Genomics Studies of Two Cereal Rust Fungi with a Focus on Avirulence Gene Searches

The rust fungi are obligate biotrophic pathogens of a wide range of plants. The plant-pathogen interactions follow a gene for gene manner: a resistance (R) gene from plants can recognize a corresponding avirulence (Avr) gene from rust fungi. This thesis focused on searches for Avr gene in two rust fungi, the wheat stem rust pathogen Puccinia graminis f. sp. tritici (Pgt), and the barley leaf rust pathogen Puccinia hordei (Ph), with Next-generation sequencing (NGS) technologies. In Chapter 2, two Pgt isolates, one wildtype and one mutant derivative that differed in virulence to host R gene Sr50, were sequenced to identify the corresponding Avr gene AvrSr50. Genome comparison of the two isolates revealed amino acid-changing variations in 18 genes encoding haustorially-expressed secreted proteins. One of these genes was validated to encode the AvrSr50 protein, showing for the first time the effectiveness of Avr gene search via comparative genomics in rust fungi. In Chapter 3, a de novo genome assembly was performed for a Ph isolate Ph612, producing 15,913 scaffolds amounting to 127Mbp. A total of 16,354 genes were predicted, including 1,072 secreted protein-encoding genes. In Chapter 4, four additional Ph isolates derived from a same clonal lineage as Ph612 were studied. The five isolates differed in virulence for three barley R genes Rph3, Rph13 and Rph19. To identify the corresponding Avr genes AvrRph3, AvrRph13, and AvrRph19, the isolates were sequenced for genome comparisons, which revealed in 114, 99 and 120 candidates for the three Avr genes, respectively. The results and logical framework presented in this thesis have contributed to the knowledge pool of rust virulence, which will assist in development of novel resistance to these pathogens in both wheat and barley

Gr\"obner-Shirshov bases for LL-algebras

In this paper, we firstly establish Composition-Diamond lemma for $\Omega$-algebras. We give a Gr\"{o}bner-Shirshov basis of the free $L$-algebra as a quotient algebra of a free $\Omega$-algebra, and then the normal form of the free $L$-algebra is obtained. We secondly establish Composition-Diamond lemma for $L$-algebras. As applications, we give Gr\"{o}bner-Shirshov bases of the free dialgebra and the free product of two $L$-algebras, and then we show four embedding theorems of $L$-algebras: 1) Every countably generated $L$-algebra can be embedded into a two-generated $L$-algebra. 2) Every $L$-algebra can be embedded into a simple $L$-algebra. 3) Every countably generated $L$-algebra over a countable field can be embedded into a simple two-generated $L$-algebra. 4) Three arbitrary $L$-algebras $A$, $B$, $C$ over a field $k$ can be embedded into a simple $L$-algebra generated by $B$ and $C$ if $|k|\leq \dim(B*C)$ and $|A|\leq|B*C|$, where $B*C$ is the free product of $B$ and $C$.Comment: 22 page

Selection of Ovine Oocytes by Brilliant Cresyl Blue Staining

Sheep oocytes derived from the ovaries collected from the slaughterhouse are often used for research on in vitro embryo production, animal cloning, transgenesis, embryonic stem cells, and other embryo biotechnology aspects. Improving the in vitro culture efficiency of oocytes can provide more materials for similar studies. Generally, determination of oocyte quality is mostly based on the layers of cumulus cells and cytoplasm or cytoplasm uniformity and colors. This requires considerable experience to better identify oocyte quality because of the intense subjectivity involved (Gordon (2003), Madison et al. (1992) and De Loos et al. (1992)). BCB staining is a function of glucose-6-phosphate dehydrogenase (G6PD) activity, an enzyme synthesized in developing oocytes, which decreases in activity with maturation. Therefore, unstained oocytes (BCB−) are high in G6PD activity, while the less mature oocytes stains are deep blue (BCB+) due to insuffcient G6PD activity to decolorize the BCB dye

In-Domain GAN Inversion for Faithful Reconstruction and Editability

Generative Adversarial Networks (GANs) have significantly advanced image synthesis through mapping randomly sampled latent codes to high-fidelity synthesized images. However, applying well-trained GANs to real image editing remains challenging. A common solution is to find an approximate latent code that can adequately recover the input image to edit, which is also known as GAN inversion. To invert a GAN model, prior works typically focus on reconstructing the target image at the pixel level, yet few studies are conducted on whether the inverted result can well support manipulation at the semantic level. This work fills in this gap by proposing in-domain GAN inversion, which consists of a domain-guided encoder and a domain-regularized optimizer, to regularize the inverted code in the native latent space of the pre-trained GAN model. In this way, we manage to sufficiently reuse the knowledge learned by GANs for image reconstruction, facilitating a wide range of editing applications without any retraining. We further make comprehensive analyses on the effects of the encoder structure, the starting inversion point, as well as the inversion parameter space, and observe the trade-off between the reconstruction quality and the editing property. Such a trade-off sheds light on how a GAN model represents an image with various semantics encoded in the learned latent distribution. Code, models, and demo are available at the project page: https://genforce.github.io/idinvert/

RESEARCH ON QUANTIFICATION OF HAZOP DEVIATION BASED ON A DYNAMIC SIMULATION AND NEURAL NETWORK

Hazard and operability (HAZOP) analysis has become more significant as the complexity of process technology has increased. However, traditional HAZOP analysis has limitations in quantifying the deviations. This work introduces artificial neural networks (ANNs) and Aspen HYSYS to explore the feasibility of HAZOP deviation quantification. With the proposed HAZOP automatic hazard analyzer (HAZOP-AHA) method, the conventional HAZOP analysis of the target process is first carried out. Second, the HYSYS dynamic model of the relevant process is established to reflect the influence of process parameters on target parameters. Third, to solve the problem of deviation identification based on multi-attribute and a large dataset, we use the ANN to process the input data. Finally, HAZOP deviation can be quantified and predicted. The method is verified by the industrial alkylation of benzene with propene to cumene. The results show that the predicted deviation severity can be close to the actual deviation severity, and the accuracy of prediction can reach nearly 100%. Thus, the method can diminish the probability of conflagration, burst, and liquid leakage

REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos

Reconstructing dynamic 3D garment surfaces with open boundaries from monocular videos is an important problem as it provides a practical and low-cost solution for clothes digitization. Recent neural rendering methods achieve high-quality dynamic clothed human reconstruction results from monocular video, but these methods cannot separate the garment surface from the body. Moreover, despite existing garment reconstruction methods based on feature curve representation demonstrating impressive results for garment reconstruction from a single image, they struggle to generate temporally consistent surfaces for the video input. To address the above limitations, in this paper, we formulate this task as an optimization problem of 3D garment feature curves and surface reconstruction from monocular video. We introduce a novel approach, called REC-MV, to jointly optimize the explicit feature curves and the implicit signed distance field (SDF) of the garments. Then the open garment meshes can be extracted via garment template registration in the canonical space. Experiments on multiple casually captured datasets show that our approach outperforms existing methods and can produce high-quality dynamic garment surfaces. The source code is available at https://github.com/GAP-LAB-CUHK-SZ/REC-MV.Comment: CVPR2023; Project Page:https://lingtengqiu.github.io/2023/REC-MV

Anesthesia Services in the Time of COVID

Our hospital is a 400-bed Level-1 trauma center with 78 ICU beds serving the greater Louisville metropolitan area. The COVID-19 pandemic forced our hospital to re-evaluate our core business operations and to develop a coherent response to a fluid situation. Between March 15 and May 15, 2020, the University of Louisville Hospital admitted more than 100 COVID-19 inpatients, approximately 30 were admitted to the intensive care unit (ICU) and most required endotracheal intubation. The following review describes our Department of Anesthesiology & Perioperative Medicine foci, actions and rationale during the COVID-19 pandemic. While we hope not to experience another pandemic in the near future, this review may be a helpful starting point for preparing for future respiratory spread pandemics