Nonunion Employee Representation in North America: Diversity, Controversy, and Uncertain Future

The diverse conceptual perspectives and practical experiences with nonunion employee representation (NER) in the United States and Canada are reviewed. We first propose a 6 dimensional descriptive schema to categorize observed NER practices. Dimensions of diversity include (1) form, (2) function, (3) subjects, (4) representation characteristics, (5) extent of power, and (6) degree of permanence. We then turn to the NER controversy, which is a tangled skein consisting of many different threads of values and prescriptions. To unbundle the controversy, we develop four "faces" of NER - (1) evolutionary voice, (2) unity of interest; (3) union avoidance, and (4) complementary voice -- so that future research can more consciously test the validity of competing perspectives with hard data. Generalizing about NER is problematic because of these many dimensions of diversity, and because NER is viewed through different ideological and conceptual lenses. We conclude that NER's future trajectory is uncertain due to conflicting trends but in the short-run is most likely to remain a modest-sized phenomenon. Working Paper 06-4

Xeroderma pigmentosum group A protein loads as a separate factor onto DNA lesions

Nucleotide excision repair (NER) is the main DNA repair pathway in mammals for removal of UV-induced lesions. NER involves the concerted action of more than 25 polypeptides in a coordinated fashion. The xeroderma pigmentosum group A protein (XPA) has been suggested to function as a central organizer and damage verifier in NER. How XPA reaches DNA lesions and how the protein is distributed in time and space in living cells are unknown. Here we studied XPA in vivo by using a cell line stably expressing physiological levels of functional XPA fused to green fluorescent protein and by applying quantitative fluorescence microscopy. The majority of XPA moves rapidly through the nucleoplasm with a diffusion rate different from those of other NER factors tested, arguing against a preassembled XPA-containing NER complex. DNA damage induced a transient ( approximately 5-min) immobilization of maximally 30% of XPA. Immobilization depends on XPC, indicating that XPA is not the initial lesion recognition protein in vivo. Moreover, loading of replication protein A on NER lesions was not dependent on XPA. Thus, XPA participates in NER by incorporation of free diffusing molecules in XPC-dependent NER-DNA complexes. This study supports a model for a rapid consecutive assembly of free NER factors, and a relatively slow simultaneous disassembly, after repair

Improved chiral nucleon-nucleon potential up to next-to-next-to-next-to-leading order

We present improved nucleon-nucleon potentials derived in chiral effective field theory up to next-to-next-to-next-to-leading order. We argue that the nonlocal momentum-space regulator employed in the two-nucleon potentials of Refs. [E. Epelbaum, W. Gloeckle, U.-G. Mei{\ss}ner, Nucl. Phys. A747 (2005) 362], [D.R. Entem, R. Machleidt, Phys. Rev. C68 (2003) 041001] is not the most efficient choice, in particular since it affects the long-range part of the interaction. We are able to significantly reduce finite-cutoff artefacts by using an appropriate regularization in coordinate space which maintains the analytic structure of the amplitude. The new potentials do not require the additional spectral function regularization employed in Ref. [E. Epelbaum, W. Gloeckle, U.-G. Mei{\ss}ner, Nucl. Phys. A747 (2005) 362] to cut off the short-range components of the two-pion exchange and make use of the low-energy constants c_i and d_i determined from pion-nucleon scattering without any fine tuning. We discuss in detail the construction of the new potentials and convergence of the chiral expansion for two-nucleon observables. We also introduce a new procedure for estimating the theoretical uncertainty from the truncation of the chiral expansion that replaces previous reliance on cutoff variation.Comment: 34 pages, 13 figures, 7 table

Kosterlitz-Thouless Phase Transition of the ANNNI model in Two Dimensions

The spin structure of an axial next-nearest-neighbor Ising (ANNNI) model in two dimensions (2D) is a renewed problem because different Monte Carlo (MC) simulation methods predicted different spin orderings. The usual equilibrium simulation predicts the occurrence of a floating incommensurate (IC) Kosterlitz-Thouless (KT) type phase, which never emerges in non-equilibrium relaxation (NER) simulations. In this paper, we first examine previously published results of both methods, and then investigate a higher transition temperature, $T_{c1}$, between the IC and paramagnetic phases. In the usual equilibrium simulation, we calculate the layer magnetization on larger lattices (up to $512 \times 512$ sites) and estimate $T_{c1} \approx 1.16J$ with frustration ratio $\kappa (\equiv -J_2/J_1) = 0.6$. We examine the nature of the phase transition in terms of the Binder ratio $g_L$ of spin overlap functions and the correlation-length ratio $\xi/L$. In the NER simulation, we observe the spin dynamics in equilibrium states by means of an autocorrelation function, and also observe the layer magnetization relaxations from the ground and disordered states. These quantities exhibit an algebraic decay at $T \lesssim 1.17J$. We conclude that the two-dimensional ANNNI model actually admits an IC phase transition of the KT type.Comment: 20 pages, 16 figure

Developmental defects and male sterility in mice lacking the ubiquitin-like DNA repair gene mHR23B.

mHR23B encodes one of the two mammalian homologs of Saccharomyces cerevisiae RAD23, a ubiquitin-like fusion protein involved in nucleotide excision repair (NER). Part of mHR23B is complexed with the XPC protein, and this heterodimer functions as the main damage detector and initiator of global genome NER. While XPC defects exist in humans and mice, mutations for mHR23A and mHR23B are not known. Here, we present a mouse model for mHR23B. Unlike XPC-deficient cells, mHR23B(-/-) mouse embryonic fibroblasts are not UV sensitive and retain the repair characteristics of wild-type cells. In agreement with the results of in vitro repair studies, this indicates that mHR23A can functionally replace mHR23B in NER. Unexpectedly, mHR23B(-/-) mice show impaired embryonic development and a high rate (90%) of intrauterine or neonatal death. Surviving animals display a variety of abnormalities, including retarded growth, facial dysmorphology, and male sterility. Such abnormalities are not observed in XPC and other NER-deficient mouse mutants and point to a separate function of mHR23B in development. This function may involve regulation of protein stability via the ubiquitin/proteasome pathway and is not or only in part compensated for by mHR23A

Named entity recognition using a new fuzzy support vector machine.

Recognizing and extracting exact name entities, like Persons, Locations, Organizations, Dates and Times are very useful to mining information from electronics resources and text. Learning to extract these types of data is called Named Entity Recognition(NER) task. Proper named entity recognition and extraction is important to solve most problems in hot research area such as Question Answering and Summarization Systems, Information Retrieval and Information Extraction, Machine Translation, Video Annotation, Semantic Web Search and Bioinformatics, especially Gene identification, proteins and DNAs names. Nowadays more researchers use three type of approaches namely, Rule-base NER, Machine Learning-base NER and Hybrid NER to identify names. Machine learning method is more famous and applicable than others, because it’s more portable and domain independent. Some of the Machine learning algorithms used in NER methods are, support vector machine(SVM), Hidden Markov Model, Maximum Entropy Model (MEM) and Decision Tree. In this paper, we review these methods and compare them based on precision in recognition and also portability using the Message Understanding Conference(MUC) named entity definition and its standard data set to find their strength and weakness of each these methods. We have improved the precision in NER from text using the new proposed method that calls FSVM for NER. In our method we have employed Support Vector Machine as one of the best machine learning algorithm for classification and we contribute a new fuzzy membership function thus removing the Support Vector Machine’s weakness points in NER precision and multi classification. The design of our method is a kind of One-Against-All multi classification technique to solve the traditional binary classifier in SVM