Single Cell Proteomics in Biomedicine: High-dimensional Data Acquisition, Visualization and Analysis

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions

Single Cell Proteomics in Biomedicine: High-dimensional Data Acquisition, Visualization and Analysis

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions

Single-cell RNA sequencing reveals the transcriptomic characteristics of peripheral blood mononuclear cells in hepatitis B vaccine non-responders

The emergence of a vaccine against hepatitis B has proven to be an important milestone in the prevention of this disease; however, 5%–10% of vaccinated individuals do not generate an immune response to the vaccine, and its molecular mechanism has not been clarified. In this study, single-cell RNA sequencing was performed on peripheral blood mononuclear cells (PBMCs) from three volunteers with a high immune response (HR) and three with no immune response (NR) to the hepatitis B vaccine. We found that the antigen-presenting activity scores of various antigen-presenting cells, the mitogen-activated protein kinase (MAPK) pathway activity scores of naive B cells, and the cell activity scores of three types of effector T cells were significantly decreased, whereas the cytotoxicity scores of CD3highCD16lowKLRG1high natural killer T (NKT) cells were significantly increased in the NR group compared with those in the HR group. Additionally, the expression levels of some classical molecules associated with distinct signaling pathways—including HLA-B, HLA-DRB5, BLNK, BLK, IL4R, SCIMP, JUN, CEBPB, NDFIP1, and TXNIP—were significantly reduced in corresponding subsets of PBMCs from the NR group relative to those of the HR group. Furthermore, the expression of several cytotoxicity-related effector molecules, such as GNLY, NKG7, GZMB, GZMM, KLRC1, KLRD1, PRF1, CST7, and CTSW, was significantly higher in CD3highCD16lowKLRG1high NKT cells derived from non-responders. Our study provides a molecular basis for the lack of response to the hepatitis B vaccine, including defective antigen presentation, decreased T cell activity, and reduced IL-4 secretion, as well as novel insight into the role of NKT cells in the immune response to the hepatitis B vaccine

CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://www.synapse.org/#!Synapse:syn51471091/wiki/.Comment: 14 pages, 8 figure

Influence of Fluorinated Polyurethane Binder on the Agglomeration Behaviors of Aluminized Propellants

In this study, fluorinated polyurethane (FPU) was prepared from dialcohol-terminated perfluoropolyether as a soft segment; isophorone diisocyanate (IPDI) as a curing agent; 1,2,4-butanetriol (BT) as a crosslinker; and 1,4-butanediol (BDO) as a chain extender. Fourier transform infrared spectroscopy (FTIR) and 1H NMR were used to characterize the structure of the FPU. The mechanical properties of the FPUs with different BDO and BT contents were also measured. The tensile strength and breaking elongation of the optimized FPU formula were 3.7 MPa and 412%, respectively. To find out the action mechanism of FPU on Al, FPU/Al was prepared by adding Al directly to FPU. The thermal decomposition of the FPU and FPU/Al was studied and compared by simultaneous differential scanning calorimetry-thermogravimetry-mass spectrometry (DSC-TG-MS). It was found that FPU can enhance the oxidation of Al by altering the oxide-shell properties. The combustion performance of the FPU propellant, compared with the corresponding hydroxyl-terminated polyether (HTPE)-based polyurethane (HPU) propellant, was recorded by a high-speed video camera. The FPU propellants were found to produce smaller agglomerates due to the generation of AlF3 in the combustion process. These findings show that FPU may be a useful binder for tuning the agglomeration and reducing two-phase flow losses of aluminized propellants

Extract Descriptors for Point Cloud Registration by Graph Clustering Attention Network

Extracting geometric descriptors in 3D vision is the first step. It plays an important role in 3D registration, 3D reconstruction, and other applications. The success of many 3D tasks is closely related to whether the geometric descriptor has accurate characteristics. Today, the main methods are divided into manual production and neural network learning. The applicability of descriptors is limited to a low-level point, corner, edge, and fixed neighborhood features. For this, we use the class attention of the point cloud. In order to extract class attention, the graph clustering approach is utilized. It can collect points with similar structures and divide regions dynamically. While maintaining rotation invariance, features can enhance their fit to the original data. Point attention and edge attention are used to describe the structural characteristics of point clouds. We combine the three attentions indicated before to improve the features obtained by the PointNet decoder. This feature can dynamically reflect the structure of the point cloud, which includes both soft shape information and rich detail information. Finally, the 3D descriptors are extracted with the FoldingNet decoder. Our method is validated on both indoor and outdoor datasets. The accuracy of the final result is improved by two percentage points