Spinoza

"Spinoza", second edition. Encyclopedia entry for the Springer Encyclopedia of EM Phil and the Sciences, ed. D. Jalobeanu and C. T. Wolfe

A categorification of Morelli's theorem

We prove a theorem relating torus-equivariant coherent sheaves on toric varieties to polyhedrally-constructible sheaves on a vector space. At the level of K-theory, the theorem recovers Morelli's description of the K-theory of a smooth projective toric variety. Specifically, let $X$ be a proper toric variety of dimension $n$ and let M_\bR = \mathrm{Lie}(T_\bR^\vee)\cong \bR^n be the Lie algebra of the compact dual (real) torus T_\bR^\vee\cong U(1)^n. Then there is a corresponding conical Lagrangian \Lambda \subset T^*M_\bR and an equivalence of triangulated dg categories \Perf_T(X) \cong \Sh_{cc}(M_\bR;\Lambda), where \Perf_T(X) is the triangulated dg category of perfect complexes of torus-equivariant coherent sheaves on $X$ and \Sh_{cc}(M_\bR;\Lambda) is the triangulated dg category of complex of sheaves on M_\bR with compactly supported, constructible cohomology whose singular support lies in $\Lambda$. This equivalence is monoidal---it intertwines the tensor product of coherent sheaves on $X$ with the convolution product of constructible sheaves on M_\bR.Comment: 20 pages. This is a strengthened version of the first half of arXiv:0811.1228v3, with new results; the second half becomes arXiv:0811.1228v

Letters to the Editor: Correspondence re R. Lapointe et al., CD40-stimulated B Lymphocytes Pulsed with Tumor Antigens Are Effective Antigen-presenting Cells That Can Generate Specific T Cells. Cancer Res 2003;63:2836–43.

La réplique provient de Réjean Lapointe, Jacques Thibodeau et Patrick Hwu; Réjean Lapointe et Jacques Thibodeau sont affiliés à la faculté de médecine de l'Université de Montréa

Quality of survival: a new concept framework to assess the quality of prolonged life in cancer

Background: Improved cancer care means that more patients are surviving longer, but there is a need to examine how well patients survive. We conducted an exploratory analysis of a new conceptual framework termed ‘quality of survival’ (QoS) that delineates the quality of patients’ experience. Methods: This project included an electronic database search to investigate the survivorship landscape and to create a visual QoS map and semi-structured interviews with patients (n = 35), clinicians (n = 40), and payers (n = 7) to support the QoS map. QoS was discussed in the context of two tumor types, metastatic non-small cell lung cancer and metastatic melanoma. Results: Despite increased long-term survival, no specific definition of QoS exists. Patients reported many impacts that affect QoS, clinicians viewed QoS as relevant to treatment decisions, and payers felt it could help communicate different aspects relevant to the patient. Four interconnected QoS dimensions were developed (quality of life, survival, side effects, and economic impact), which vary in importance along the care continuum. Conclusion: QoS is a patient-centric concept that could help decision-making and patient communication. The QoS map could provide a framework to monitor patient experience and help patients frame what treatment attribute is most important to them at any point in the cancer continuum

Random Walks on a Fluctuating Lattice: A Renormalization Group Approach Applied in One Dimension

We study the problem of a random walk on a lattice in which bonds connecting nearest neighbor sites open and close randomly in time, a situation often encountered in fluctuating media. We present a simple renormalization group technique to solve for the effective diffusive behavior at long times. For one-dimensional lattices we obtain better quantitative agreement with simulation data than earlier effective medium results. Our technique works in principle in any dimension, although the amount of computation required rises with dimensionality of the lattice.Comment: PostScript file including 2 figures, total 15 pages, 8 other figures obtainable by mail from D.L. Stei

Characterization of the stretched exponential trap-time distributions in one-dimensional coupled map lattices

Stretched exponential distributions and relaxation responses are encountered in a wide range of physical systems such as glasses, polymers and spin glasses. As found recently, this type of behavior occurs also for the distribution function of certain trap time in a number of coupled dynamical systems. We analyze a one-dimensional mathematical model of coupled chaotic oscillators which reproduces an experimental set-up of coupled diode-resonators and identify the necessary ingredients for stretched exponential distributions.Comment: 8 pages, 8 figure

Psychological Sequelae of the Station Nightclub Fire: Comparing Survivors with and without Physical Injuries Using a Mixed-Methods Analysis

Background: Surveying survivors from a large fire provides an opportunity to explore the impact of emotional trauma on psychological outcomes. Methods: This is a cross-sectional survey of survivors of The Station Fire. Primary outcomes were post-traumatic stress (Impact of Event Scale – Revised) and depressive (Beck Depression Inventory) symptoms. Linear regression was used to examine differences in symptom profiles between those with and without physical injuries. The free-response section of the survey was analyzed qualitatively to compare psychological sequelae of survivors with and without physical injuries. Results: 104 participants completed the study survey; 47% experienced a burn injury. There was a 42% to 72% response rate range. The mean age of respondents was 32 years, 62% were male, and 47% experienced a physical injury. No significant relationships were found between physical injury and depressive or post-traumatic stress symptom profiles. In the qualitative analysis, the emotional trauma that survivors experienced was a major, common theme regardless of physical injury. Survivors without physical injuries were more likely to experience survivor guilt, helplessness, self-blame, and bitterness. Despite the post-fire challenges described, most survivors wrote about themes of recovery and renewal. Conclusions: All survivors of this large fire experienced significant psychological sequelae. These findings reinforce the importance of mental health care for all survivors and suggest a need to understand factors influencing positive outcomes

Benefits of biomarker selection and clinico-pathological covariate inclusion in breast cancer prognostic models

Introduction: Multi-marker molecular assays have impacted management of early stage breast cancer, facilitating adjuvant chemotherapy decisions. We generated prognostic models that incorporate protein-based molecular markers and clinico-pathological variables to improve survival prediction. Methods: We used a quantitative immunofluorescence method to study protein expression of 14 markers included in the Oncotype DX™ assay on a 638 breast cancer patient cohort with 15-year follow-up. We performed cross-validation analyses to assess performance of multivariate Cox models consisting of these markers and standard clinico-pathological covariates, using an average time-dependent Area Under the Receiver Operating Characteristic curves and compared it to nested Cox models obtained by robust backward selection procedures. Results: A prognostic index derived from of a multivariate Cox regression model incorporating molecular and clinico-pathological covariates (nodal status, tumor size, nuclear grade, and age) is superior to models based on molecular studies alone or clinico-pathological covariates alone. Performance of this composite model can be further improved using feature selection techniques to prune variables. When stratifying patients by Nottingham Prognostic Index (NPI), the most prognostic markers in high and low NPI groups differed. Similarly, for the node-negative, hormone receptor-positive sub-population, we derived a compact model with three clinico-pathological variables and two protein markers that was superior to the full model. Conclusions: Prognostic models that include both molecular and clinico-pathological covariates can be more accurate than models based on either set of features alone. Furthermore, feature selection can decrease the number of molecular variables needed to predict outcome, potentially resulting in less expensive assays.This work was supported by a grant from the Susan G Komen Foundation (to YK)