Heterogeneous Impacts in PROGRESA

The “common effect” model in program evaluation assumes that all treated individuals have the same impact from a program. Our paper contributes to the recent literature that tests and goes beyond the common effect model by investigating impact heterogeneity using data from the experimental evaluation of the Mexican conditional cash transfer program PROGRESA. Our analysis builds upon and extends that in Heckman, Smith and Clements (1997) and more recent studies of quantile treatment effects and random coefficient models. We find strong evidence of systematic (i.e. subgroup) variation in impacts in PROGRESA and modest evidence of heterogeneous impacts conditional on the systematic impacts. We find evidence against the perfect positive dependence assumption that underlies the interpretation of quantile treatment effects as impacts at quantiles of the untreated outcome distribution. Our paper concludes with a discussion of the policy relevance of our findings and of heterogeneous impacts more generally.randomized experiment, quantile treatment effects, heterogeneous impacts

Robust GPS Satellite Signal Acquisition Using Lifting Wavelet Transform

A novel GPS satellite signal acquisition scheme that utilizes lifting wavelet to improve acquisition performance is proposed. Acquisition in GPS system is used to calculate the code phase (or shift) and find the pseudo-range, which is used to calculate the position. The performance of a GPS receiver is assessed by its ability to precisely measure the pseudo-range, which depends on noise linked to the signals in the receiver’s tracking loops. The level of GPS receiving equipment system noise determines in part how precisely pseudo-range can be measured. Our objective, in this paper, is to achieve robust real-time positioning with maximum of accuracy in the presence of noise. Robust positioning describes a positioning system\'s ability to maintain position data continuity and accuracy through most or all anticipated operational conditions. In order to carry out a robust less complex GPS signals acquisition system and to facilitate its implementation, a substitute algorithm for calculating the convolution by using lifting wavelet decomposition is proposed. Simulation is used for verifying the performance which shows that the proposed scheme based lifting wavelet transform outperforms both FFT search and signal decimation schemes in the presence of a hostile environment

A blind channel shortening for multiuser, multicarrier CDMA system over multipath fading channel

In this paper we derive the Multicarrier Equalization by Restoration of Redundancy (MERRY) algorithm: A blind, adaptive channel shortening algorithm for updating a Time-domain Equalizer (TEQ) in a system employing MultiCarrier Code Division Multiple Access (MC-CDMA) modulation. We show that the MERRY algorithm applied to the MC-CDMA system converges considerably more rapidly than in the Orthogonal Frequency Division Multiplexing (OFDM) system [1]. Simulations results are provided to demonstrate the performance of the algorithm

Comparison between Suitable Priors for Additive Bayesian Networks

Additive Bayesian networks are types of graphical models that extend the usual Bayesian generalized linear model to multiple dependent variables through the factorisation of the joint probability distribution of the underlying variables. When fitting an ABN model, the choice of the prior of the parameters is of crucial importance. If an inadequate prior - like a too weakly informative one - is used, data separation and data sparsity lead to issues in the model selection process. In this work a simulation study between two weakly and a strongly informative priors is presented. As weakly informative prior we use a zero mean Gaussian prior with a large variance, currently implemented in the R-package abn. The second prior belongs to the Student's t-distribution, specifically designed for logistic regressions and, finally, the strongly informative prior is again Gaussian with mean equal to true parameter value and a small variance. We compare the impact of these priors on the accuracy of the learned additive Bayesian network in function of different parameters. We create a simulation study to illustrate Lindley's paradox based on the prior choice. We then conclude by highlighting the good performance of the informative Student's t-prior and the limited impact of the Lindley's paradox. Finally, suggestions for further developments are provided.Comment: 8 pages, 4 figure

Protective Effect of Green Tea (Camellia sinensis (L.) Kuntze) against Prostate Cancer: From in Vitro Data to Algerian Patients

© 2017 Somia Lassed et al.Green tea (GT) has been studied for its effects as antioxidant and cancer-preventive agent. Epidemiological studies showed that GT consumption decreases the risk for prostate cancer (PC). To investigate whether erythrocyte oxidative stress (OS) is associated with PC and whether daily consumption of GT improves the oxidative phenotype, we performed a study in a group of Algerian PC patients, preceded by an in vitro study to characterize composition and antioxidant/antiproliferative activities of the GT used. This contained a high content of phenolic and flavonoid compounds, demonstrating in vitro antioxidant activity and significant antiproliferative effect on human prostate cancer PC-3 cell line. Seventy PC patients and 120 age-matched healthy subjects participated in the study, with glutathione (GSH), malondialdehyde (MDA), and catalase activity evaluated before and after GT consumption. The results showed a reduced GSH and catalase activity and a high level of MDA in erythrocytes from PC patients. The consumption of 2-3 cups per day of GT during 6 months significantly increased GSH concentration and catalase activity and decreased MDA concentration. In conclusion, GT significantly decreased OS in Algerian PC patients. Regular consumption of GT for a long period may prevent men from developing PC or at least delay its progression

Leveraging gene expression subgroups to classify DLBCL patients and select for clinical benefit from a novel agent

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, commonly described by cell-of-origin (COO) molecular subtypes. We sought to identify novel patient subgroups through an unsupervised analysis of a large public dataset of gene expression profiles from newly diagnosed de novo DLBCL patients, yielding 2 biologically distinct subgroups characterized by differences in the tumor microenvironment. Pathway analysis and immune deconvolution algorithms identified higher B-cell content and a strong proliferative signal in subgroup A and enriched T-cell, macrophage, and immune/inflammatory signals in subgroup B, reflecting similar biology to published DLBCL stratification research. A gene expression classifier, featuring 26 gene expression scores, was derived from the public dataset to discriminate subgroup A (classifier-negative, immune-low) and subgroup B (classifier-positive, immune-high) patients. Subsequent application to an independent series of diagnostic biopsies replicated the subgroups, with immune cell composition confirmed via immunohistochemistry. Avadomide, a CRL4CRBN E3 ubiquitin ligase modulator, demonstrated clinical activity in relapsed/refractory DLBCL patients, independent of COO subtypes. Given the immunomodulatory activity of avadomide and the need for a patient-selection strategy, we applied the gene expression classifier to pretreatment biopsies from relapsed/refractory DLBCL patients receiving avadomide (NCT01421524). Classifier-positive patients exhibited an enrichment in response rate and progression-free survival of 44% and 6.2 months vs 19% and 1.6 months for classifier-negative patients (hazard ratio, 0.49; 95% confidence interval, 0.280-0.86; P = .0096). The classifier was not prognostic for rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone or salvage immunochemotherapy. The classifier described here discriminates DLBCL tumors based on tumor and nontumor composition and has potential utility to enrich for clinical response to immunomodulatory agents, including avadomide

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

Outcomes of anti-CD38 isatuximab plus pomalidomide and dexamethasone in five relapsed myeloma patients with prior exposure to anti-C38 daratumumab: case series

© 2022 The Authors. Published by Taylor & Francis and International Society of Hematology. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1080/16078454.2022.2028978Objectives: Daratumumab is the first anti-CD38 monoclonal antibody (Mab) used to treat myeloma in the newly diagnosed setting and in the relapsed setting. Isatuximab, another Mab targeting a specific epitope on the CD38 receptor, was recently approved in the UK in combination with pomalidomide and dexamethasone (IsaPomDex) to treat myeloma patients who received three prior lines of therapy. However, there is a lack of understanding of whether using a prior anti-CD38 Mab (e.g. daratumumab) can affect the efficacy of another Mab (e.g. isatuximab), when the latter is used to treat a subsequent relapse.Methods: We performed a UK-wide outcomes study of IsaPomDex in the real-world. In this case series, we report a detailed descriptive analysis of the characteristics and clinical outcomes of five IsaPomDex patients in UK routine practice (Patients I to V), with a prior exposure to daratumumab.Results: Age range was 51-77 years with two patients >70 and three patients <70 years. The cytogenetic risk was standard in two patients, high in two patients and not known in one patient. Prior daratumumab regimen were monotherapy (dara-mono) in one patient (II), and daratumumab with bortezomib and dexamethasone (DVd) in four patients. Responses to prior daratumumab were: very good partial response (VGPR) in two patients (I and III), minor response-stable disease (MR-SD) in one patient (II), and progressive disease (PD) in two patients (IV and V). Median (range) number of IsaPomDex cycles received was 2 (1-4). Outcomes of IsaPomDex were PD in three patients (II, IV and V) and a response in two patients. Response categories were: MR-SD in patient I and PR in patient III.Discussion: Despite the limitations of our case series, we described the first UK real-world report of IsaPomDex outcomes in myeloma patients with a prior exposure to daratumumab.Conclusion: Large prospective studies are required to further evaluate myeloma outcomes in this setting.Published versio

FORG3D: Force-directed 3D graph editor for visualization of integrated genome scale data

<p>Abstract</p> <p>Background</p> <p>Genomics research produces vast amounts of experimental data that needs to be integrated in order to understand, model, and interpret the underlying biological phenomena. Interpreting these large and complex data sets is challenging and different visualization methods are needed to help produce knowledge from the data.</p> <p>Results</p> <p>To help researchers to visualize and interpret integrated genomics data, we present a novel visualization method and bioinformatics software tool called FORG3D that is based on real-time three-dimensional force-directed graphs. FORG3D can be used to visualize integrated networks of genome scale data such as interactions between genes or gene products, signaling transduction, metabolic pathways, functional interactions and evolutionary relationships. Furthermore, we demonstrate its utility by exploring gene network relationships using integrated data sets from a <it>Caenorhabditis elegans </it>Parkinson's disease model.</p> <p>Conclusion</p> <p>We have created an open source software tool called FORG3D that can be used for visualizing and exploring integrated genome scale data.</p