Deep learning models for modeling cellular transcription systems

Cellular signal transduction system (CSTS) plays a fundamental role in maintaining homeostasis of a cell by detecting changes in its environment and orchestrates response. Perturbations of CSTS lead to diseases such as cancers. Almost all CSTSs are involved in regulating the expression of certain genes and leading to signature changes in gene expression. Therefore, the gene expression profile of a cell is the readout of the state of its CSTS and could be used to infer CSTS. However, a gene expression profile is a convoluted mixture of the responses to all active signaling pathways in cells. Therefore it is difficult to find the genes associated with an individual pathway. An efficient way of de-convoluting signals embedded in the gene expression profile is needed. At the beginning of the thesis, we applied Pearson correlation coefficient analysis to study cellular signals transduced from ceramide species (lipids) to genes. We found significant correlations between specific ceramide species or ceramide groups and gene expression. We showed that various dihydroceramide families regulated distinct subsets of target genes predicted to participate in distinct biologic processes. However, it’s well known that the signaling pathway structure is hierarchical. Useful information may not be fully detected if only linear models are used to study CSTS. More complex non-linear models are needed to represent the hierarchical structure of CSTS. This motivated us to investigate contemporary deep learning models (DLMs). Later, we applied various deep hierarchical models to learn a distributed representation of statistical structures embedded in transcriptomic data. The models learn and represent the hierarchical organization of transcriptomic machinery. Besides, they provide an abstract representation of the statistical structure of transcriptomic data with flexibility and different degrees of granularity. We showed that deep hierarchical models were capable of learning biologically sensible representations of the data (e.g., the hidden units in the first hidden layer could represent transcription factors) and revealing novel insights regarding the machinery regulating gene expression. We also showed that the model outperformed state-of-the-art methods such as Elastic-Net Linear Regression, Support Vector Machine and Non-Negative Matrix Factorization

Investigation on the feasibility of trifluoroiodomethane (cf3i) for application in gas-insulated lines

It is widely acknowledged that the world needs to reduce the level of greenhouse gas emissions. It is proposed to use potentially cleaner renewable energy sources to replace fossil fuels, and thus reduce greenhouse gas emissions. A significant challenge facing renewable energy sources, however, is that the power generation facilities are often located far from the load centres, meaning that new high capacity long-distance transmission systems would need to be built. This is a particular issue since there are increasing difficulties in obtaining approval to construct new overhead lines (OHL). An alternative is gas-insulated lines (GIL), a system for the transmission of electricity over long distance and is considered as a viable technical solution in places where OHL cannot be constructed. The currently adopted gas medium in GIL, however, is sulphur hexafluoride (SF6), which is a potent greenhouse gas. Trifluoroiodomethane (CF3I) has been proposed as an alternative insulation medium to SF6 in GIL, and this thesis investigates the potential of using a CF3I gas mixture in GIL applications. It is hoped that the research can lead to a new form of environmentally friendly power transmission system that could cope with the increasing power demand in large metropolitan areas, and contribute to the reduction of SF6 usage in the high-voltage industry. The literature survey reviewed the research work on CF3I gas and its mixtures to date. Several research gaps were identified, and these informed the investigations carried out in this research. Reduced-scale coaxial test systems with the electric field properties of a full-scale 400 kV GIL were designed, developed and fabricated. The designs were simulated using COMSOL to ensure that the highest field would be along the centre of the conductor. The effective ionisation coefficients of various CF3I gas mixtures were calculated using BOLSIG+, which provided estimated values for the critical reduced field strength of each gas mixture. Extensive laboratory tests using a standard lightning impulse (1.2/50) were conducted on the fabricated prototypes using various CF3I/CO2 and CF3I/N2 gas mixtures to determine the 50% breakdown voltage. The breakdown characteristics of CF3I gas mixtures were examined for pressure, geometric ratio, impulse polarity, buffer gas and mixture content. Based on the measured breakdown voltage and calculated critical reduced field strength of various CF3I gas mixtures, a two-stage streamer/leader mathematical model was developed to evaluate the reduction in field strength at higher pressures. A comparative study was carried out on CF3I gas mixtures in a rod-plane electrode configuration under standard lightning impulse and steep-front square impulse waveforms. This investigation focused on the V-t characteristics of CF3I gas mixtures in this particular configuration. A phase equilibrium experiment was also carried out to determine the boiling point of various CF3I gas mixtures

Breakdown of CF3I Gas and its Mixtures under Lightning Impulse in Coaxial-GIL Geometry

SF6 is widely used in modern transmission and distribution networks because of its outstanding dual qualities: arc quenching and dielectric insulation. As a gas medium, SF6 is chemically inert, non-toxic, and non-flammable, which makes possible the construction of compact SF6 switchgear. One major known disadvantage of the gas is that it has a global warming potential which is 23,900 times higher than CO2. This has led to research into alternative gases with a much lower environmental impact, and one of the emerging candidates is CF3I. The high boiling temperature of CF3I means that it has to be used as part of a mixture inside gas-insulated equipment. To carry out the investigation on CF3I, a scaled-down coaxial system that replicates the maximum electric field of a 400 kV GIL system was designed and fabricated. The insulation performances of CF3I/CO2 and CF3I/N2 gas mixtures were then examined by measuring the 50% breakdown voltage, U50, using a standard lightning impulse waveform (1.2/50) under absolute pressures of 1 to 4 bar. The experimental results show that CF3I gas mixtures have promising potential as an insulation medium for application in gasinsulated lines

Incremental Collaborative Beam Alignment for Millimeter Wave Cell-Free MIMO Systems

Millimeter wave (mmWave) cell-free MIMO achieves an extremely high rate while its beam alignment (BA) suffers from excessive overhead due to a large number of transceivers. Recently, user location and probing measurements are utilized for BA based on machine learning (ML) models, e.g., deep neural network (DNN). However, most of these ML models are centralized with high communication and computational overhead and give no specific consideration to practical issues, e.g., limited training data and real-time model updates. In this paper, we study the {probing} beam-based BA for mmWave cell-free MIMO downlink with the help of broad learning (BL). For channels without and with uplink-downlink reciprocity, we propose the user-side and base station (BS)-side BL-aided incremental collaborative BA approaches. Via transforming the centralized BL into a distributed learning with data and feature splitting respectively, the user-side and BS-side schemes realize implicit sharing of multiple user data and multiple BS features. Simulations confirm that the user-side scheme is applicable to fast time-varying and/or non-stationary channels, while the BS-side scheme is suitable for systems with low-bandwidth fronthaul links and a central unit with limited computing power. The advantages of proposed schemes are also demonstrated compared to traditional and DNN-aided BA schemes.Comment: 15 pages, 15 figures, to appear in the IEEE Transactions on Communications, 202

CF3I Gas Mixtures: Breakdown Characteristics and Potential for Electrical Insulation

SF6 is a potent greenhouse gas, and there has been research into more environmentally friendly alternative gases with the aim of replacing the use of SF6 gas in high-voltage equipment. So far, the research into alternative gases has shown that CF3I gas mixtures have promising dielectric properties comparable to those of SF6. This paper provides an overview of research into CF3I gas and its mixtures, and gives an insight into its key properties. These include laboratory tests on the gas mixtures and initial applications to electrical power equipment. The insulation capability makes CF3I a feasible alternative to SF6 as an insulation medium where arc quenching is not required. On the other hand, iodine deposition after electrical discharge means CF3I may not be a suitable arc quenching gas for switchgear applications that require high current interruption unless a solution is found for controlled capture of the iodine

Contrastive Shapelet Learning for Unsupervised Multivariate Time Series Representation Learning

Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable representation for many downstream tasks without using inaccessible labels. However, existing approaches usually adopt the models originally designed for other domains (e.g., computer vision) to encode the time series data and rely on strong assumptions to design learning objectives, which limits their ability to perform well. To deal with these problems, we propose a novel URL framework for multivariate time series by learning time-series-specific shapelet-based representation through a popular contrasting learning paradigm. To the best of our knowledge, this is the first work that explores the shapelet-based embedding in the unsupervised general-purpose representation learning. A unified shapelet-based encoder and a novel learning objective with multi-grained contrasting and multi-scale alignment are particularly designed to achieve our goal, and a data augmentation library is employed to improve the generalization. We conduct extensive experiments using tens of real-world datasets to assess the representation quality on many downstream tasks, including classification, clustering, and anomaly detection. The results demonstrate the superiority of our method against not only URL competitors, but also techniques specially designed for downstream tasks. Our code has been made publicly available at https://github.com/real2fish/CSL