Cell Death Response to DNA Damage.

The cell death response to DNA damage is discussed in this Perspectives piece with cancer as the backdrop because DNA damaging agents (DDA) are widely used to treat cancer. From decades of clinical results, we learn that DDA have cured some cancers but their toxicity is temporary in most cancers due to emergence of DDA-resistant cancer cells. Investigation of DDA-activated genes, proteins, and pathways, known collectively as the DNA damage response (DDR), has uncovered the inner workings of DDR that protect the genome to sustain life. Paradoxically, however, DDR can also activate death. Current knowledge on DDA-activated death and hypotheses for how DDR may determine when and where to execute death are discussed. Given that cancer cells suffer from DDR defects, which account for their initial sensitivity to DDA, future therapeutic development may exploit those cancer-specific DDR defects to selectively create death-inducing DNA lesions, without using DDA, to kill DDA-resistant cancers

Delay Differential Analysis of Seizures in Multichannel Electrocorticography Data

High-density electrocorticogram (ECoG) electrodes are capable of recording neurophysiological data with high temporal resolution with wide spatial coverage. These recordings are a window to understanding how the human brain processes information and subsequently behaves in healthy and pathologic states. Here, we describe and implement delay differential analysis (DDA) for the characterization of ECoG data obtained from human patients with intractable epilepsy. DDA is a time-domain analysis framework based on embedding theory in nonlinear dynamics that reveals the nonlinear invariant properties of an unknown dynamical system. The DDA embedding serves as a low-dimensional nonlinear dynamical basis onto which the data are mapped. This greatly reduces the risk of overfitting and improves the method's ability to fit classes of data. Since the basis is built on the dynamical structure of the data, preprocessing of the data (e.g., filtering) is not necessary. We performed a large-scale search for a DDA model that best fit ECoG recordings using a genetic algorithm to qualitatively discriminate between different cortical states and epileptic events for a set of 13 patients. A single DDA model with only three polynomial terms was identified. Singular value decomposition across the feature space of the model revealed both global and local dynamics that could differentiate electrographic and electroclinical seizures and provided insights into highly localized seizure onsets and diffuse seizure terminations. Other common ECoG features such as interictal periods, artifacts, and exogenous stimuli were also analyzed with DDA. This novel framework for signal processing of seizure information demonstrates an ability to reveal unique characteristics of the underlying dynamics of the seizure and may be useful in better understanding, detecting, and maybe even predicting seizures

Price Equivalent Impacts of the DDA in the Korean Raw-milk Market

This study estimates the potential impacts of the Doha Development Agenda (DDA) on the Korean raw-milk market. The DDA has not reached an agreement yet. Although there are different attitudes about several issues such as Special Safeguard Mechanism (SSM), Sensitive Products (SP), and Tariff Rate Quota (TRQ) creation, WTO member countries have reached an agreement for major issues of the modalities in the DDA. Hence, this study estimates the impacts of the DDA that will finally reach an agreement sooner or later. For estimating the impacts of the DDA, this study makes a dairy trade model for the Korean dairy industry and measures the impacts of the DDA in terms of raw-milk price for fluid use incurred by further tariff cuts in the Korean dairy market by the DDA. This study considers several scenarios because the status of Korea is not settled yet and a country can select dairy products as sensitive products, special products, or general products and a country can select different options in each category. The results of this study can be used for preparing policies for subsidizing the domestic raw-milk producers to rebalance their loss in the raw-milk market incurred by the DDA.DDA, dairy, milk, tariff, price equivalent, Agricultural and Food Policy, Demand and Price Analysis, International Relations/Trade, Livestock Production/Industries,

Big Data Dimensional Analysis

The ability to collect and analyze large amounts of data is a growing problem within the scientific community. The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. One of the main challenges associated with big data variety is automatically understanding the underlying structures and patterns of the data. Such an understanding is required as a pre-requisite to the application of advanced analytics to the data. Further, big data sets often contain anomalies and errors that are difficult to know a priori. Current approaches to understanding data structure are drawn from the traditional database ontology design. These approaches are effective, but often require too much human involvement to be effective for the volume, velocity and variety of data encountered by big data systems. Dimensional Data Analysis (DDA) is a proposed technique that allows big data analysts to quickly understand the overall structure of a big dataset, determine anomalies. DDA exploits structures that exist in a wide class of data to quickly determine the nature of the data and its statical anomalies. DDA leverages existing schemas that are employed in big data databases today. This paper presents DDA, applies it to a number of data sets, and measures its performance. The overhead of DDA is low and can be applied to existing big data systems without greatly impacting their computing requirements.Comment: From IEEE HPEC 201