279 research outputs found
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.Comment: 37 pages, 4 figure
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
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