Blockade of PD-1/PD-L1 promotes adoptive T-Cell immunotherapy in a tolerogenic environment

Adoptive cellular immunotherapy using in vitro expanded CD8+ T cells shows promise for tumour immunotherapy but is limited by eventual loss of function of the transferred T cells through factors that likely include inactivation by tolerogenic dendritic cells (DC). The coinhibitory receptor programmed death-1 (PD-1), in addition to controlling T-cell responsiveness at effector sites in malignancies and chronic viral diseases is an important modulator of dendritic cell-induced tolerance in naive T cell populations. The most potent therapeutic capacity amongst CD8+ T cells appears to lie within Tcm or Tcm-like cells but memory T cells express elevated levels of PD-1. Based on established trafficking patterns for Tcm it is likely Tcm-like cells interact with lymphoid-tissue DC that present tumour-derived antigens and may be inherently tolerogenic to develop therapeutic effector function. As little is understood of the effect of PD-1/PD-L1 blockade on Tcm-like CD8+ T cells, particularly in relation to inactivation by DC, we explored the effects of PD-1/PD-L1 blockade in a mouse model where resting DC tolerise effector and memory CD8+ T cells. Blockade of PD-1/PDL1 promoted effector differentiation of adoptively-transferred Tcm-phenotype cells interacting with tolerising DC. In tumour-bearing mice with tolerising DC, effector activity was increased in both lymphoid tissues and the tumour-site and anti-tumour activity was promoted. Our findings suggest PD-1/PD-L1 blockade may be a useful adjunct for adoptive immunotherapy by promoting effector differentiation in the host of transferred Tcmlike cells. © 2015 Blake et al.

Ratios of T-cell immune-effectors with tumour associated macrophages and PD-1/PD-L1 axis immune-checkpoint molecules, as prognosticators in diffuse large B cell lymphoma: A population-based study

Background Risk-stratification of diffuse large B-cell lymphoma (DLBCL) requires identification of patients with disease that is not cured despite initial R-CHOP. Although the prognostic importance of the tumour microenvironment (TME) is established, the optimal strategy to quantify it is unknown. Methods The relationship between immune-effector and inhibitory (checkpoint) genes was assessed by NanoString™ in 252 paraffin-embedded DLBCL tissues. A model to quantify net anti-tumoural immunity as an outcome predictor was tested in 158 R-CHOP treated patients, and validated in tissue/blood from two independent R-CHOP treated cohorts of 233 and 140 patients respectively. Findings T and NK-cell immune-effector molecule expression correlated with tumour associated macrophage and PD-1/PD-L1 axis markers consistent with malignant B-cells triggering a dynamic checkpoint response to adapt to and evade immune-surveillance. A tree-based survival model was performed to test if immune-effector to checkpoint ratios were prognostic. The CD4*CD8:(CD163/CD68)*PD-L1 ratio was better able to stratify overall survival than any single or combination of immune markers, distinguishing groups with disparate 4-year survivals (92% versus 47%). The immune ratio was independent of and added to the revised international prognostic index (R-IPI) and cell-of-origin (COO). Tissue findings were validated in 233 DLBCL R-CHOP treated patients. Furthermore, within the blood of 140 R-CHOP treated patients immune-effector:checkpoint ratios were associated with differential interim-PET/CT+ve/-ve expression.

Self-regularized pseudo time-marching schemes for structural system identification with static measurements

We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non-linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least-square solution is through a regularized Gauss-Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time,recursive integration of the pseudo-dynamical GNM (PD-GNM) update equation addresses the major numerical difficulty associated with the near-zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo-dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through a pseudo-dynamic ensemble Kalman filter (PD-EnKF). For multiple sets of measurements involving various load cases, we expedite the speed of thePD-EnKF by proposing an inner iteration within every time step. Results using the pseudo-dynamic strategy obtained through PD-EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD-EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small.

Self-regularized pseudo time-marching schemes for structural system identification with static measurements

We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non-linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least-square solution is through a regularized Gauss-Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time, recursive integration of the pseudo-dynamical GNM (PD-GNM) update equation addresses the major numerical difficulty associated with the near-zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo-dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through apseudo-dynamic ensemble Kalman filter (PD-EnKF). For multiple sets ofmeasurements involving various load cases, we expedite the speed of the PD-EnKF by proposing an inner iteration within every time step. Results using the pseudo-dynamic strategy obtained through PD-EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD-EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small. Copyright (C) 2009 John Wiley & Sons, Ltd.

Magnetoelectric Effect in L1(0)-CoPd Thin Films

The effect of an applied electric field on the magnetic properties of L1(0)-ordered CoPd thin films is investigated by first-principle calculations. Both the magnetic moment and the magnetocrystalline anisotropy of the surface atoms are changed by the electric field, but the net effect depends on the surface termination. The magnetocrystalline anisotropy switches from in-plane to perpendicular in the presence of external electric field. Typical magnetic-moment changes are 0.1 mu(B) per eV/angstrom The main mechanism is the shift of the Fermi level, but the anisotropy change also reflects a crystal-field change due to incomplete screening.

A regularized linear classifier for effective text classification

In document community support vector machines and naïve bayes classifier are known for their simplistic yet excellent performance. Normally the feature subsets used by these two approaches complement each other, however a little has been done to combine them. The essence of this paper is a linear classifier, very similar to these two. We propose a novel way of combining these two approaches, which synthesizes best of them into a hybrid model. We evaluate the proposed approach using 20ng dataset, and compare it with its counterparts. The efficacy of our results strongly corroborate the effectiveness of our approach.

Scalable sequential alternating proximal methods for sparse structural SVMs and CRFs

Structural Support Vector Machines (SSVMs) and Conditional Random Fields (CRFs) are popular discriminative methods used for classifying structured and complex objects like parse trees, image segments and part-of-speech tags. The datasets involved are very large dimensional, and the models designed using typical training algorithms for SSVMs and CRFs are non-sparse. This non-sparse nature of models results in slow inference. Thus, there is a need to devise new algorithms for sparse SSVM and CRF classifier design. Use of elastic net and L1-regularizer has already been explored for solving primal CRF and SSVM problems, respectively, to design sparse classifiers. In this work, we focus on dual elastic net regularized SSVM and CRF. By exploiting the weakly coupled structure of these convex programming problems, we propose a new sequential alternating proximal (SAP) algorithm to solve these dual problems. This algorithm works by sequentially visiting each training set example and solving a simple subproblem restricted to a small subset of variables associated with that example. Numerical experiments on various benchmark sequence labeling datasets demonstrate that the proposed algorithm scales well. Further, the classifiers designed are sparser than those designed by solving the respective primal problems and demonstrate comparable generalization performance. Thus, the proposed SAP algorithm is a useful alternative for sparse SSVM and CRF classifier design.

Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression

Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classification. In this work, we propose an alternating optimization approach to solve the dual problems of elastic net regularized linear classification Support Vector Machines (SVMs) and logistic regression (LR). One of the sub-problems turns out to be a simple projection. The other sub-problem can be solved using dual coordinate descent methods developed for non-sparse L2-regularized linear SVMs and LR, without altering their iteration complexity and convergence properties. Experiments on very large datasets indicate that the proposed dual coordinate descent - projection (DCD-P) methods are fast and achieve comparable generalization performance after the first pass through the data, with extremely sparse models.

Resveratrol Metabolites Modify Adipokine Expression and Secretion in 3T3-L1 Pre-Adipocytes and Mature Adipocytes

Objective: Due to the low bioavailability of resveratrol, determining whether its metabolites exert any beneficial effect is an interesting issue. Methods: 3T3-L1 maturing pre-adipocytes were treated during differentiation with 25 mu M of resveratrol or with its metabolites and 3T3-L1 mature adipocytes were treated for 24 hours with 10 mM resveratrol or its metabolites. The gene expression of adiponectin, leptin, visfatin and apelin was assessed by Real Time RT-PCR and their concentration in the incubation medium was quantified by ELISA. Results: Resveratrol reduced mRNA levels of leptin and increased those of adiponectin. It induced the same changes in leptin secretion. Trans-resveratrol-3-O-glucuronide and trans-resveratrol-4'-O-glucuronide increased apelin and visfatin mRNA levels. Trans-resveratrol-3-O-sulfate reduced leptin mRNA levels and increased those of apelin and visfatin. Conclusions: The present study shows for the first time that resveratrol metabolites have a regulatory effect on adipokine expression and secretion. Since resveratrol has been reported to reduce body-fat accumulation and to improve insulin sensitivity, and considering that these effects are mediated in part by changes in the analyzed adipokines, it may be proposed that resveratrol metabolites play a part in these beneficial effects of resveratrol.

Bayesian and L1 Approaches to Sparse Unsupervised Learning

The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborative filtering. While existing work highlights the many advantages of L1 methods, in this paper we find that L1 regularisation often dramatically underperforms in terms of predictive performance when compared with other methods for inferring sparsity. We focus on unsupervised latent variable models, and develop L1 minimising factor models, Bayesian variants of "L1", and Bayesian models with a stronger L0-like sparsity induced through spike-and-slab distributions. These spike-and-slab Bayesian factor models encourage sparsity while accounting for uncertainty in a principled manner and avoiding unnecessary shrinkage of non-zero values. We demonstrate on a number of data sets that in practice spike-and-slab Bayesian methods outperform L1 minimisation, even on a computational budget. We thus highlight the need to re-assess the wide use of L1 methods in sparsity-reliant applications, particularly when we care about generalising to previously unseen data, and provide an alternative that, over many varying conditions, provides improved generalisation performance.

A 5.6-mW Power Dissipation CMOS Frequency Synthesizer for L1/L2 Dual-Band GPS Application

This paper presents a wide tuning range CMOS frequency synthesizer for dual-band GPS receiver, which has been fabricated in a standard 0.18-um RF CMOS process. With a high Q on-chip inductor, the wide-band VCO shows a tuning range from 2 to 3.6GHz to cover 2.45GHz and 3.14GHz in case of process corner or temperature variation, with a current consumption varying accordingly from 0.8mA to 0.4mA, from a 1.8V supply voltage. The measurement results show that the whole frequency synthesizer costs a very low power consumption of 5.6mW working at L I band with in-band phase noise less than -82dBc/Hz and out-of-band phase noise about -112 dBc/Hz at 1MHz offset from a 3.142GHz carrier.

Robust Tensor Analysis With L1-Norm

Tensor analysis plays an important role in modern image and vision computing problems. Most of the existing tensor analysis approaches are based on the Frobenius norm, which makes them sensitive to outliers. In this paper, we propose L1-norm-based tensor analysis (TPCA-L1), which is robust to outliers. Experimental results upon face and other datasets demonstrate the advantages of the proposed approach.

L1-Norm-Based 2DPCA

In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.