A Type-Theoretic Account of Neg-Raising Predicates in Tree Adjoining Grammars

International audienceNeg-Raising (NR) verbs form a class of verbs with a clausal complement that show the following behavior: when a negation syntactically attaches to the matrix predicate, it can semantically attach to the embedded predicate. This paper presents an account of NR predicates within Tree Adjoining Grammar (TAG). We propose a lexical semantic interpretation that heavily relies on a Montague-like semantics for TAG and on higher-order types

V2368 Oph: An eclipsing and double-lined spectroscopic binary used as a photometric comparison star for U Oph

The A-type star HR 6412 = V2368 Oph was used by several investigators as a photometric comparison star for the known eclipsing binary U Oph but was found to be variable by three independent groups, including us. By analysing series of new spectral and photometric observations and a critical compilation of available radial velocities, we were able to find the correct period of light and radial-velocity variations and demonstrate that the object is an eclipsing and double-lined spectroscopic binary moving in a highly eccentric orbit. We derived a linear ephemeris T min.I = HJD (2454294.67 +/- 0.01) + (38.32712 +/- 0.00004)d x E and estimated preliminary basic physical properties of the binary. The dereddened UBV magnitudes and effective temperatures of the primary and secondary, based on our light- and velocity-curve solutions, led to distance estimates that agree with the Hipparcos distance within the errors. We find that the mass ratio must be close to one, but the limited number and wavelength range of our current spectra does not allow a truly precise determination of the binary masses. Nevertheless, our results show convincingly that both binary components are evolved away from the main sequence, which makes this system astrophysically very important. There are only a few similarly evolved A-type stars among known eclipsing binaries. Future systematic observations and careful analyses can provide very stringent tests for the stellar evolutionary theory.Comment: 10 pages, 7 figs, in press 2011 A&

Invasion speeds for structured populations in fluctuating environments

We live in a time where climate models predict future increases in environmental variability and biological invasions are becoming increasingly frequent. A key to developing effective responses to biological invasions in increasingly variable environments will be estimates of their rates of spatial spread and the associated uncertainty of these estimates. Using stochastic, stage-structured, integro-difference equation models, we show analytically that invasion speeds are asymptotically normally distributed with a variance that decreases in time. We apply our methods to a simple juvenile-adult model with stochastic variation in reproduction and an illustrative example with published data for the perennial herb, \emph{Calathea ovandensis}. These examples buttressed by additional analysis reveal that increased variability in vital rates simultaneously slow down invasions yet generate greater uncertainty about rates of spatial spread. Moreover, while temporal autocorrelations in vital rates inflate variability in invasion speeds, the effect of these autocorrelations on the average invasion speed can be positive or negative depending on life history traits and how well vital rates ``remember'' the past

Global Considerations in Hierarchical Clustering Reveal Meaningful Patterns in Data

BACKGROUND: A hierarchy, characterized by tree-like relationships, is a natural method of organizing data in various domains. When considering an unsupervised machine learning routine, such as clustering, a bottom-up hierarchical (BU, agglomerative) algorithm is used as a default and is often the only method applied. METHODOLOGY/PRINCIPAL FINDINGS: We show that hierarchical clustering that involve global considerations, such as top-down (TD, divisive), or glocal (global-local) algorithms are better suited to reveal meaningful patterns in the data. This is demonstrated, by testing the correspondence between the results of several algorithms (TD, glocal and BU) and the correct annotations provided by experts. The correspondence was tested in multiple domains including gene expression experiments, stock trade records and functional protein families. The performance of each of the algorithms is evaluated by statistical criteria that are assigned to clusters (nodes of the hierarchy tree) based on expert-labeled data. Whereas TD algorithms perform better on global patterns, BU algorithms perform well and are advantageous when finer granularity of the data is sought. In addition, a novel TD algorithm that is based on genuine density of the data points is presented and is shown to outperform other divisive and agglomerative methods. Application of the algorithm to more than 500 protein sequences belonging to ion-channels illustrates the potential of the method for inferring overlooked functional annotations. ClustTree, a graphical Matlab toolbox for applying various hierarchical clustering algorithms and testing their quality is made available. CONCLUSIONS: Although currently rarely used, global approaches, in particular, TD or glocal algorithms, should be considered in the exploratory process of clustering. In general, applying unsupervised clustering methods can leverage the quality of manually-created mapping of proteins families. As demonstrated, it can also provide insights in erroneous and missed annotations

Crystal structure of SEL1L: Insight into the roles of SLR motifs in ERAD pathway

Terminally misfolded proteins are selectively recognized and cleared by the endoplasmic reticulum-associated degradation (ERAD) pathway. SEL1L, a component of the ERAD machinery, plays an important role in selecting and transporting ERAD substrates for degradation. We have determined the crystal structure of the mouse SEL1L central domain comprising five Sel1-Like Repeats (SLR motifs 5 to 9; hereafter called SEL1Lcent). Strikingly, SEL1Lcent forms a homodimer with two-fold symmetry in a head-to-tail manner. Particularly, the SLR motif 9 plays an important role in dimer formation by adopting a domain-swapped structure and providing an extensive dimeric interface. We identified that the full-length SEL1L forms a self-oligomer through the SEL1Lcent domain in mammalian cells. Furthermore, we discovered that the SLR-C, comprising SLR motifs 10 and 11, of SEL1L directly interacts with the N-terminus luminal loops of HRD1. Therefore, we propose that certain SLR motifs of SEL1L play a unique role in membrane bound ERAD machinery.ope

EnzyMiner: automatic identification of protein level mutations and their impact on target enzymes from PubMed abstracts

BACKGROUND: A better understanding of the mechanisms of an enzyme's functionality and stability, as well as knowledge and impact of mutations is crucial for researchers working with enzymes. Though, several of the enzymes' databases are currently available, scientific literature still remains at large for up-to-date source of learning the effects of a mutation on an enzyme. However, going through vast amounts of scientific documents to extract the information on desired mutation has always been a time consuming process. In this paper, therefore, we describe an unique method, termed as EnzyMiner, which automatically identifies the PubMed abstracts that contain information on the impact of a protein level mutation on the stability and/or the activity of a given enzyme. RESULTS: We present an automated system which identifies the abstracts that contain an amino-acid-level mutation and then classifies them according to the mutation's effect on the enzyme. In the case of mutation identification, MuGeX, an automated mutation-gene extraction system has an accuracy of 93.1% with a 91.5 F-measure. For impact analysis, document classification is performed to identify the abstracts that contain a change in enzyme's stability or activity resulting from the mutation. The system was trained on lipases and tested on amylases with an accuracy of 85%. CONCLUSION: EnzyMiner identifies the abstracts that contain a protein mutation for a given enzyme and checks whether the abstract is related to a disease with the help of information extraction and machine learning techniques. For disease related abstracts, the mutation list and direct links to the abstracts are retrieved from the system and displayed on the Web. For those abstracts that are related to non-diseases, in addition to having the mutation list, the abstracts are also categorized into two groups. These two groups determine whether the mutation has an effect on the enzyme's stability or functionality followed by displaying these on the web