Beyond the Detector Horizon:Forecasting Gravitational-Wave Strong Lensing

When gravitational waves pass near massive astrophysical objects, they can be gravitationally lensed. The lensing can split them into multiple wave-fronts, magnify them, or imprint beating patterns on the waves. Here we focus on the multiple images produced by strong lensing. In particular, we investigate strong lensing forecasts, the rate of lensing, and the role of lensing statistics in strong lensing searches. Overall, we find a reasonable rate of lensed detections for double, triple, and quadruple images at the LIGO--Virgo--KAGRA design sensitivity. We also report the rates for A+ and LIGO Voyager and briefly comment on potential improvements due to the inclusion of sub-threshold triggers. We find that most galaxy-lensed events originate from redshifts $z \sim 1-4$ and report the expected distribution of lensing parameters for the observed events. Besides forecasts, we investigate the role of lensing forecasts in strong lensing searches, which explore repeated event pairs. One problem associated with the searches is the rising number of event pairs, which leads to a rapidly increasing false alarm probability. We show how knowledge of the expected galaxy lensing time delays in our searches allow us to tackle this problem. Once the time delays are included, the false alarm probability increases linearly (similar to non-lensed searches) instead of quadratically with time, significantly improving the search. For galaxy cluster lenses, the improvement is less significant. The main uncertainty associated with these forecasts are the merger-rate density estimates at high redshift, which may be better resolved in the future

Generation of Functional CLL-Specific Cord Blood CTL Using CD40-Ligated CLL APC

PMCID: PMC3526610This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Update in the management of chronic lymphocytic leukemia

Advances in the treatment of chronic lymphocytic leukemia (CLL) have improved initial overall response (OR) rates, complete response (CR) rates and progression free survival (PFS). Despite these advances, CLL remains incurable with standard therapies. Thus, there remains a need for more effective therapies in both the upfront and relapsed setting, particularly for patients with high-risk cytogenetic abnormalities such as del(11q22) and del(17p13). The 2008 American Society of Hematology (ASH) Annual Meeting featured several presentations which highlighted the ongoing clinical advances in CLL. The benefit of adding rituximab to purine analog therapy in the upfront setting was demonstrated by a large randomized study which showed that the addition of rituximab to fludarabine and cyclophosphamide (FCR) significantly improved OR, CR and PFS. The improvement in PFS directly resulted from an improved ability to eliminate minimal residual disease (MRD) in the peripheral blood, highlighting the importance of MRD eradication. However, a multi-center study suggested that the high CR rates to chemoimmunotherapy regimens such as FCR obtained in academic centers may not be reproducible when the same regimens are given in the community setting. The immunomodulatory drug lenalidomide is active in relapsed high-risk CLL, but two studies of lenalidomide in previously untreated CLL patients failed to achieve a CR and were associated with significant tumor lysis, tumor flare and hematologic toxicity. In the relapsed setting, a combination study of the bifunctional alkylator bendamustine and rituximab (BR) demonstrated a high OR rate in patients with del(11q22) and del(17p13), indicating that further studies to define's bendamustine activity are warranted in high-risk CLL. Similarly, the CDK inhibitor flavopiridol demonstrated significant clinical activity and durable remissions in heavily treated, refractory CLL patients with high-risk cytogenetic features and bulky lymphadenopathy. The monoclonal anti-CD20 antibody ofatumumab appeared to be superior to rituximab in relapsed CLL patients with bulky nodal disease or high-risk cytogenetic features. Ongoing studies of these agents and other novel therapeutic agents in clinical development hold forth the promise that treatment options for CLL patients will continue to expand and improve

The Emerging Role of Ofatumumab in the Treatment of Chronic Lymphocytic Leukemia

The treatment of chronic lymphocytic leukemia (CLL) has evolved over the past decade. Our better understanding of disease biology and risk stratification has allowed delivering more effective therapies. In fact, front-line chemoimmunotherapy has demonstrated improvement in overall survival when compared to chemotherapy in randomized studies. Yet, treatment of relapsed CLL remains challenging and few agents are effective in that setting. Ofatumumab (Ofa) is a humanized monoclonal antibody targeted against CD20 with demonstrable activity in rituximab-resistant CLL cell lines. This agent was recently approved for the treatment of relapsed/refractory CLL patients who have failed fludarabine and alemtuzumab. In this review, we provide a historical perspective on approaches to CLL as front-line and in the relapsed setting. We further summarize novel anti-CD20 antibodies with specific emphasis on ofa. We review studies that led to ofatumumab’s approval including pre-clinical data, trials using ofa in combination therapies, and adverse events/toxicities reported with this agent

Photoswitchable diacylglycerols enable optical control of protein kinase C.

Increased levels of the second messenger lipid diacylglycerol (DAG) induce downstream signaling events including the translocation of C1-domain-containing proteins toward the plasma membrane. Here, we introduce three light-sensitive DAGs, termed PhoDAGs, which feature a photoswitchable acyl chain. The PhoDAGs are inactive in the dark and promote the translocation of proteins that feature C1 domains toward the plasma membrane upon a flash of UV-A light. This effect is quickly reversed after the termination of photostimulation or by irradiation with blue light, permitting the generation of oscillation patterns. Both protein kinase C and Munc13 can thus be put under optical control. PhoDAGs control vesicle release in excitable cells, such as mouse pancreatic islets and hippocampal neurons, and modulate synaptic transmission in Caenorhabditis elegans. As such, the PhoDAGs afford an unprecedented degree of spatiotemporal control and are broadly applicable tools to study DAG signaling

A Comparative Study of Different Methodologies for Fault Diagnosis in Multivariate Quality Control

Different methodologies for fault diagnosis in multivariate quality control have been proposed in recent years. These methods work in the space of the original measured variables and have performed reasonably well when there is a reduced number of mildly correlated quality and/or process variables with a well-conditioned covariance matrix. These approaches have been introduced by emphasizing their positive or negative virtues, generally on an individual basis, so it is not clear for the practitioner the best method to be used. This paper provides a comprehensive study of the performance of diverse methodological approaches when tested on a large number of distinct simulated scenarios. Our primary aim is to highlight key weaknesses and strengths in these methods as well as clarifying their relationships and the requirements for their implementation in practice.Vidal Puig, S.; Ferrer, A. (2014). A Comparative Study of Different Methodologies for Fault Diagnosis in Multivariate Quality Control. Communications in Statistics - Simulation and Computation. 43(5):986-1005. doi:10.1080/03610918.2012.720745S9861005435Arteaga, F., & Ferrer, A. (2010). How to simulate normal data sets with the desired correlation structure. Chemometrics and Intelligent Laboratory Systems, 101(1), 38-42. doi:10.1016/j.chemolab.2009.12.003Doganaksoy, N., Faltin, F. W., & Tucker, W. T. (1991). Identification of out of control quality characteristics in a multivariate manufacturing environment. Communications in Statistics - Theory and Methods, 20(9), 2775-2790. doi:10.1080/03610929108830667Fuchs, C., & Benjamini, Y. (1994). Multivariate Profile Charts for Statistical Process Control. Technometrics, 36(2), 182-195. doi:10.1080/00401706.1994.10485765Hawkins, D. M. (1991). Multivariate Quality Control Based on Regression-Adiusted Variables. Technometrics, 33(1), 61-75. doi:10.1080/00401706.1991.10484770Editorial Board. (2007). Computational Statistics & Data Analysis, 51(8), iii-v. doi:10.1016/s0167-9473(07)00125-9Hayter, A. J., & Tsui, K.-L. (1994). Identification and Quantification in Multivariate Quality Control Problems. Journal of Quality Technology, 26(3), 197-208. doi:10.1080/00224065.1994.11979526HOCHBERG, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75(4), 800-802. doi:10.1093/biomet/75.4.800HOMMEL, G. (1988). A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika, 75(2), 383-386. doi:10.1093/biomet/75.2.383Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28(4), 409-428. doi:10.1080/00224065.1996.11979699Li, J., Jin, J., & Shi, J. (2008). Causation-BasedT2Decomposition for Multivariate Process Monitoring and Diagnosis. Journal of Quality Technology, 40(1), 46-58. doi:10.1080/00224065.2008.11917712Mason, R. L., Tracy, N. D., & Young, J. C. (1995). Decomposition ofT2 for Multivariate Control Chart Interpretation. Journal of Quality Technology, 27(2), 99-108. doi:10.1080/00224065.1995.11979573Mason, R. L., Tracy, N. D., & Young, J. C. (1997). A Practical Approach for Interpreting Multivariate T2 Control Chart Signals. Journal of Quality Technology, 29(4), 396-406. doi:10.1080/00224065.1997.11979791Murphy, B. J. (1987). Selecting Out of Control Variables With the T 2 Multivariate Quality Control Procedure. The Statistician, 36(5), 571. doi:10.2307/2348668Rencher, A. C. (1993). The Contribution of Individual Variables to Hotelling’s T 2 , Wilks’ Λ, and R 2. Biometrics, 49(2), 479. doi:10.2307/2532560Roy, J. (1958). Step-Down Procedure in Multivariate Analysis. The Annals of Mathematical Statistics, 29(4), 1177-1187. doi:10.1214/aoms/1177706449Runger, G. C., Alt, F. B., & Montgomery, D. C. (1996). Contributors to a multivariate statistical process control chart signal. Communications in Statistics - Theory and Methods, 25(10), 2203-2213. doi:10.1080/03610929608831832Sankoh, A. J., Huque, M. F., & Dubey, S. D. (1997). Some comments on frequently used multiple endpoint adjustment methods in clinical trials. Statistics in Medicine, 16(22), 2529-2542. doi:10.1002/(sici)1097-0258(19971130)16:223.0.co;2-jTukey, J. W., Ciminera, J. L., & Heyse, J. F. (1985). Testing the Statistical Certainty of a Response to Increasing Doses of a Drug. Biometrics, 41(1), 295. doi:10.2307/253066