GAS-PHASE ACID-BASE PROPERTIES AND CONFORMATIONS OF OLIGOPEPTIDES THROUGH MASS SPECTROMETRY AND COMPUTATIONAL STUDIES

This dissertation presents a comprehensive study of the peptides of interest to deeper understand the gas-phase acid-base properties in relation to their conformations and chirality. In the first part of the study, two pairs of alanine (A)-based isomeric peptides consisting of a basic probe, lysine (Lys) or 2,3-diaminopropionic acid (Dap), were investigated to understand the nature of the enhanced basicity when the basic probe was moved from the N-terminus to the C-terminus. In the second part of the study, alanine-based peptides containing a cysteine (C) as the acidic probe were investigated to understand the chirality effects on the gas-phase acidity by altering the chiral centers systemically. Previous studies by mass spectrometry showed that the peptides ALys and AADap have had remarkably higher proton affinity (PA) compared to their isomeric counterparts LysA and DapAA. In this work, conformations, energetics, and molecular properties of the peptide systems have been thoroughly characterized through infrared multiple photon dissociation (IRMPD) spectroscopy and quantum chemical computations utilizing a set of molecular modeling tools. The molecular properties include charge distribution, dipole moment, torsional strain, hydrogen bonding, and non-covalent interaction. Computational studies yielded the lowest energy conformations along with their theoretical infrared (IR) spectra for each of the peptide systems. The resulting theoretical proton affinities are in excellent agreement with experiments. The results also suggest that the relative stability of the protonated peptides is the main source of the difference in the gas-phase basicity between the isomeric peptides. Structurally representative conformations for the protonated peptides were identified by matching the theoretical IR spectra to the corresponding IRMPD spectra. The band features of the IRMPD spectra were analyzed in detail by vibrational mode decomposition. The N-probe peptide ions, LysAH+ and DapAAH+, adopt diverse backbone geometries and intramolecular hydrogen bonding networks, and rely heavily on the hydrogen bonds for conformational stabilization. In contrast, the C-probe peptide ions, ALysH+ and AADapH+, adopt helical conformations, and benefit from the interaction between the helix macrodipole and the charged NH3+ group. The low torsional strain on the Lys sidechain contributes significantly to the conformational stability for ALysH+ than for LysAH+. The chirality of each residue in CAAA and Ac-CAAA (Ac represents the acetyl group) alters from the L- to the D-form systematically to generate two series of peptides. Qualitative comparison of the gas-phase acidity was achieved through mass spectrometry measurements using the Cooks’ kinetic method. The following two acidity ladders from the most acidic to the least acidic were obtained: CAAdA \u3e CAdAA ~ CAAA \u3e dCAAA \u3e CdAAA, and Ac-dCAAA \u3e Ac-CAAdA \u3e Ac-CAAA \u3e Ac-CAdAA \u3e Ac-CdAAA, where the superscript-d in front of the amino acid symbol indicates the D-form of that residue. In both non-acetylated and acetylated peptides, the gas-phase acidity increases as the D-alanine moves further away from the N-terminal acidic probe cysteine. Inversion of the cysteine residue from the L- to the D-form reduces the gas-phase acidity of the non-acetylated peptide and enhances the gas-phase acidity of the acetylated one. Overall, the change in the gas-phase acidity is likely due to the conformational reorganization in the deprotonated peptides upon chiral inversion

Protecting Heritage Trees in Weifang City, Shandong Province, Northern China

Heritage trees are long-lived trees or notable trees with great historical significance, scientific value, or commemorative importance. Many heritage trees have been preserved in Weifang City, Shandong Province, Northern China. In this paper, the origin, species diversity, age structure, health, surrounding habitats, distribution, genera floristic composition, and challenges of protecting the heritage trees were investigated using literature analysis, field survey, and interview. There are 864 heritage trees in the city, composed of 49 species, 41 genera, and 25 families. The heritage trees are divided into three original types: religious trees, naturally dispersed and preserved wild trees, and trees with agricultural backgrounds or used as offerings. Particularly, Sophora japonica, Ziziphus jujube, Ginkgo biloba, Sabina chinensis, Platycladus orientalis, and Osmanthus fragrans are the six most common species. There are 208 individuals of 500 years or elder, 293 individuals of 300–499 years old, 359 individuals of 100–299 years old, and 4 individuals of notable trees. Most of them are distributed in low-urbanized areas of the 4 county-level cities and Linqu county, and few are distributed in high-urbanized areas of the 4 districts of Weifang City. There are 14, 12, and 6 genera belonging to the areal-types of Temperate, Cosmopolitan, and Tropic, respectively, similar to that of wild seed plants in Yishan Mountain. There are some challenges in protecting heritage trees, such as urbanization, habitat deterioration, natural disasters, anthropic activities, health decline, and inadequate management. Some protection measures that have been taken previously are summarized, and some measures that should be taken in the future are proposed

4D Unsupervised Object Discovery

Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. With the growth of data volume, the expensive cost of annotations is the major limitation hindering further study. Therefore, discovering objects without annotations has great significance. However, this task seems impractical on still-image or point cloud alone due to the lack of discriminative information. Previous studies underlook the crucial temporal information and constraints naturally behind multi-modal inputs. In this paper, we propose 4D unsupervised object discovery, jointly discovering objects from 4D data -- 3D point clouds and 2D RGB images with temporal information. We present the first practical approach for this task by proposing a ClusterNet on 3D point clouds, which is jointly iteratively optimized with a 2D localization network. Extensive experiments on the large-scale Waymo Open Dataset suggest that the localization network and ClusterNet achieve competitive performance on both class-agnostic 2D object detection and 3D instance segmentation, bridging the gap between unsupervised methods and full supervised ones. Codes and models will be made available at https://github.com/Robertwyq/LSMOL.Comment: Accepted by NeurIPS 2022. 17 pages, 6 figure