Therapeutic approaches to enhance natural killer cell cytotoxicity

Enhancing the cytotoxicity of natural killer (NK) cells has emerged as a promising strategy in cancer immunotherapy, due to their pivotal role in immune surveillance and tumor clearance. This literature review provides a comprehensive overview of therapeutic approaches designed to augment NK cell cytotoxicity. We analyze a wide range of strategies, including cytokine-based treatment, monoclonal antibodies, and NK cell engagers, and discuss criteria that must be considered when selecting an NK cell product to combine with these strategies. Furthermore, we discuss the challenges and limitations associated with each therapeutic strategy, as well as the potential for combination therapies to maximize NK cell cytotoxicity while minimizing adverse effects. By exploring the wealth of research on this topic, this literature review aims to provide a comprehensive resource for researchers and clinicians seeking to develop and implement novel therapeutic strategies that harness the full potential of NK cells in the fight against cancer. Enhancing NK cell cytotoxicity holds great promise in the evolving landscape of immunotherapy, and this review serves as a roadmap for understanding the current state of the field and the future directions in NK cell-based therapies

MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes

The combined analysis of magnetoencephalography (MEG)/electroencephalography and functional magnetic resonance imaging (fMRI) measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the blood oxygenation level dependent (BOLD) response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater signal-to-noise ratio, that confirms the expectation arising from the nature of the experiment. The highly non-linear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources

Machine learning techniques for the computer security domain of anomaly detection

In this dissertation, we examine the machine learning issues raised by the domain of anomaly detection for computer security. The anomaly detection task is to recognize the presence of an unusual and potentially hazardous state within the activities of a computer user, system, or network. “Unusual” is defined with respect to some model of “normal” behavior which may be either hard-coded or learned from observation. We focus here on learning models of normalcy at the user behavioral level, as observed through command line data. An anomaly detection agent faces many learning problems including learning from streams of temporal data, learning from instances of a single class, and adaptation to a dynamically changing concept. We describe two approaches to the construction of such models: one that employs instance-based models of user behaviors and one that uses hidden Markov models. We demonstrate the performance of sensors based on these models under a wide range of parameter settings and show conditions under which maximal classification performance is achieved. Using provided labels of users\u27 job descriptions, we demonstrate that users can be roughly divided into behavioral classes related to their experience level. Finally, we study methods for adapting user models to changing behavioral patterns and show the methods\u27 performance strengths and weaknesses

Efficient Multiplicative Updates for Support Vector Machines

The dual formulation of the support vector machine (SVM) objective function is an instance of a nonnegative quadratic programming problem. We reformulate the SVM objective function as a matrix factorization problem which establishes a connection with the regularized nonnegative matrix factorization (NMF) problem. This allows us to derive a novel multiplicative algorithm for solving hard and soft margin SVM. The algorithm follows as a natural extension of the updates for NMF and semi-NMF. No additional parameter setting, such as choosing learning rate, is required. Exploiting the connection between SVM and NMF formulation, we show how NMF algorithms can be applied to the SVM problem. Multiplicative updates that we derive for SVM problem also represent novel updates for semi-NMF. Further this unified view yields algorithmic insights in both directions: we demonstrate that the Kernel Adatron algorithm for solving SVMs can be adapted to NMF problems. Experiments demonstrate rapid convergence to good classifiers. We analyze the rates of asymptotic convergence of the updates and establish tight bounds. We test them on several datasets using various kernels and report equivalent classification performance to that of a standard SVM.

MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes

The combined analysis of magnetoencephalography (MEG)/electroencephalography and functional magnetic resonance imaging (fMRI) measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the blood oxygenation level dependent (BOLD) response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater signal-to-noise ratio, that confirms the expectation arising from the nature of the experiment. The highly non-linear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system’s behavior by stabilizing the differential equations system and by requiring fewer computational resources