From natural to eximious : harnessing the power of natural killer cells against solid tumors

Cancer heterogeneity, which enables clonal survival and treatment resistance, is shaped by active immune responses. Unchallenged results from clinical trials show the power of stimulating our immune system to attack tumor cells. Engineered T cells and checkpoint blockade are at the forefront of current immunotherapy strategies. Whereas our immune system includes a diverse range of effector cells, which could directly or indirectly kill the target cells, and these immune cells must organize in a synergistic way to overcome multiple immune-evasion mechanisms and achieve complete tumor eradication. An essential type of effector cell is natural killer (NK) cell. These are cytotoxic innate lymphocytes identified by their splendid capacity to kill virus-infected, stressed or transformed cells. Ex vivo expanded NK cells used for hematological malignancies showed promising results, associated with in vivo NK cells expansion after infusion. However, due to the limited growth factors in the tumor microenvironment (TME), infused NK cells undergo changes in their phenotype and ability to survive. The type I cytokine family members IL-2 and IL-15 play a pivotal role to maintain homeostasis of the innate and adaptive immunity. Endogenous levels of IL-15 have been linked with sustained persistence of infused NK cells. Thus, the secret for NK cell resistance in the TME could be uncovered by investigating IL-15 primed NK cells under various forms of immunosuppression. In study I, we found that IL-15 primed NK cells acquire resistance against prostaglandin E2 (PGE2) mediated suppression by upregulation of phosphodiesterase 4A (PDE4A) in CD25+CD54+ NK cells. These CD25+CD54+ NK cells showed superior killing capacity under the suppression of PGE2 in vitro (2D and 3D culture) and in vivo (zebrafish model) experiments. In study II, we demonstrated that upregulated mTOR pathway primed by IL-15 lead to increased thiol density which protected not only NK cells but other lymphocytes against ROS in tumor microenvironment. In study III, we showed that upregulation of the IL-2α receptor (CD25) in NK cells enables an immunometabolic competition of IL-2 in the TME between Treg and NK cells. In summary, this thesis provides mechanistic insights for tumor-NK cell interaction and elucidates the potential therapeutic approach for harvesting "eximious" NK cells against solid tumors

Water for the future

Water is a fundamental element for lives. Located in Long Island detached from the mainland of New York State, the densely-populated counties - Kings, Queens, Nassau, and Suffolk Counties - rely on groundwater for their sole freshwater source for a long time. The underground geology determines the groundwater movement on western Long Island: from Nassau County to Queens. When overpumping happens in Queens, Nassau County is firstly threatened by lowered water table. The thesis is aiming to propose a local solution to mitigate the problem brought by groundwater movement when overpumping. In Phase 1, the study focuses on the underground geology of aquifers, and groundwater flow to understand the relationship between aquifers and groundwater system. Phase 2 provides a framework to a potential solution in regional scale based on three criteria. Phase 3 proposes a growing system starting from a granular scale to mitigate the problem

The Impact of ChatGPT on the Demand for Human Content Generating and Editing Services: Evidence from an Online Labor Market

The rise of generative AI has been a subject of debate among researchers and practitioners regarding its effect on the labor market. While some argue that it may displace jobs, others suggest it could create new opportunities and improve productivity. This study examines the impact of the ChatGPT launch on 18,130 services with 199,430 observations using a difference-in-differences approach and data from the online labor marketplace Fiverr. The findings suggest that ChatGPT had a negative effect on the demand for human content generating and editing services, with a concentration on writing services. However, there was no significant effect on the demand for editing services. The study also found that the demand for services with higher prices was more negatively affected. These results contribute to the ongoing debate on the impact of generative AI on the labor market and offer practical recommendations for service providers to navigate this new AI-driven landscape

The Relationship between Mobile Web and Mobile App Channels for Retailers

As smartphones and tablets are becoming ubiquitous, mobile ecommerce is also evolving rapidly. Consumers can shop on mobile devices in two ways; they either open a mobile browser and visit a retailer\u27s website, or download the retailer\u27s mobile app and shop within the app. However, it is unclear how retailers should manage these two emerging channels together. This proposed study aims to investigate the relationship between mobile web and mobile app channels by analyzing how a change to one channel affects the outcome in the other. To infer causality, we utilize an exogenous event in the mobile web channel to assess how it influences the demand of retailers\u27 mobile apps. The results could reveal whether these two mobile channels complement or substitute each other. This study contributes to the literature of multi-channel management in mobile commerce and provides important managerial implications for retailers to better leverage the growing mobile channels

Poisoning Retrieval Corpora by Injecting Adversarial Passages

Dense retrievers have achieved state-of-the-art performance in various information retrieval tasks, but to what extent can they be safely deployed in real-world applications? In this work, we propose a novel attack for dense retrieval systems in which a malicious user generates a small number of adversarial passages by perturbing discrete tokens to maximize similarity with a provided set of training queries. When these adversarial passages are inserted into a large retrieval corpus, we show that this attack is highly effective in fooling these systems to retrieve them for queries that were not seen by the attacker. More surprisingly, these adversarial passages can directly generalize to out-of-domain queries and corpora with a high success attack rate -- for instance, we find that 50 generated passages optimized on Natural Questions can mislead >94% of questions posed in financial documents or online forums. We also benchmark and compare a range of state-of-the-art dense retrievers, both unsupervised and supervised. Although different systems exhibit varying levels of vulnerability, we show they can all be successfully attacked by injecting up to 500 passages, a small fraction compared to a retrieval corpus of millions of passages.Comment: EMNLP 2023. Our code is available at https://github.com/princeton-nlp/corpus-poisonin

Second-order flows for approaching stationary points of a class of non-convex energies via convex-splitting schemes

The use of accelerated gradient flows is an emerging field in optimization, scientific computing and beyond. This paper contributes to the theoretical underpinnings of a recently-introduced computational paradigm known as second-order flows, which demonstrate significant performance particularly for the minimization of non-convex energy functionals defined on Sobolev spaces, and are characterized by novel dissipative hyperbolic partial differential equations. Our approach hinges upon convex-splitting schemes, a tool which is not only pivotal for clarifying the well-posedness of second-order flows, but also yields a versatile array of robust numerical schemes through temporal and spatial discretization. We prove the convergence to stationary points of such schemes in the semi-discrete setting. Further, we establish their convergence to time-continuous solutions as the time-step tends to zero, and perform a comprehensive error analysis in the fully discrete case. Finally, these algorithms undergo thorough testing and validation in approaching stationary points of non-convex variational models in applied sciences, such as the Ginzburg-Landau energy in phase-field modeling and a specific case of the Landau-de Gennes energy of the Q-tensor model for liquid crystals

Development and validation of prognostic index based on purine metabolism genes in patients with bladder cancer

BackgroundBladder cancer (BLCA) is a prevalent malignancy affecting the urinary system and is associated with significant morbidity and mortality worldwide. Dysregulation of tumor metabolic pathways is closely linked to the initiation and proliferation of BLCA. Tumor cells exhibit distinct metabolic activities compared to normal cells, and the purine metabolism pathway, responsible for providing essential components for DNA and RNA synthesis, is believed to play a crucial role. However, the precise involvement of Purine Metabolism Genes (PMGs) in the defense mechanism against BLCA remains elusive.MethodsThe integration of BLCA samples from the TCGA and GEO datasets facilitated the quantitative evaluation of PMGs, offering potential insights into their predictive capabilities. Leveraging the wealth of information encompassing mRNAsi, gene mutations, CNV, TMB, and clinical features within these datasets further enriched the analysis, augmenting its robustness and reliability. Through the utilization of Lasso regression, a prediction model was developed, enabling accurate prognostic assessments within the context of BLCA. Additionally, co-expression analysis shed light on the complex relationship between gene expression patterns and PMGs, unraveling their functional relevance and potential implications in BLCA.ResultsPMGs exhibited increased expression levels in the high-risk cohort of BLCA patients, even in the absence of other clinical indicators, suggesting their potential as prognostic markers. GSEA revealed enrichment of immunological and tumor-related pathways specifically in the high-risk group. Furthermore, notable differences were observed in immune function and m6a gene expression between the low- and high-risk groups. Several genes, including CLDN6, CES1, SOST, SPRR2A, MYBPH, CGB5, and KRT1, were found to potentially participate in the oncogenic processes underlying BLCA. Additionally, CRTAC1 was identified as potential tumor suppressor genes. Significant discrepancies in immunological function and m6a gene expression were observed between the two risk groups, further highlighting the distinct molecular characteristics associated with different prognostic outcomes. Notably, strong correlations were observed among the prognostic model, CNVs, SNPs, and drug sensitivity profiles.ConclusionPMGs have been implicated in the etiology and progression of bladder cancer (BLCA). Prognostic models corresponding to this malignancy aid in the accurate prediction of patient outcomes. Notably, exploring the potential therapeutic targets within the tumor microenvironment (TME) such as PMGs and immune cell infiltration holds promise for effective BLCA management, albeit necessitating further research. Moreover, the identification of a gene signature associated with purine Metabolism presents a credible and alternative approach for predicting BLCA, signifying a burgeoning avenue for targeted therapeutic investigations in the field of BLCA