المؤلفون: Ghose, Ritobrata

المساهمون: University/Department: Universitat Pompeu Fabra. Departament de Medicina i Ciències de la Vida

مرشدي الرسالة: Sdelci, Sara

المصدر: TDX (Tesis Doctorals en Xarxa)

مصطلحات موضوعية: Cancer adaptation, Mechanical confinement, Nuclear metabolism, KRAS signalling, TLR signalling, Adaptación del cáncer, Confinamiento mecánico, Metabolismo nuclear, Señalización KRAS, Señalización TLR

الوصف: Cancer cells are constantly subject to complex stresses in the tumour microenvironment. These stresses encapsulate a wide array of challenging stimuli from physical and mechanical forces to interference in major mechanistic signalling cascades. The work in this thesis aims to tackle the question of cancer adaptations in two contexts. First, we investigate the impact of mechanical stress on cancer cells and how subcellular mitochondria redistribution leads to important metabolic and nuclear energetics cues. These cues in turn enable critical DNA damage repair processes which are necessary to maintain correct cell cycle progression. Second, we investigate the loss of ARF6, an important mediator of KRAS signalling in pancreatic cancer, whose loss is compensated through a mechanistic rewiring which activates TLR2 signalling. Dual inhibition of both ARF6 and TLR2 together almost entirely abolishes pancreatic cancer cell proliferation, migration, and spheroid formation in vitro, and tumour growth and metastasis in vivo. Together, the work in this thesis highlights important processes in the mechanical and mechanistic adaptive landscapes of cancer.

الوصف (مترجم): Las células cancerosas están constantemente sometidas a complejos factores de estrés en el microambiente tumoral. Estos factores de estrés engloban una amplia variedad de estímulos que van desde fuerzas físicas y mecánicas hasta interferencias en las principales cascadas de señalización. El trabajo de esta tesis tiene como objetivo abordar la cuestión de las adaptaciones del cáncer en dos contextos. Primero, investigamos el impacto del estrés mecánico en las células cancerosas y cómo la redistribución subcelular mitocondrial conduce a importantes señales energéticas metabólicas y nucleares. Estas señales, a su vez, permiten procesos críticos de reparación del daño del ADN que son necesarios para mantener la correcta progresión del ciclo celular. En segundo lugar, investigamos la pérdida de ARF6, un mediador importante de la señalización de KRAS en el cáncer de páncreas, cuya pérdida se compensa a través de un mecanismo de adaptación que activa la señalización de TLR2. La inhibición simultanea de ARF6 y TLR2 inhibe prácticamente por completo la proliferación, migración y formación de esferoides en células de cáncer de páncreas in vitro, así como el crecimiento tumoral y la metástasis in vivo. En conjunto, el trabajo de esta tesis enfatiza adaptaciones de procesos mecánicos y cascadas de señalización del cáncer.
Programa de Doctorat en Biomedicina

وصف الملف: application/pdf

الوصول الحر: http://hdl.handle.net/10803/690402Test

المؤلفون: Ghose, Partha

مصطلحات موضوعية: Quantitative Biology - Neurons and Cognition, Quantum Physics

الوصف: It is shown that Brownian motions executed by state points of neural membranes generate a Schr\"{o}dinger-like equation with $\hbar/m$ replaced by the coefficient of diffusion $\sigma$ of the substrates.
Comment: 6 pages, no figures

الوصول الحر: http://arxiv.org/abs/2406.16991Test

المؤلفون: Sarkar, Meenakshi, Bhardwaj, Devansh, Ghose, Debasish

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics

الوصف: Stochastic video generation is particularly challenging when the camera is mounted on a moving platform, as camera motion interacts with observed image pixels, creating complex spatio-temporal dynamics and making the problem partially observable. Existing methods typically address this by focusing on raw pixel-level image reconstruction without explicitly modelling camera motion dynamics. We propose a solution by considering camera motion or action as part of the observed image state, modelling both image and action within a multi-modal learning framework. We introduce three models: Video Generation with Learning Action Prior (VG-LeAP) treats the image-action pair as an augmented state generated from a single latent stochastic process and uses variational inference to learn the image-action latent prior; Causal-LeAP, which establishes a causal relationship between action and the observed image frame at time $t$, learning an action prior conditioned on the observed image states; and RAFI, which integrates the augmented image-action state concept into flow matching with diffusion generative processes, demonstrating that this action-conditioned image generation concept can be extended to other diffusion-based models. We emphasize the importance of multi-modal training in partially observable video generation problems through detailed empirical studies on our new video action dataset, RoAM.

الوصول الحر: http://arxiv.org/abs/2406.14436Test

المؤلفون: Ghose, Souvik, Das, Tapas K.

مصطلحات موضوعية: Astrophysics - High Energy Astrophysical Phenomena

الوصف: Realization of the stationary integral solutions of steady state transonic accretion flow in spherical symmetry helps to understand accretion phenomena on various astrophysical objects. In recent years, attempts have been made to study accreting black hole systems as an example of autonomous dynamical systems. The fixed point analysis is used to study the transonic properties of accretion flow onto an astrophysical black hole, hence the nature of the phase orbits for the transonic flow solutions can be understood without constructing the integral solutions. Since a large-scale astrophysical fluid flow is vulnerable to external perturbation, one needs to ensure that the stationary accretion solutions are stable under perturbation. By adopting a time-dependent stability analysis scheme for the accretion flow, one demonstrates under which condition the perturbation will not diverge. It has also been observed that a space-time metric, dubbed the sonic metric, can be constructed to describe the propagation of the perturbation embedded within the accreting fluid, which mimics a black hole like spacetime within the accreting fluid, where the transonic surfaces can be identified with a black hole like horizons. Such identification is accomplished using the theory of causal structure - by constructing Carter-Penrose diagrams. An accreting black hole system, thus, can be perceived as a classical analogue gravity model naturally found in the universe. Hence, accretion phenomena onto astrophysical black holes can be looked upon from three apparently non-overlapping perspectives - astrophysical processes, theory of dynamical systems, and emergent gravity (alternatively, the analogue gravity) phenomena, respectively. The present article illustrates, by taking the simplest possible accretion flow model, how one can study astrophysical accretion processes from the aforementioned perspectives (Abridged).
Comment: 10 pages, 3 figues, RevTex Class, Comments Welcome

الوصول الحر: http://arxiv.org/abs/2406.02673Test

المؤلفون: A, Sajid Ahamed M, Menon, Prathyush P, Ghose, Debasish

مصطلحات موضوعية: Computer Science - Multiagent Systems, Electrical Engineering and Systems Science - Systems and Control

الوصف: Existing UAS Traffic Management (UTM) frameworks designate preplanned flight paths to uncrewed aircraft systems (UAS), enabling the UAS to deliver payloads. However, with increasing delivery demand between the source-destination pairs in the urban airspace, UAS will likely experience considerable congestion on the nominal paths. We propose a rule-based congestion mitigation strategy that improves UAS safety and airspace utilization in congested traffic streams. The strategy relies on nominal path information from the UTM and positional information of other UAS in the vicinity. Following the strategy, UAS opts for alternative local paths in the unoccupied airspace surrounding the nominal path and avoids congested regions. The strategy results in UAS traffic exploring and spreading to alternative adjacent routes on encountering congestion. The paper presents queuing models to estimate the expected traffic spread for varying stochastic delivery demand at the source, thus helping to reserve the airspace around the nominal path beforehand to accommodate any foreseen congestion. Simulations are presented to validate the queuing results in the presence of static obstacles and intersecting UAS streams.

الوصول الحر: http://arxiv.org/abs/2405.20972Test

المؤلفون: Zhang, Zhanguang, Chetelat, Didier, Cotnareanu, Joseph, Ghose, Amur, Xiao, Wenyi, Zhen, Hui-Ling, Zhang, Yingxue, Hao, Jianye, Coates, Mark, Yuan, Mingxuan

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Artificial Intelligence

الوصف: Boolean satisfiability (SAT) problems are routinely solved by SAT solvers in real-life applications, yet solving time can vary drastically between solvers for the same instance. This has motivated research into machine learning models that can predict, for a given SAT instance, which solver to select among several options. Existing SAT solver selection methods all rely on some hand-picked instance features, which are costly to compute and ignore the structural information in SAT graphs. In this paper we present GraSS, a novel approach for automatic SAT solver selection based on tripartite graph representations of instances and a heterogeneous graph neural network (GNN) model. While GNNs have been previously adopted in other SAT-related tasks, they do not incorporate any domain-specific knowledge and ignore the runtime variation introduced by different clause orders. We enrich the graph representation with domain-specific decisions, such as novel node feature design, positional encodings for clauses in the graph, a GNN architecture tailored to our tripartite graphs and a runtime-sensitive loss function. Through extensive experiments, we demonstrate that this combination of raw representations and domain-specific choices leads to improvements in runtime for a pool of seven state-of-the-art solvers on both an industrial circuit design benchmark, and on instances from the 20-year Anniversary Track of the 2022 SAT Competition.
Comment: Accepted by KDD 2024

الوصول الحر: http://arxiv.org/abs/2405.11024Test

المؤلفون: Nweye, Kingsley, Kaspar, Kathryn, Buscemi, Giacomo, Fonseca, Tiago, Pinto, Giuseppe, Ghose, Dipanjan, Duddukuru, Satvik, Pratapa, Pavani, Li, Han, Mohammadi, Javad, Ferreira, Luis Lino, Hong, Tianzhen, Ouf, Mohamed, Capozzoli, Alfonso, Nagy, Zoltan

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Computers and Society, Electrical Engineering and Systems Science - Systems and Control

الوصف: As more distributed energy resources become part of the demand-side infrastructure, it is important to quantify the energy flexibility they provide on a community scale, particularly to understand the impact of geographic, climatic, and occupant behavioral differences on their effectiveness, as well as identify the best control strategies to accelerate their real-world adoption. CityLearn provides an environment for benchmarking simple and advanced distributed energy resource control algorithms including rule-based, model-predictive, and reinforcement learning control. CityLearn v2 presented here extends CityLearn v1 by providing a simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create virtual grid-interactive communities for resilient, multi-agent distributed energy resources and objective control with dynamic occupant feedback. This work details the v2 environment design and provides application examples that utilize reinforcement learning to manage battery energy storage system charging/discharging cycles, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

الوصول الحر: http://arxiv.org/abs/2405.03848Test

المؤلفون: Anandh, Thivin, Ghose, Divij, Jain, Himanshu, Ganesan, Sashikumaar

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Neural and Evolutionary Computing, Mathematics - Numerical Analysis

الوصف: Variational Physics-Informed Neural Networks (VPINNs) utilize a variational loss function to solve partial differential equations, mirroring Finite Element Analysis techniques. Traditional hp-VPINNs, while effective for high-frequency problems, are computationally intensive and scale poorly with increasing element counts, limiting their use in complex geometries. This work introduces FastVPINNs, a tensor-based advancement that significantly reduces computational overhead and improves scalability. Using optimized tensor operations, FastVPINNs achieve a 100-fold reduction in the median training time per epoch compared to traditional hp-VPINNs. With proper choice of hyperparameters, FastVPINNs surpass conventional PINNs in both speed and accuracy, especially in problems with high-frequency solutions. Demonstrated effectiveness in solving inverse problems on complex domains underscores FastVPINNs' potential for widespread application in scientific and engineering challenges, opening new avenues for practical implementations in scientific machine learning.
Comment: 31 pages, 19 figures, 4 algorithms

الوصول الحر: http://arxiv.org/abs/2404.12063Test

المؤلفون: Fonseca, Tiago, Ferreira, Luis, Cabral, Bernardo, Severino, Ricardo, Nweye, Kingsley, Ghose, Dipanjan, Nagy, Zoltan

مصطلحات موضوعية: Computer Science - Multiagent Systems, Electrical Engineering and Systems Science - Systems and Control

الوصف: Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) emerge as a potential solution to the Electric Vehicles' (EVs) integration into the energy grid. These strategies promise enhanced grid resilience and economic benefits for both vehicle owners and grid operators. Despite the announced prospective, the adoption of these strategies is still hindered by an array of operational problems. Key among these is the lack of a simulation platform that allows to validate and refine V2G and G2V strategies. Including the development, training, and testing in the context of Energy Communities (ECs) incorporating multiple flexible energy assets. Addressing this gap, first we introduce the EVLearn, a simulation module for researching in both V2G and G2V energy management strategies, that models EVs, their charging infrastructure and associated energy flexibility dynamics; second, this paper integrates EVLearn with the existing CityLearn framework, providing V2G and G2V simulation capabilities into the study of broader energy management strategies. Results validated EVLearn and its integration into CityLearn, where the impact of these strategies is highlighted through a comparative simulation scenario.
Comment: 10 pages, 7 figures, 3 tables, 11 equations

الوصول الحر: http://arxiv.org/abs/2404.06521Test

المؤلفون: Sarkar, Meenakshi, Ghose, Debasish

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Long-term video generation and prediction remain challenging tasks in computer vision, particularly in partially observable scenarios where cameras are mounted on moving platforms. The interaction between observed image frames and the motion of the recording agent introduces additional complexities. To address these issues, we introduce the Action-Conditioned Video Generation (ACVG) framework, a novel approach that investigates the relationship between actions and generated image frames through a deep dual Generator-Actor architecture. ACVG generates video sequences conditioned on the actions of robots, enabling exploration and analysis of how vision and action mutually influence one another in dynamic environments. We evaluate the framework's effectiveness on an indoor robot motion dataset which consists of sequences of image frames along with the sequences of actions taken by the robotic agent, conducting a comprehensive empirical study comparing ACVG to other state-of-the-art frameworks along with a detailed ablation study.

الوصول الحر: http://arxiv.org/abs/2404.05439Test