LUMINESCENT NANOCRYSTALS: SYNTHESIS, CHARACTERIZATION AND OPTICAL TUNING

Ph.DDOCTOR OF PHILOSOPH

Feedback Control Variables Have No Influence on the Permanence of a Discrete n

We consider a discrete n-species Schoener competition system with time delays and feedback controls. By using difference inequality theory, a set of conditions which guarantee the permanence of system is obtained. The results indicate that feedback control variables have no influence on the persistent property of the system. Numerical simulations show the feasibility of our results

DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence

Recent development of spatial transcriptomics (ST) is capable of associating spatial information at different spots in the tissue section with RNA abundance of cells within each spot, which is particularly important to understand tissue cytoarchitectures and functions. However, for such ST data, since a spot is usually larger than an individual cell, gene expressions measured at each spot are from a mixture of cells with heterogenous cell types. Therefore, ST data at each spot needs to be disentangled so as to reveal the cell compositions at that spatial spot. In this study, we propose a novel method, named deconvoluting spatial transcriptomics data through graph-based convolutional networks (DSTG), to accurately deconvolute the observed gene expressions at each spot and recover its cell constitutions, thus achieving high-level segmentation and revealing spatial architecture of cellular heterogeneity within tissues. DSTG not only demonstrates superior performance on synthetic spatial data generated from different protocols, but also effectively identifies spatial compositions of cells in mouse cortex layer, hippocampus slice and pancreatic tumor tissues. In conclusion, DSTG accurately uncovers the cell states and subpopulations based on spatial localization. DSTG is available as a ready-to-use open source software (https://github.com/Su-informatics-lab/DSTG) for precise interrogation of spatial organizations and functions in tissues

Deep learning for in vitro prediction of pharmaceutical formulations

Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error approach by individual experiences of pharmaceutical scientists, which is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of this research is to use deep learning to predict pharmaceutical formulations. In this paper, two different types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assessing the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. The result shows the accuracies of both two deep neural networks were above 80% and higher than other machine learning models, which showed good prediction in pharmaceutical formulations. In summary, deep learning with the automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was firstly developed for the prediction of pharmaceutical formulations. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical researches from experience-dependent studies to data-driven methodologies

scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics

Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. Through benchmarking with other label transfer methods on a total of 30 single cell omics datasets, scGCN consistently demonstrates superior accuracy on leveraging cells from different tissues, platforms, and species, as well as cells profiled at different molecular layers. scGCN is implemented as an integrated workflow as a python software, which is available at https://github.com/QSong-github/scGCN

High efficiency uniform positron beam loading in a hollow channel plasma wakefield accelerator

We propose a novel positron beam loading regime in a hollow plasma channel that can efficiently accelerate $e^+$ beam with high gradient and narrow energy spread. In this regime, the $e^+$ beam coincides with the drive $e^-$ beam in time and space and their net current distribution determines the plasma wakefields. By precisely shaping the beam current profile and loading phase according to explicit expressions, three-dimensional Particle-in-Cell (PIC) simulations show that the acceleration for $e^+$ beam of $\sim$nC charge with $\sim$GV/m gradient, $\lesssim$0.5% induced energy spread and $\sim$50% energy transfer efficiency can be achieved simultaneously. Besides, only tailoring the current profile of the more tunable $e^-$ beam instead of the $e^+$ beam is enough to obtain such favorable results. A theoretical analysis considering both linear and nonlinear plasma responses in hollow plasma channels is proposed to quantify the beam loading effects. This theory agrees very well with the simulation results and verifies the robustness of this beam loading regime over a wide range of parameters

Semimetal Contacts to Monolayer Semiconductor: Weak Metalization as an Effective Mechanism to Schottky Barrier Lowering

Recent experiment has uncovered semimetal bismuth (Bi) as an excellent electrical contact to monolayer MoS$_2$ with ultralow contact resistance. The contact physics of the broader semimetal/monolayer-semiconductor family beyond Bi/MoS$_2$, however, remains largely unexplored thus far. Here we perform a comprehensive first-principle density functional theory investigation on the electrical contact properties between six archetypal two-dimensional (2D) transition metal dichalcogenide (TMDC) semiconductors, i.e. MoS$_2$, WS$_2$, MoSe$_2$, WSe$_2$, MoTe$_2$ and WTe$_2$, and two representative types of semimetals, Bi and antimony (Sb). As Bi and Sb work functions energetically aligns well with the TMDC conduction band edge, Ohmic or nearly-Ohmic $n$-type contacts are prevalent. The interlayer distance of semimetal/TMDC contacts are significantly larger than that of the metal/TMDC counterparts, which results in only weak metalization of TMDC upon contact formation. Intriguingly, such weak metalization generates semimetal-induced gap states (MIGS) that extends below the conduction band minimum, thus offering an effective mechanism to reduce or eliminate the $n$-type Schottky barrier height (SBH) while still preserving the electronic structures of 2D TMDC. A modified Schottky-Mott rule that takes into account SMIGS, interface dipole potential, and Fermi level shifting is proposed, which provides an improved agreement with the DFT-simulated SBH. We further show that the tunneling-specific resistivity of Sb/TMDC contacts are generally lower than the Bi counterparts, thus indicating a better charge injection efficiency can be achieved through Sb contacts. Our findings reveal the promising potential of Bi and Sb as excellent companion electrode materials for advancing 2D semiconductor device technology.Comment: 12 pages, 7 figure

Effects of the Largest Lake of the Tibetan Plateau on the Regional Climate

Qinghai Lake is the largest lake in China. However, its influence on the local climate remains poorly understood. By using an atmosphere-lake coupled model, we investigated the impact of the lake on the local climate. After the adjustment of four key parameters, the model reasonably reproduced the lake-air interaction. Superimposed by the orographic effects on lake-land breeze circulation, the presence of the lake enhanced precipitation over the southern part of the lake and its adjacent land, while slightly reduced precipitation along the northern shore of the lake. The lake effect on local precipitation revealed a distinct seasonal and diurnal variability, reducing precipitation in May (-6.6%) and June (-4.5%) and increasing it from July (5.7%) to November (125.6%). During the open water season, the lake's daytime cooling effect weakened and the nighttime warming effect strengthened, affecting spatial distribution and intensity of lake-induced precipitation. In early summer, precipitation slightly decreased over the north part of the lake due to the lake's daytime cooling. In turn, lake-induced nighttime warming increased precipitation over the southern section of the lake and its adjacent land. With the start of the autumn cooling in September, heat and moisture fluxes from the lake resulted in precipitation increase in both daytime and nighttime over the entire lake. In October, the background atmospheric circulation coupled with the strong lake effects lead to a small amount but high proportion of lake-induced precipitation spreading evenly over the lake.Peer reviewe