Microscale quantification of mycosphere pH and oxygen as drivers of bacterial fungal interactions

Fungi and bacteria co-inhabit a wide variety of habitats, and their interactions are significant drivers of many ecosystem services and functions. Creating unique microenvironments, fungal mycelia and their surroundings (defined here as the mycosphere) allow for spatially distinct fungal bacterial activities and interactions at the microscale. Fungi in particular modulate the mycosphere pH and oxygen as the drivers and/or the results of various fungal processes. However, due to the microscopic diameters of hyphae (typically 2-10 μm), it is experimentally difficult to non-invasively access themycosphere to thereby analyze the local pH and oxygen on hyphae or around mycelia. Hence, in this thesis, I aimed to develop and deploy microscale techniques to analyzethe mycosphere pH and oxygen in vitroand thereby to further resolve their influences on the local microbial life for a better understanding of mycosphere habitat properties and functioning

Performances of Symmetric Loss for Private Data from Exponential Mechanism

This study explores the robustness of learning by symmetric loss on private data. Specifically, we leverage exponential mechanism (EM) on private labels. First, we theoretically re-discussed properties of EM when it is used for private learning with symmetric loss. Then, we propose numerical guidance of privacy budgets corresponding to different data scales and utility guarantees. Further, we conducted experiments on the CIFAR-10 dataset to present the traits of symmetric loss. Since EM is a more generic differential privacy (DP) technique, it being robust has the potential for it to be generalized, and to make other DP techniques more robust.Comment: 14th International Workshop on Parallel and Distributed Algorithms and Applications (PDAA2022

End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion

Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model knowledge graphs. The model can be efficiently trained and scalable to large knowledge graphs. However, there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure. In this work, we propose a novel end-to-end Structure-Aware Convolutional Network (SACN) that takes the benefit of GCN and ConvE together. SACN consists of an encoder of a weighted graph convolutional network (WGCN), and a decoder of a convolutional network called Conv-TransE. WGCN utilizes knowledge graph node structure, node attributes and edge relation types. It has learnable weights that adapt the amount of information from neighbors used in local aggregation, leading to more accurate embeddings of graph nodes. Node attributes in the graph are represented as additional nodes in the WGCN. The decoder Conv-TransE enables the state-of-the-art ConvE to be translational between entities and relations while keeps the same link prediction performance as ConvE. We demonstrate the effectiveness of the proposed SACN on standard FB15k-237 and WN18RR datasets, and it gives about 10% relative improvement over the state-of-the-art ConvE in terms of HITS@1, HITS@3 and [email protected]: The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019

In silico prediction of housekeeping long intergenic non-coding RNAs reveals HKlincR1 as an essential player in lung cancer cell survival

Prioritising long intergenic noncoding RNAs (lincRNAs) for functional characterisation is a significant challenge. Here we applied computational approaches to discover lincRNAs expected to play a critical housekeeping (HK) role within the cell. Using the Illumina Human BodyMap RNA sequencing dataset as a starting point, we first identified lincRNAs ubiquitously expressed across a panel of human tissues. This list was then further refined by reference to conservation score, secondary structure and promoter DNA methylation status. Finally, we used tumour expression and copy number data to identify lincRNAs rarely downregulated or deleted in multiple tumour types. The resulting list of candidate essential lincRNAs was then subjected to co-expression analyses using independent data from ENCODE and The Cancer Genome Atlas (TCGA). This identified a substantial subset with a predicted role in DNA replication and cell cycle regulation. One of these, HKlincR1, was selected for further characterisation. Depletion of HKlincR1 affected cell growth in multiple lung cancer cell lines, and led to disruption of genes involved in cell growth and viability. In addition, HKlincR1 expression was correlated with overall survival in lung adenocarcinoma patients. Our in silico studies therefore reveal a set of housekeeping noncoding RNAs of interest both in terms of their role in normal homeostasis, and their relevance in tumour growth and maintenance

Transcriptional and epigenetic regulation of oestrogen signalling in breast cancer cells

Breast cancer is a common disease in women and has major impacts on health and quality of life. About 70% of breast cancers over express ERα, and are classified as ER positive breast cancer. Oestrogen receptor alpha (ERα) belongs to the nuclear receptor superfamily and is responsible for many effects of oestrogen on normal and cancerous breast tissue. Endocrine therapies that block the function of ERα or the synthesis of oestrogen have been a mainstay of ERα positive breast cancer treatment. However, their efficacy is limited by intrinsic and acquired drug resistance overtime, and endocrine resistance remains one of the biggest challenges in breast cancer treatment. In order to investigate the underlying mechanisms of acquired drug resistance, and to develop new strategies for breast cancer therapy, I generated a novel long-term oestrogen deprived cell line (DH) in serum-free condition. As DH cells are cultured in a defined media with known concentrations of growth factors, it provides an ideal system to identify and dissect changes in signalling pathways in response to hormones and inhibitors in vitro. At the same time, DH cells are representative of ER positive breast cancers treated with drugs that reduce the level of oestrogen. It enables the identification of survival pathways that could be activated during oestrogen deprivation. By using this cell model, I find that oestrogen stimulation enables cells to up-regulate the EGFR level and simultaneously reduces ERα expression at both mRNA and protein levels. Once up-regulated, EGFR expression is maintained despite oestrogen withdrawal indicating a stable transcriptional re-programming at the EGFR promoter. By using the whole genome expression microarrays, I identified a list of genes that also show stable changes in gene expression in response to oestrogen, suggesting that the oestrogen promotes transcriptional re-programming at multiple pathways in cells. In terms of signalling pathways, oestrogen activates the growth promoting MAPK pathway in an EGFR dependent manner and a 5-day oestrogen pulse substantially increases the resistance of cells to tamoxifen, while cells remain sensitive to the EGFR inhibitor, demonstrating a functional switch between ERα and EGFR survival pathways. Furthermore, microarray analysis of ERα and EGFR downstream target genes shows that there is a general activation of MAPK gene signature after 5 days of oestrogen stimulation in DH cells. In this thesis, I also investigate the molecular mechanism of oestrogen induced EGFR up-regulation in ER positive breast cancer cells. c-Myb is an oestrogen responsive transcription factor whose expression is regulated by ERα in breast cancer cells. I demonstrate that oestrogen treatment leads to ERα dependent c-Myb up-regulation in DH cells. I also find that c-Myb transiently locates upstream of the EGFR promoter to enhance its expression. As the up-regulation of EGFR in ER positive breast cancer could lead to survival pathway switching and endocrine therapy resistance, c-Myb could be a good drug target to prevent the likelihood these switches and subsequent relapse on endocrine therapies. The expression of EGFR remains high after the removal of oestrogen suggesting there may be epigenetic changes, which maintain the transcriptional re-programming stimulated by c-Myb. Bisulphite sequencing however demonstrates EGFR promoter DNA methylation pattern is not affected by oestrogen. Meanwhile, ChIP microarrays with four different histone modifications show no significant changes around the promoter area of EGFR in response to oestrogen. These observations suggest that alternative epigenetic modifications or epigenetic alternations at other genes may subsequently lead to the stable expression of EGFR in response to oestrogen

3-Ethyl-1H-1,2,4-triazole-5(4H)-thione

The mol­ecule of the title compound, C4H7N3S, exists as the thione tautomer in the solid state. The asymmetric unit consits of one mol­ecule in which all atoms are located on a crystallographic mirror plane. In the crystal, adjacent mol­ecules are linked by N—H⋯N and N—H⋯S hydrogen bonds into chains running along the a axis. π–π stacking inter­actions between the triazole rings [centroid–centroid distance = 3.740 (1) Å and inter­planar distance = 3.376 Å] may further stabilize the structure