Université de Montréal

About: Université de Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 45641 authors who have published 100476 publications receiving 4004007 citations. The organization is also known as: University of Montreal & UdeM.

Ecology and Management of the Soybean Aphid in North America

Abstract: The soybean aphid, Aphis glycines Matsumura, has become the single most important arthropod pest of soybeans in North America. Native to Asia, this invasive species was first discovered in North America in July 2000 and has rapidly spread throughout the northcentral United States, much of southeastern Canada, and the northeastern United States. In response, important elements of the ecology of the soybean aphid in North America have been elucidated, with economic thresholds, sampling plans, and chemical control recommendations widely adopted. Aphid-resistant soybean varieties were available to growers in 2010. The preexisting community of aphid natural enemies has been highly effective in suppressing aphid populations in many situations, and classical biological control efforts have focused on the addition of parasitoids of Asian origin. The keys to sustainable management of this pest include understanding linkages between the soybean aphid and other introduced and native species in a landscape context al...

Community ecology in the age of multivariate multiscale spatial analysis

Abstract: Species spatial distributions are the result of population demography, behavioral traits, and species interactions in spatially heterogeneous environmental conditions. Hence the composition of species assemblages is an integrative response variable, and its variability can be explained by the complex interplay among several structuring factors. The thorough analysis of spatial variation in species assemblages may help infer processes shaping ecological communities. We suggest that ecological studies would benefit from the combined use of the classical statistical models of community composition data, such as constrained or unconstrained multivariate analyses of site-by-species abundance tables, with rapidly emerging and diversifying methods of spatial pattern analysis. Doing so allows one to deal with spatially explicit ecological models of beta diversity in a biogeographic context through the multiscale analysis of spatial patterns in original species data tables, including spatial characterization of fitted or residual variation from environmental models. We summarize here the recent progress for specifying spatial features through spatial weighting matrices and spatial eigenfunctions in order to define spatially constrained or scale-explicit multivariate analyses. Through a worked example on tropical tree communities, we also show the potential of the overall approach to identify significant residual spatial patterns that could arise from the omission of important unmeasured explanatory variables or processes.

Cartilage in normal and osteoarthritis conditions.

Abstract: The preservation of articular cartilage depends on keeping the cartilage architecture intact. Cartilage strength and function depend on both the properties of the tissue and on their structural parameters. The main structural macromolecules are collagen and proteoglycans (aggrecan). During life, cartilage matrix turnover is mediated by a multitude of complex autocrine and paracrine anabolic and catabolic factors. These act on the chondrocytes and can lead to repair, remodeling or catabolic processes like those that occur in osteoarthritis. Osteoarthritis is characterized by degradation and loss of articular cartilage, subchondral bone remodeling, and, at the clinical stage of the disease, inflammation of the synovial membrane. The alterations in osteoarthritic cartilage are numerous and involve morphologic and metabolic changes in chondrocytes, as well as biochemical and structural alterations in the extracellular matrix macromolecules.

Imagery Rehearsal Therapy for Chronic Nightmares in Sexual Assault Survivors With Posttraumatic Stress Disorder: A Randomized Controlled Trial

Abstract: ContextChronic nightmares occur frequently in patients with posttraumatic stress disorder (PTSD) but are not usually a primary target of treatment.ObjectiveTo determine if treating chronic nightmares with imagery rehearsal therapy (IRT) reduces the frequency of disturbing dreams, improves sleep quality, and decreases PTSD symptom severity.Design, Setting, and ParticipantsRandomized controlled trial conducted from 1995 to 1999 among 168 women in New Mexico; 95% had moderate-to-severe PTSD, 97% had experienced rape or other sexual assault, 77% reported life-threatening sexual assault, and 58% reported repeated exposure to sexual abuse in childhood or adolescence.InterventionParticipants were randomized to receive treatment (n = 88) or to the wait-list control group (n = 80). The treatment group received IRT in 3 sessions; controls received no additional intervention, but continued any ongoing treatment.Main Outcome MeasuresScores on the Nightmare Frequency Questionnaire (NFQ), Pittsburgh Sleep Quality Index (PSQI), PTSD Symptom Scale (PSS), and Clinician-Administered PTSD Scale (CAPS) at 3- and 6-month follow-up.ResultsA total of 114 participants completed follow-up at 3 and/or 6 months. Comparing baseline to follow-up (n = 97-114), treatment significantly reduced nights per week with nightmares (Cohen d = 1.24; P<.001) and number of nightmares per week (Cohen d = 0.85; P<.001) on the NFQ and improved sleep (on the PSQI, Cohen d = 0.67; P<.001) and PTSD symptoms (on the PSS, Cohen d = 1.00; P<.001 and on the CAPS, Cohen d = 1.53; P<.001). Control participants showed small, nonsignificant improvements for the same measures (mean Cohen d = 0.21). In a 3-point analysis (n = 66-77), improvements occurred in the treatment group at 3-month follow-up (treatment vs control group, Cohen d = 1.15 vs 0.07 for nights per week with nightmares; 0.95 vs −0.06 for nightmares per week; 0.77 vs 0.31 on the PSQI, and 1.06 vs 0.31 on the PSS) and were sustained without further intervention or contact between 3 and 6 months. An intent-to-treat analysis (n = 168) confirmed significant differences between treatment and control groups for nightmares, sleep, and PTSD (all P<.02) with moderate effect sizes for treatment (mean Cohen d = 0.60) and small effect sizes for controls (mean Cohen d = 0.14). Posttraumatic stress symptoms decreased by at least 1 level of clinical severity in 65% of the treatment group compared with symptoms worsening or not changing in 69% of controls (χ21 = 12.80; P<.001).ConclusionsImagery rehearsal therapy is a brief, well-tolerated treatment that appears to decrease chronic nightmares, improve sleep quality, and decrease PTSD symptom severity.

A semantic matching energy function for learning with multi-relational data

Abstract: Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval, to natural language processing. In this paper, we present a new neural network architecture designed to embed multi-relational graphs into a flexible continuous vector space in which the original data is kept and enhanced. The network is trained to encode the semantics of these graphs in order to assign high probabilities to plausible components. We empirically show that it reaches competitive performance in link prediction on standard datasets from the literature as well as on data from a real-world knowledge base (WordNet). In addition, we present how our method can be applied to perform word-sense disambiguation in a context of open-text semantic parsing, where the goal is to learn to assign a structured meaning representation to almost any sentence of free text, demonstrating that it can scale up to tens of thousands of nodes and thousands of types of relation.