254 research outputs found
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 beam with high gradient and narrow energy
spread. In this regime, the beam coincides with the drive 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 beam of nC charge with
GV/m gradient, 0.5% induced energy spread and 50% energy
transfer efficiency can be achieved simultaneously. Besides, only tailoring the
current profile of the more tunable beam instead of the 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 with ultralow contact resistance. The
contact physics of the broader semimetal/monolayer-semiconductor family beyond
Bi/MoS, 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, WS,
MoSe, WSe, MoTe and WTe, 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 -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 -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
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