFeB/2D MoSe2 interface allowing for the PMA development in a broader CoFeB thickness-range than common systems such as CoFeB/MgO. Using X-ray magnetic circular dichroism analysis, we attribute generation of this PMA to interfacial d-d hybridization and deduce a general rule to enhance its magnitude. We also demonstrate favorable magnetic softness and considerable magnetic moment preserved at the interface and theoretically predict the interfacial band matching for spin filtering. Our work highlights the CoFeB/2D MoSe2 interface as a promising platform for examination of TMD-based spintronic applications and might stimulate further development with other combinations of FM/2D TMD interfaces.