No abstract

Tryptophan catabolism by indoleamine 2,3-dioxygenase 1 (IDO1) plays a key role in tumoral resistance to immune rejection. In humans, constitutive expression of IDO1 has been observed in several tumor types. However, a comprehensive analysis of its expression in normal and tumor tissues is still required to anticipate the risks and potential benefits of IDO1 inhibitors. Using a newly validated monoclonal antibody to human IDO1, we performed an extensive immunohistochemical analysis of IDO1 expression in normal and tumor tissues. In normal tissues, IDO1 was expressed by endothelial cells in the placenta and lung and by epithelial cells in the female genital tract. In lymphoid tissues, IDO1 was expressed in mature dendritic cells with a phenotype (CD83 þ ,

Recent research on universal and minimax wavelet shrinkage and thresholding methods has demonstrated near{ideal estimation performance in various asymptotic frameworks. However, image processing practice has shown that universal thresholding methods are outperformed by simple Bayesian estimators assuming independent wavelet coe cients and heavy{tailed priors such as Generalized Gaussian distributions (GGDs). In this paper, we investigate various connections between shrinkage methods and MAP estimation using such priors. In particular, we state a simple condition under which MAP estimates are sparse. We also introduce a new family of complexity priors based upon Rissanen's universal prior on integers. One particular estimator in this class outperforms conventional wavelet{based MDL estimators. We develop analytical expressions for the shrinkage rules implied by GGD and complexity priors. This allows us to show the equivalence between universal hard thresholding, MAP estimation using a very heavy{ tailed GGD, and MDL estimation using one of the new complexity priors. Theoretical analysis supported by numerous practical experiments shows the robustness of some of these estimates against misspeci cations of the prior { a basic concern in image processing applications.

Abstract-An information-theoretic analysis of information hiding is presented in this paper, forming the theoretical basis for design of information-hiding systems. Information hiding is an emerging research area which encompasses applications such as copyright protection for digital media, watermarking, fingerprinting, steganography, and data embedding. In these applications, information is hidden within a host data set and is to be reliably communicated to a receiver. The host data set is intentionally corrupted, but in a covert way, designed to be imperceptible to a casual analysis. Next, an attacker may seek to destroy this hidden information, and for this purpose, introduce additional distortion to the data set. Side information (in the form of cryptographic keys and/or information about the host signal) may be available to the information hider and to the decoder.We formalize these notions and evaluate the hiding capacity, which upper-bounds the rates of reliable transmission and quantifies the fundamental tradeoff between three quantities: the achievable information-hiding rates and the allowed distortion levels for the information hider and the attacker. The hiding capacity is the value of a game between the information hider and the attacker. The optimal attack strategy is the solution of a particular rate-distortion problem, and the optimal hiding strategy is the solution to a channel-coding problem. The hiding capacity is derived by extending the Gel'fand-Pinsker theory of communication with side information at the encoder. The extensions include the presence of distortion constraints, side information at the decoder, and unknown communication channel. Explicit formulas for capacity are given in several cases, including Bernoulli and Gaussian problems, as well as the important special case of small distortions. In some cases, including the last two above, the hiding capacity is the same whether or not the decoder knows the host data set. It is shown that many existing information-hiding systems in the literature operate far below capacity.

Earlier and more reliable detection of drug-induced kidney injury would improve clinical care and help to streamline drug-development. As the current standards to monitor renal function, such as blood urea nitrogen (BUN) or serum creatinine (SCr), are late indicators of kidney injury, we conducted ten nonclinical studies to rigorously assess the potential of four previously described nephrotoxicity markers to detect drug-induced kidney and liver injury. Whereas urinary clusterin outperformed BUN and SCr for detecting proximal tubular injury, urinary total protein, cystatin C and beta2-microglobulin showed a better diagnostic performance than BUN and SCr for detecting glomerular injury. Gene and protein expression analysis, in-situ hybridization and immunohistochemistry provide mechanistic evidence to support the use of these four markers for detecting kidney injury to guide regulatory decision making in drug development. The recognition of the qualification of these biomarkers by the EMEA and FDA will significantly enhance renal safety monitoring.

No abstract

An information-theoretic analysis of information hiding is presented in this paper, forming the theoretical basis for design of information-hiding systems. Information hiding is an emerging research area which encompasses applications such as copyright protection for digital media, watermarking, fingerprinting, steganography, and data embedding. In these applications, information is hidden within a host data set and is to be reliably communicated to a receiver. The host data set is intentionally corrupted, but in a covert way, designed to be imperceptible to a casual analysis. Next, an attacker may seek to destroy this hidden information, and for this purpose, introduce additional distortion to the data set. Side information (in the form of cryptographic keys and/or information about the host signal) may be available to the information hider and to the decoder. We formalize these notions and evaluate the hiding capacity, which upper-bounds the rates of reliable transmission and quantifies the fundamental tradeoff between three quantities: the achievable information-hiding rates and the allowed distortion levels for the information hider and the attacker. The hiding capacity is the value of a game between the information hider and the attacker. The optimal attack strategy is the solution of a particular rate-distortion problem, and the optimal hiding strategy is the solution to a channel-coding problem. The hiding capacity is derived by extending the Gel'fand-Pinsker theory of communication with side information at the encoder. The extensions include the presence of distortion constraints, side information at the decoder, and unknown communication channel. Explicit formulas for capacity are given in several cases, including Bernoulli and Gaussian problems, as well as the important special case of small distortions. In some cases, including the last two above, the hiding capacity is the same whether or not the decoder knows the host data set. It is shown that many existing information-hiding systems in the literature operate far below capacity.

No abstract