Cancer biomarkers detection using microstructured protein chip: implementation of customized multiplex immunoassay

Protein chips have demonstrated to be a sensitive and low cost solution to identify and detect tumor markers. However, efficient multiparametric analysis remains a challenge due to protein variability. Crucial parameters are the design of stable and reproducible surfaces which maintain biological activity of immobilized proteins, and immobilization conditions (buffer, pH, concentration). We have developed and characterized various surface chemistries for the immobilization of anti-tumor antigen antibodies onto microstructured glass slides. The effect of surface properties and antibody immobilization conditions was evaluated on the detection of tumor antigens involved in colorectal cancer. Experimental results demonstrated that each antibody displays variable biological activities depending on the surface chemistry and on the immobilization procedure. Under optimized conditions, we can reach a limit of detection in tumor antigen as low as 10 pM. Our microstructured chip offers the possibility to implement a customized multiplex immunoassay combining optimal immobilization condition for each antibody on the same chip

Using Latent Growth Modeling to Understand Longitudinal Effects in MIS Theory: A Primer

The use of structural equation modeling (SEM) has grown dramatically in the field of management information systems (MIS) in the past twenty years, but SEM’s focus has been primarily on cross-sectional data sets. Functionally, SEM has been used to test measurement and path models, but the SEM approach has not been applied to repeated measures designs. In this article, we describe latent growth models (LGMs), an extension of SEM, which focuses on how observed and/or latent variables change over time. The purpose of this paper is to provide a primer on the use of LGMs, as well as to advocate for its use to extend MIS theory. We illustrate several flexible applications of LGMs using longitudinal data, including conditional, unconditional, and dual growth models. We discuss the advantages of using LGMs over other more traditional longitudinal approaches, and highlight areas in MIS where researchers can use this technique effectively

Surfaces de réponse par krigeage pour l'optimisation de formes aérodynamiques

Les simulations numériques RANS, utilisées en aérodynamique pour évaluer la performance d'une forme, sont coûteuses en temps de calcul. Comme une optimisation nécessite l'étude de plusieurs dizaines de formes différentes, ce coût limite le champ des problèmes envisageables. C'est donc en employant une technique efficace pour le calcul de gradients (méthode adjointe) et un algorithme d'optimisation par gradient (méthode quasi-Newton) que sont habituellement traités les problèmes de minimisation de traînée en conception à Airbus. Cet algorithme converge très rapidement, mais vers un optimum local. Dans ce contexte, un nouvel optimiseur basé sur des surfaces de réponse construites par une méthode de Krigeage est proposé. Appliqué sur différents cas tests (profil, voilure, configuration complète), celui-ci semble effectivement apporter de nouvelles possibilités aux concepteurs : à un surcout modéré, la solution obtenue est meilleure. De plus, cet optimiseur semble aussi capable de traiter des problèmes de grande dimension (48 variables) en interpolant le vecteur gradient aux points de construction du Krigeage. En optimisation multidisciplinaire, les surfaces de réponse sont largement employées pour échanger facilement des données entre différentes disciplines. Ainsi, une approche d'optimisation bi-niveau avec couplage fluide/structure par surface de réponse est étudiée. L'application considérée traite de l'intégration d'une installation motrice (positionnement) sur un avion de transport civil. ABSTRACT : Aerodynamic shape optimization problems based on flow simulations solving RANS equations are computationally expensive. In order to keep this cost acceptable, optimization problems are usualy solved using a gradient based optimization algorithm and an adjoint solver to efficiently compute the gradient of the objective function. This framework, implemented at Airbus, converges quickly but to a local optimum. In this thesis, a response surface based optimizer using kriging model is described and applied to aerodynamic test cases. Compared to the gradient based optimizer, it achieves better function values at a moderate additionnal cost. Moreover, the use of gradient enhanced kriging (or cokriging model) enables to efficiently apply this new optimizer to high dimensional problems (48 design variables). In a multidisciplinary context, a bilevel optimization framework was applied to solve a problem of fluid/structure powerplant integration. Using this framework, both disciplines considered were handled independantly by discipline specific solvers and the coupling was achieved thanks to response surface

Studying Within-Person Changes in Work Motivation in the Short and Medium- Term: You Will Likely Need More Measurement Points than You Think!

En el estudio de la motivación laboral, el análisis intrapersona de datos provenientes de diseños longitudinales es hoy común en el área. Sin embargo, algunas características del diseño de investigación empleado pueden estar influyendo en la variabilidad intrapersona encontrada. En este trabajo analizamos cómo influye el número de medidas repetidas por participante en la varianza intrapersona de la motivación laboral. Mediante dos estudios (una revisión sistemática y un estudio empírico original) mostramos cómo el número de medidas repetidas influye significativamente en la varianza intrapersona encontrada, que llega a alcanzar valores máximos del 52%-54% sobre el total de la varianza. Para llegar a esos valores de varianza explicada en el caso de la motivación laboral se necesitarían un mínimo de 25-30 medidas repetidas para ser rigurosos en la medida de la varianza intraperson

Multi-modal Predictive Models of Diabetes Progression

With the increasing availability of wearable devices, continuous monitoring of individuals' physiological and behavioral patterns has become significantly more accessible. Access to these continuous patterns about individuals' statuses offers an unprecedented opportunity for studying complex diseases and health conditions such as type 2 diabetes (T2D). T2D is a widely common chronic disease that its roots and progression patterns are not fully understood. Predicting the progression of T2D can inform timely and more effective interventions to prevent or manage the disease. In this study, we have used a dataset related to 63 patients with T2D that includes the data from two different types of wearable devices worn by the patients: continuous glucose monitoring (CGM) devices and activity trackers (ActiGraphs). Using this dataset, we created a model for predicting the levels of four major biomarkers related to T2D after a one-year period. We developed a wide and deep neural network and used the data from the demographic information, lab tests, and wearable sensors to create the model. The deep part of our method was developed based on the long short-term memory (LSTM) structure to process the time-series dataset collected by the wearables. In predicting the patterns of the four biomarkers, we have obtained a root mean square error of 1.67% for HBA1c, 6.22 mg/dl for HDL cholesterol, 10.46 mg/dl for LDL cholesterol, and 18.38 mg/dl for Triglyceride. Compared to existing models for studying T2D, our model offers a more comprehensive tool for combining a large variety of factors that contribute to the disease

An Electronic Daily Diary Study of Anal Intercourse in Drug-Using Women

Women (N = 138) with histories of illicit drug use were recruited into an electronic diary study that used Android smartphones for data collection. The diary was to be completed each day for 12 weeks using an "app" created in HTML5 and accessed over the Internet via smartphone. Data collection included information on sexual behaviors with up to 10 partners per day and contextual factors surrounding sexual behavior such as drug use before/after, type of sexual behavior (oral, vaginal, anal), and other activities such as using condoms for vaginal and anal intercourse and use of sexual lubricants. The sample was predominantly African American (58 %); 20 % Latina, 20 % White and 2 % reported as Other. Most women reported either less than a high school education (33 %) or having a high school diploma (33 %). The mean age was 39 years (SD = 11.78). Anal intercourse occurred on days when women also reported using illicit drugs, specifically methamphetamine and cocaine. Anal intercourse was not an isolated sexual activity, but took place on days when vaginal intercourse and giving and receiving oral sex also occurred along with illicit drug use. Anal intercourse also occurred on days when women reported they wanted sex. HIV prevention interventions must address the risks of anal intercourse for women, taking into account concurrent drug use and sexual pleasure that may reduce individual harm-reduction behaviors

The relative importance of phytoplankton aggregates and zooplankton fecal pellets to carbon export: insights from free-drifting sediment trap deployments in naturally iron-fertilised waters near the Kerguelen Plateau

The first KErguelen Ocean and Plateau compared Study (KEOPS1), conducted in the naturally iron-fertilised Kerguelen bloom, demonstrated that fecal material was the main pathway for exporting carbon to the deep ocean during summer (January–February 2005), suggesting a limited role of direct export via phytodetrital aggregates. The KEOPS2 project reinvestigated this issue during the spring bloom initiation (October–November 2011), when zooplankton communities may exert limited grazing pressure, and further explored the link between carbon flux, export efficiency and dominant sinking particles depending upon surface plankton community structure. Sinking particles were collected in polyacrylamide gel-filled and standard free-drifting sediment traps (PPS3/3), deployed at six stations between 100 and 400 m, to examine flux composition, particle origin and their size distributions. Results revealed an important contribution of phytodetrital aggregates (49 ± 10 and 45 ± 22% of the total number and volume of particles respectively, all stations and depths averaged). This high contribution dropped when converted to carbon content (30 ± 16% of total carbon, all stations and depths averaged), with cylindrical fecal pellets then representing the dominant fraction (56 ± 19%).At 100 and 200 m depth, iron- and biomass-enriched sites exhibited the highest carbon fluxes (maxima of 180 and 84 ± 27 mg C m-2 d-1, based on gel and PPS3/3 trap collection respectively), especially where large fecal pellets dominated over phytodetrital aggregates. Below these depths, carbon fluxes decreased (48 ± 21% decrease on average between 200 and 400 m), and mixed aggregates composed of phytodetritus and fecal matter dominated, suggesting an important role played by physical aggregation in deep carbon export.Export efficiencies determined from gels, PPS3/3 traps and 234Th disequilibria (200 m carbon flux/net primary productivity) were negatively correlated to net primary productivity with observed decreases from ~ 0.2 at low-iron sites to ~ 0.02 at high-iron sites. Varying phytoplankton communities and grazing pressure appear to explain this negative relationship. Our work emphasises the need to consider detailed plankton communities to accurately identify the controls on carbon export efficiency, which appear to include small spatio-temporal variations in ecosystem structure

Methodological criteria for the assessment of moderators in systematic reviews of randomised controlled trials : a consensus study

Background: Current methodological guidelines provide advice about the assessment of sub-group analysis within RCTs, but do not specify explicit criteria for assessment. Our objective was to provide researchers with a set of criteria that will facilitate the grading of evidence for moderators, in systematic reviews. Method: We developed a set of criteria from methodological manuscripts (n = 18) using snowballing technique, and electronic database searches. Criteria were reviewed by an international Delphi panel (n = 21), comprising authors who have published methodological papers in this area, and researchers who have been active in the study of sub-group analysis in RCTs. We used the Research ANd Development/University of California Los Angeles appropriateness method to assess consensus on the quantitative data. Free responses were coded for consensus and disagreement. In a subsequent round additional criteria were extracted from the Cochrane Reviewers’ Handbook, and the process was repeated. Results: The recommendations are that meta-analysts report both confirmatory and exploratory findings for subgroups analysis. Confirmatory findings must only come from studies in which a specific theory/evidence based apriori statement is made. Exploratory findings may be used to inform future/subsequent trials. However, for inclusion in the meta-analysis of moderators, the following additional criteria should be applied to each study: Baseline factors should be measured prior to randomisation, measurement of baseline factors should be of adequate reliability and validity, and a specific test of the interaction between baseline factors and interventions must be presented. Conclusions: There is consensus from a group of 21 international experts that methodological criteria to assess moderators within systematic reviews of RCTs is both timely and necessary. The consensus from the experts resulted in five criteria divided into two groups when synthesising evidence: confirmatory findings to support hypotheses about moderators and exploratory findings to inform future research. These recommendations are discussed in reference to previous recommendations for evaluating and reporting moderator studies