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Recombinant adeno-associated virus (AAV) vectors are the leading platform for gene delivery for a variety of clinical applications. Patients with preexisting antibodies to AAV are currently excluded from most AAV gene therapy trials to avoid vector neutralization and ensure response to therapy. Anti-AAV neutralizing antibodies (NAbs) are typically assessed by in vitro cell-based transduction inhibition (TI) assays. However, clinical relevance of the determined enrollment cutoff and the inherent variability of a cell-based assay present challenges for use as an enrollment screening test. Here, we describe an enrollment cutoff that was clinically validated and strategies to overcome assay challenges to enable long-term stable performance. A validated anti-AAV6 cell-based TI assay was used to support clinical enrollment across multiple investigational gene therapies and to evaluate AAV6 seroprevalence in healthy and disease populations. The clinical enrollment cutoff was determined statistically using samples collected from healthy donors, applying a 0.1% false error rate with the inclusion of a minimum significant ratio (MSR) metric and in consideration of results from in vivo mouse passive transfer studies. Our strategy for long-term monitoring and control of assay performance employed plate quality control samples flanking the predefined cutoff. An approach using donor samples was implemented to bridge different lots of critical reagents without the need to redefine the cutoff.

A novel concept is described here that utilizes externally applied heat to a solid rod of reconstituted tobacco biomass to form a stream of aerosol under progressively oxygen-deficient atmosphere. The boundary of auto-ignition was determined at oxygen concentrations of 0%, 5%, 10%, and 21% and then the effects of these different atmospheres on various parameters were studied. Experimental results indicated that the ignition temperature decreased with the increase of oxygen concentration and a negative temperature coefficient (NTC) for atmosphere with oxygen was observed at before but close to ignition temperature. Significant differences in the yields of CO and CO between oxygen and oxygen-free atmosphere were observed. The mass of aerosol produced under an inert atmosphere and reduced-oxygen atmosphere were characterized with selected volatile and semi-volatile components, phenols, aldehydes, and other organic compounds of interests. In addition, higher oxygen concentration resulted in higher bulk and surface oxygen content of solid-phase residue, the reduction of carbon and hydrogen element content and related functional groups, and the content of inorganic compounds also exhibited an increase with oxygen concentration. By systematically changing oxygen concentrations of the biomass bed with increasing temperatures, an aerosol stream of controlled chemical composition and yields could be formed without leading to ignition. Key chemical markers of inhalation harm were measured and compared to each other under different degrees close to combustion. Studying reconstituted tobacco or other biomass materials in such a way could provide alternative and useful information in the design heated biomass aerosol generators.

A lit cigarette forms a positive and negative pressure zone by a puff with respect to the position of its paper burn line. Smoke aerosols generated from the two zones are then pulled through the rod under the puff to form the mainstream smoke. This phenomenon is fundamental to the thermophysics and the resultant chemical composition of the mainstream smoke. In this study, we created two different airflow pathways inside a heated tobacco rod by a puff, and investigated the differences in aerosol formation and its chemical compositions. The two different pressure-induced conditions, one through an open-ended tobacco rod (marked as HNB, a label of a designed airflow pathway of commercial heated tobacco products called heat-not-burn prior), and the other through a closed-ended tobacco rod (marked as NSC, a label of a novel-designed airflow pathway of heated tobacco products), were compared for their aerosol collected mass (ACM), the contents of nicotine, water and added aerosol agents such as propylene glycol (PG) and glycerol (VG), as well as selected aldehydes and ketones in the mainstream aerosol. Aerosol particle distribution and the heated temperature along different rod locations were also compared during a puff. The results indicated marked differences in the aerosol formation processes between the two HNB and NSC systems. The transfer ratios of the main aerosol components were significantly higher for the NSC; the levels of formaldehyde and acetaldehyde were significantly lower under the NSC than the HNB condition. There were also significant differences for the aerosol particle number concentration (APNC) and count median diameter (CMD) for the two systems. The lack of convective heat transfer in the aerosol formation under the NSC condition resulted in a relatively stable thermal aerosol generation zone, reflected by the temperature difference between the two systems in the selected locations. The NSC mode of tobacco heating thus offers a novel and enhanced aerosol generation for heat tobacco product designs.

The main drawbacks of traditional densely connected convolution networks (DenseNet) lie in: complex network models, excessive parameters, a large amount of computational and storage resources, falling into the problem of over-fitting, resulting in low object recognition accuracy. In addition, in the field of fine-grained image classification, the recognition performance is insufficient due to the inadequate representation capability of extracting features. In order to cope with these problems, we propose a novel shallow densely connected convolution networks (called DenseNet-S), it works as: (1) we adopt a shallow network training strategy to degrade the computational complexity and reduce the parameters, in order to avoid excessive number of layers affecting the recognition accuracy; (2) we propose a novel squeeze method to further reduce the network parameters and effectively alleviate the over-fitting phenomena. In addition, we apply the fire module and add the squeeze layer and the expand layer to the convolution module in DenseNet; (3) we employ the factorization technique into small convolutions, which can partition a large two-dimensional convolution into two small one-dimensional convolutions, in order to improve the feature extraction capability and the recognition performance in fine-grained image classification. The effectiveness of DenseNet-S was evaluated by extensive experiments on three benchmark datasets including CIFAR-10, CIFAR-100 and SVHN.

Large-scale fine-grained image retrieval (FGIR) aims to retrieve images belonging to the same subcategory as a given query by capturing subtle differences in a large-scale setting. Recently, Vision Transformers (ViT) have been employed in FGIR due to their powerful self-attention mechanism for modeling long-range dependencies. However, most Transformer-based methods focus primarily on leveraging self-attention to distinguish fine-grained details, while overlooking the high computational complexity and redundant dependencies inherent to these models, limiting their scalability and effectiveness in large-scale FGIR. In this paper, we propose an Efficient and Effective ViT-based framework, termed EET, which integrates token pruning module with a discriminative transfer strategy to address these limitations. Specifically, we introduce a content-based token pruning scheme to enhance the efficiency of the vanilla ViT, progressively removing background or low-discriminative tokens at different stages by exploiting feature responses and self-attention mechanism. To ensure the resulting efficient ViT retains strong discriminative power, we further present a discriminative transfer strategy comprising both discriminative knowledge transfer and discriminative region guidance. Using a distillation paradigm, these components transfer knowledge from a larger ``teacher'' ViT to a more efficient ``student'' model, guiding the latter to focus on subtle yet crucial regions in a cost-free manner. Extensive experiments on two widely-used fine-grained datasets and four large-scale fine-grained datasets demonstrate the effectiveness of our method. Specifically, EET reduces the inference latency of ViT-Small by 42.7\\% and boosts the retrieval performance of 16-bit hash codes by 5.15\\% on the challenging NABirds dataset.

Zebrafish （Danio rerio）, a small tropical freshwater fish native to east India, has rapidly emerged as a popular vertebrate model organism in the last two decades. There are multiple advantages of using zebrafish in biological research, including its small size, short generation time of 2-3 months, external fertilization and development, high fecundity without seasonal variation, transparent embryos, and inexpensive maintenance. The well-annotated zebrafish genome contains more than 26,000 protein-coding genes, sharing over 80% similarity with human genome, and approximately 70% of human genes have its zebrafish orthologues. Drug administration in zebrafish can be as simple as adding chemicals into fish water, and therefore it is doable to design projects with a small budget compared to mice and rats. Zebrafish is also famous for its amazing regenerative abilities, for instance, it can regenerate fins, hair cells, retina, even heart and brain. All these features make zebrafish a valuable animal model to dissect the mechanisms of biological processes and human diseases [1,2].

Notch, a type I transmembrane receptor, is subjected to proteolysis upon ligand binding to release its intracellular domain (NICD). NICD acts as the messenger, translocating into nucleus to activate the downstream target genes. Notch mediates a variety of biological functions in all metazoans including cell-fate selection, proliferation and differentiation. While much has been learned about the Notch signaling pathway through years of genetic, developmental and biochemical studies, some fundamental questions still remain largely unanswered: How is ligand-dependent activation regulated? How is Notch cleaved within its transmembrane domain? How is Notch signaling utilized in specific biological environments? This dissertation addressed several aspects of these questions by analyzing Notch signaling in three different experimental systems. The Presenilin-dependent γ-secretase is responsible for proteolytic processing of the Notch and other substrates within their transmembrane domains. I have reconstituted γ-secretase in yeast by co-expressing all the four components of γ-secretase with a transmembrane substrate form of mouse Notch1, thus building a reporter system to select for the Notch transmembrane cleavage in the single-cell eukaryotic yeast Saccharomyces cerevisiae . This system will be used by others in the lab to explore the mechanistic aspects of Notch cleavage. Previous studies from several genetic systems suggest that Notch signaling may be regulated by endocytosis. I attempted to address this question in the mammalian tissue culture system, by overexpressing dominant-negative forms of basic endocytic components, Dynamin and several Rab proteins. My experimental results implied that the activation of mammalian Notch may not be Dynamin-dependent, at least in the tissue culture environment. The biological functions of Notch signaling in vertebrate organogenesis were studied in two mouse model systems, the hair follicle and the limb. The availability of floxed alleles of Notch1, 2 and Presenilin permitted me to conduct a comprehensive loss of function analysis in a stage- and tissue specific manner using Msx2-Cre as the deletor gene. I discovered that Presenilin-dependent Notch signaling plays distinctive roles in each of the three related epithelial lineages including the epidermis, the hair follicles and the sebaceous glands. When Notch was removed from the Apical Ectoderm Ridge (AER), limb syndactyly with variable penetrance was observed, suggesting a contribution of Notch signaling to the soft tissue formation of the mouse limb. In summary, my experiments suggest that Notch signaling participates in a context dependent way in the development of many vertebrate tissues. At the cellular level, my studies laid the groundwork for detailed biochemical analysis of key events in Notch signaling.

Peripheral neuropathies are estimated to affect several million patients in the US with no long-lasting therapy currently available. In humans, the Nav1.7 sodium channel, encoded by the SCN9A gene, is involved in a spectrum of inherited neuropathies, and has emerged as a promising target for analgesic drug development. The development of a selective Nav1.7 inhibitor has been challenging, in part due to structural similarities among other Nav channels. Here we present preclinical studies for the first genomic medicine approach using engineered zinc finger repressors (ZFRs) specifically targeting human/nonhuman primate (NHP) SCN9A. AAV-mediated delivery of ZFRs in human iPSC-derived neurons resulted in 90% reduction of SCN9A with no detectable off-target activity. To establish proof-of-concept, a ZFR targeting the mouse Scn9a was assessed in the SNI neuropathic pain mouse model, which resulted in up to 70% repression of Scn9a in mouse DRGs and was associated with reduction in pain hypersensitivity as measured by increased mechanical- and cold-induced pain thresholds. AAV-mediated intrathecal delivery of ZFR in NHPs demonstrated up to 60% repression of SCN9A in bulk DRG tissue and on single-cell levels in the nociceptors. The treatment was well tolerated in NHPs, and no dose-limiting findings were observed four weeks after a single intrathecal injection. Taken together, our results demonstrate that AAV-delivered ZFR targeting the SCN9A gene is promising and supports further development as a potential therapy for peripheral neuropathies.

Prompt and accurate precipitation forecast is very important for development management of regional water resource, flood disaster prevention and people's daily activity and production plan; however, non-linear and nonstationary characteristics of precipitation data and noise seriously affect forecast accuracy. This paper combines multicellular gene expression programming with more powerful function mining ability and wavelet analysis with more powerful denoising and extracting data fine feature capability for precipitation forecast modeling, proposing to estimate meteorological precipitation with WTGEPRP algorithm. Comparative result for simulation experiment with actual precipitation data in Zhengzhou, Nanning and Melbourne in Australia indicated that: fitting and forecasting performance of WTGEPRP algorithm is better than the algorithm Multicellular Gene Expression Programming-based Hybrid Model for Precipitation Prediction Coupled with EMD, Supporting Vector Regression, BP Neural Network, Multicellular Gene Expression Programming and Gene Expression Programming, and has good application prospect.

In this dissertation, which consists of three independent essays, we study issues concerned with capital markets and corporate financing decisions. Essay One, \"Design and Valuation of Corporate Securities with Stochastic Interest Rates,\" examines two related issues about corporate debt, the valuation and design, in a world where interest rate fluctuates. In particular, we are interested in pricing callable and convertible bonds and in understanding what types of firms will issue debt with call provision and what types will use convertible debt. Essay Two, \"Design and Valuation of Corporate Securities with Strategic Debt Service and Asymmetric Information,\" studies the valuation of corporate securities and the optimal financing decision by a firm in a continuous-time setting under asymmetric information. By integrating strategic debt service (which arises from the presence of liquidation costs) and asymmetric information in a unified analytical framework, this essay considers the two essential factors which influence the corporate financing decision. In particular, the essay addresses both the valuation of securities and characterizes the circumstances under which a firm may prefer equity to debt or vice versa. One of the basic results of this essay is that the risk premium of debt could be significantly increased by the asymmetric information effect. This part of risk premium could be particularly significant for those good firms with a project which will produce much higher cash flows than what the market expects. We also find that a firm's financing decision depends on its primitives: firms are more apt to rely on equity if they have: (1) high growth potential, (2) riskier project, (3) higher ratio of intangible assets to total assets and (4) lesser information asymmetry; firms would prefer debt, otherwise. Essay Three, \"An Empirical Investigation on Swaps: International Evidence,\" is devoted to investigating empirical evidence in international swap markets. Especially, we examine the relationship between interest rate swaps in two currencies. By using daily swap quotes on sterling and dollar interest rate swaps, we compare the actual US swap rates with those implied from sterling swaps. (Abstract shortened by UMI.)