12,038 research outputs found
Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics
MOTIVATION: Gene expression is characterised by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data is prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. RESULTS: Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both the cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in a supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful when applied to both allele and non-allele-specific scRNA-seq data. AVAILABILITY: The code for Neural Network and Approximate Bayesian Computation inference is available at https://github.com/WT215/nnRNA and https://github.com/WT215/Julia_ABC respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online
Development of Flame Retardant and Antibacterial Dual Functionalised Flexible Polyurethane Foam
Flexible Polyurethane foam (PUF), with its unique properties, such as lightweight and softness, has been utilised extensively. Nevertheless, owing to the intrinsic high flammability and low ignition temperature, PUF-associated fire risks are always a concern. During PUF’s combustion, excessive heat and toxic gases can be generated, threatening the health and life of human beings and causing huge property loss. Consequently, improving the flame retardancy of the PUF is of importance. Later, the global COVID-19 pandemic broke out in 2019, leading to the public’s increased awareness of maintaining good hygiene conditions. Since PUF products are frequently in contact with humans daily, rendering the PUF with bacterial-killing properties should also be addressed.
This dissertation delivers studies on introducing flame retardancy to the PUF via a surface engineering method named the layer-by-layer (LbL) assembly. Due to the consequent COVID-19 situation, this thesis expands the investigations to endow the PUF with antibacterial performances. Preliminary research on fabricating a newly emerged two-dimensional material called MXene (Ti3C2) and chitosan (CH) as flame retardants (FRs) to impart fire safety performances to the PUF was conducted. With only 6.9 wt.% mass added to the PUF, unprecedented fire resistance and smoke suppression properties were received. It was revealed that the FR mechanism was ascribed to the hybrid coating’s excellent barrier and carbonisation effects. Further investigations on improving the PUFs’ biodegradability identified synergistic effects between the MXene with the CH and phytic acid, demonstrating the great potential for reducing the toxicity and improving the eco-friendliness of the PUFs. Additionally, this thesis analysed the FR and antibacterial dual-functionalised PUFs. The synthesised MXene, CH, and silver ion hybridised coating endows the foam with exceptional bactericidal properties with decreases of 99.7 % in gram-negative bacteria and 88.9 % in gram-positive bacteria compared with the unmodified counterpart. Excellent flame retardancy possessed by the dual-functionalised PUFs was discovered. The compatibility of the two functional coatings was evaluated and confirmed. The results manifest the great potential for eradicating the fire risks of PUFs and providing traditional PUF products with antibacterial properties, further expanding PUF’s applications
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Many Physical Design Problems are Sparse QCQPs
Physical design refers to mathematical optimization of a desired objective
(e.g. strong light--matter interactions, or complete quantum state transfer)
subject to the governing dynamical equations, such as Maxwell's or
Schrodinger's differential equations. Computing an optimal design is
challenging: generically, these problems are highly nonconvex and finding
global optima is NP hard. Here we show that for linear-differential-equation
dynamics (as in linear electromagnetism, elasticity, quantum mechanics, etc.),
the physical-design optimization problem can be transformed to a sparse-matrix,
quadratically constrained quadratic program (QCQP). Sparse QCQPs can be tackled
with convex optimization techniques (such as semidefinite programming) that
have thrived for identifying global bounds and high-performance designs in
other areas of science and engineering, but seemed inapplicable to the design
problems of wave physics. We apply our formulation to prototypical photonic
design problems, showing the possibility to compute fundamental limits for
large-area metasurfaces, as well as the identification of designs approaching
global optimality. Looking forward, our approach highlights the promise of
developing bespoke algorithms tailored to specific physical design problems.Comment: 9 pages, 4 figures, plus references and Supplementary Material
Leveraging a machine learning based predictive framework to study brain-phenotype relationships
An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the overarching question of how to best structure and run experiments ambiguous. In this work, I cover two explicit pieces of this larger question, the relationship between data representation and predictive performance and a case study on issues related to data collected from disparate sites and cohorts. I then present the Brain Predictability toolbox, a soft- ware package to explicitly codify and make more broadly accessible to researchers the recommended steps in performing a predictive experiment, everything from framing a question to reporting results. This unique perspective ultimately offers recommen- dations, explicit analytical strategies, and example applications for using machine learning to study the brain
Spatial and temporal hierarchical decomposition methods for the optimal power flow problem
The subject of this thesis is the development of spatial and temporal decomposition
methods for the optimal power flow problem, such as in the transmissiondistribution
network topologies. In this context, we propose novel decomposition
interfaces and effectivemethodology for both the spatial and temporal dimensions
applicable to linear and non-linear representations of the OPF problem.
These two decomposition strategies are combined with a Benders-based algorithmand
have advantages in model building time, memory management and solving
time. For example, in the 2880-period linear problems, the decomposition finds
optimal solutions up to 50 times faster and allows even larger instances to be solved;
and in multi-period non-linear problems with 48 periods, close-to-optimal feasible
solutions are found 7 times faster.
With these decompositions, detailed networks can be optimized in coordination,
effectively exploiting the value of the time-linked elements in both transmission and
distribution levels while speeding up the solution process, preserving privacy, and
adding flexibility when dealing with different models at each level.
In the non-linear methodology, significant challenges, such as active set determination,
instability and non-convex overestimations, may hinder its effectiveness,
and they are addressed, making the proposed methodology more robust and stable.
A test network was constructed by combining standard publicly available networks
resulting in nearly 1000 buses and lines with up to 8760 connected periods;
several interfaces were presented depending on the problemtype and its topology
using a modified Benders algorithm. Insight was given into why a Benders-based
decomposition was used for this type of problem instead of a common alternative:
ADMM.
The methodology is useful mainly in two sets of applications: when highly detailed
long-termlinear operational problems need to be solved, such as in planning
frameworks where the operational problems solved assume no prior knowledge; and
in full AC-OPF problems where prior information from historic solutions can be used
to speed up convergence
Feature Papers in Compounds
This book represents a collection of contributions in the field of the synthesis and characterization of chemical compounds, natural products, chemical reactivity, and computational chemistry. Among its contents, the reader will find high-quality, peer-reviewed research and review articles that were published in the open access journal Compounds by members of the Editorial Board and the authors invited by the Editorial Office and Editor-in-Chief
Intelligent computing : the latest advances, challenges and future
Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing
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