Non-Conventional Copyright: Do New and Atypical Works Deserve Protection?

Enrico Bonadio and Nicola Lucchi (eds.

Modeling large scale species abundance with latent spatial processes

Modeling species abundance patterns using local environmental features is an important, current problem in ecology. The Cape Floristic Region (CFR) in South Africa is a global hot spot of diversity and endemism, and provides a rich class of species abundance data for such modeling. Here, we propose a multi-stage Bayesian hierarchical model for explaining species abundance over this region. Our model is specified at areal level, where the CFR is divided into roughly $37{,}000$ one minute grid cells; species abundance is observed at some locations within some cells. The abundance values are ordinally categorized. Environmental and soil-type factors, likely to influence the abundance pattern, are included in the model. We formulate the empirical abundance pattern as a degraded version of the potential pattern, with the degradation effect accomplished in two stages. First, we adjust for land use transformation and then we adjust for measurement error, hence misclassification error, to yield the observed abundance classifications. An important point in this analysis is that only $28$ of the grid cells have been sampled and that, for sampled grid cells, the number of sampled locations ranges from one to more than one hundred. Still, we are able to develop potential and transformed abundance surfaces over the entire region. In the hierarchical framework, categorical abundance classifications are induced by continuous latent surfaces. The degradation model above is built on the latent scale. On this scale, an areal level spatial regression model was used for modeling the dependence of species abundance on the environmental factors.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS335 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Multi-Bit Differential Fault Analysis of Grain-128 with Very Weak Assumptions

Very few differential fault attacks (DFA) were reported on {\em Grain-128} so far. In this paper we present a generic attack strategy that allows the adversary to challenge the cipher under different multi-bit fault models with faults at a targeted keystream generation round even if bit arrangement of the actual cipher device is unknown. Also unique identification of fault locations is not necessary. To the best of our knowledge, this paper assumes the weakest adversarial power ever considered in the open literature for DFA on {\em Grain-128} and develops the most realistic attack strategy so far on {\em Grain-128}. In particular, when a random area within $k \in \{1,2,3,4,5\}$ neighbourhood bits can only be disturbed by a single fault injection at the first keystream generation round ($k$-neighbourhood bit fault), without knowing the locations or the exact number of bits the injected fault has altered, our attack strategy always breaks the cipher with $5$ faults. In a weaker setup even if bit arrangement of the cipher device is unknown, bad-faults (at the first keystream generation round) are rejected with probabilities $0.999993$, $0.999979$, $0.999963$, $0.999946$ and $0.999921$ assuming that the adversary will use only 1, 2, 3, 4 and 5 neighbourhood bit faults respectively for {\em key-IV} recovery

Targeting Mitochondrial Cell Death Pathway to Overcome Drug Resistance with a Newly Developed Iron Chelate

Background: Multi drug resistance (MDR) or cross-resistance to multiple classes of chemotherapeutic agents is a major obstacle to successful application of chemotherapy and a basic problem in cancer biology. The multidrug resistance gene, MDR1, and its gene product P-glycoprotein (P-gp) are an important determinant of MDR. Therefore, there is an urgent need for development of novel compounds that are not substrates of P-glycoprotein and are effective against drug-resistant cancer. Methodology/Principal Findings: In this present study, we have synthesized a novel, redox active Fe (II) complex (chelate), iron N- (2-hydroxy acetophenone) glycinate (FeNG). The structure of the complex has been determined by spectroscopic means. To evaluate the cytotoxic effect of FeNG we used doxorubicin resistant and/or sensitive T lymphoblastic leukemia cells and show that FeNG kills both the cell types irrespective of their MDR phenotype. Moreover, FeNG induces apoptosis in doxorubicin resistance T lymphoblastic leukemia cell through mitochondrial pathway via generation reactive oxygen species (ROS). This is substantiated by the fact that the antioxidant N-acetyle-cysteine (NAC) could completely block ROS generation and, subsequently, abrogated FeNG induced apoptosis. Therefore, FeNG induces the doxorubicin resistant T lymphoblastic leukemia cells to undergo apoptosis and thus overcome MDR. Conclusion/Significance: Our study provides evidence that FeNG, a redox active metal chelate may be a promising ne