Resistance prediction in AML: analysis of 4601 patients from MRC/NCRI, HOVON/SAKK, SWOG and MD Anderson Cancer Center

Therapeutic resistance remains the principal problem in acute myeloid leukemia (AML). We used area under receiver-operating characteristic curves (AUCs) to quantify our ability to predict therapeutic resistance in individual patients, where AUC=1.0 denotes perfect prediction and AUC=0.5 denotes a coin flip, using data from 4601 patients with newly diagnosed AML given induction therapy with 3+7 or more intense standard regimens in UK Medical Research Council/National Cancer Research Institute, Dutch–Belgian Cooperative Trial Group for Hematology/Oncology/Swiss Group for Clinical Cancer Research, US cooperative group SWOG and MD Anderson Cancer Center studies. Age, performance status, white blood cell count, secondary disease, cytogenetic risk and FLT3-ITD/NPM1 mutation status were each independently associated with failure to achieve complete remission despite no early death (‘primary refractoriness’). However, the AUC of a bootstrap-corrected multivariable model predicting this outcome was only 0.78, indicating only fair predictive ability. Removal of FLT3-ITD and NPM1 information only slightly decreased the AUC (0.76). Prediction of resistance, defined as primary refractoriness or short relapse-free survival, was even more difficult. Our limited ability to forecast resistance based on routinely available pretreatment covariates provides a rationale for continued randomization between standard and new therapies and supports further examination of genetic and posttreatment data to optimize resistance prediction in AML.

Metastases in normal-sized pelvic lymph nodes: detection with diffusion-weighted MR imaging

PURPOSE To prospectively assess the diagnostic performance of diffusion-weighted (DW) magnetic resonance (MR) imaging in the detection of pelvic lymph node metastases in patients with prostate and/or bladder cancer staged as N0 with preoperative cross-sectional imaging. MATERIALS AND METHODS This study was approved by an independent ethics committee. Written informed consent was obtained from all patients. Patients with no enlarged lymph nodes on preoperative cross-sectional images who were scheduled for radical resection of the primary tumor and extended pelvic lymph node dissection were enrolled. All patients were examined with a 3-T MR unit, and examinations included conventional and DW MR imaging of the entire pelvis. Image analysis was performed by three independent readers blinded to any clinical information. Metastases were diagnosed on the basis of high signal intensity on high b value DW MR images and morphologic features (shape, border). Histopathologic examination served as the standard of reference. Sensitivity and specificity were calculated, and bias-corrected 95% confidence intervals (CIs) were obtained with the bootstrap method. The Fleiss and Cohen κ and median test were applied for statistical analyses. RESULTS A total of 4846 lymph nodes were resected in 120 patients. Eighty-eight lymph node metastases were found in 33 of 120 patients (27.5%). Short-axis diameter of these metastases was less than or equal to 3 mm in 68, more than 3 mm to 5 mm in 13, more than 5 mm to 8 mm in five; and more than 8 mm in two. On a per-patient level, the three readers correctly detected metastases in 26 (79%; 95% CI: 64%, 91%), 21 (64%; 95% CI: 45%, 79%), and 25 (76%; 95% CI: 60%, 90%) of the 33 patients with metastases, with respective specificities of 85% (95% CI: 78%, 92%), 79% (95% CI: 70%, 88%), and 84% (95% CI: 76%, 92%). Analyzed according to hemipelvis, lymph node metastases were detected with histopathologic examination in 44 of 240 pelvic sides (18%); the three readers correctly detected these on DW MR images in 26 (59%; 95% CI: 45%, 73%), 19 (43%; 95% CI: 27%, 57%), and 28 (64%; 95% CI: 47%, 78%) of the 44 cases. CONCLUSION DW MR imaging enables noninvasive detection of small lymph node metastases in normal-sized nodes in a substantial percentage of patients with prostate and bladder cancer diagnosed as N0 with conventional cross-sectional imaging techniques.

Determinanten kognitiver Angstmanifestation während schulischer Tests: Eine Längsschnittstudie

Theoretischer Hintergrund und Fragestellung: Schulische Tests dienen der Feststellung von Wissen und Können. Wie jede Messung kann auch diese durch Störvariablen verzerrt werden. Während Tests erlebte Angst ist ein solcher potentieller Störeinfluss: Angst kann Testleistungen beinträchtigen, da sie sich hinderlich auf die Informationsverarbeitung auswirken kann (Störung des Wissensabrufs und des Denkens; Zeidner, 1998). Dieser kognitiven Angstmanifestation (Rost & Schermer, 1997) liegt die angstbedingte automatische Aufmerksamkeitsorientierung auf aufgaben-irrelevante Gedanken während der Testbearbeitung zugrunde (Eysenck, Derakshan, Santos & Calvo, 2007). Es hat sich allerdings gezeigt, dass Angst nicht grundsätzlich mit Testleistungseinbußen einhergeht (Eysenck et al., 2007). Wir gehen davon aus, dass die Kapazität zur Selbstkontrolle bzw. Aufmerksamkeitsregulation (Baumeister, Muraven & Tice, 2000; Schmeichel & Baumeister, 2010) ein Faktor ist, der bedingt, wie stark kognitive Angstmanifestation während Tests und damit zusammenhängende Leistungseinbußen auftreten. Ängstliche Lernende mit höherer Aufmerksamkeitsregulationskapazität sollten ihrer automatischen Aufmerksamkeitsorientierung auf aufgaben-irrelevante Gedanken erfolgreicher entgegensteuern und ihre Aufmerksamkeit weiterhin auf die Aufgabenbearbeitung richten können. Dem entsprechend sollten sie trotz Angst weniger kognitive Angstmanifestation während Tests erleben als ängstliche Lernende mit geringerer Aufmerksamkeitsregulationskapazität. Auch die Selbstwirksamkeitserwartung und das Selbstwertgefühl sind Variablen, die in der Vergangenheit mit der Bewältigung von Angst und Stress in Verbindung gebracht wurden (Bandura, 1977; Baumeister, Campbell, Krueger & Vohs, 2003). Daher wurden diese Variablen als weitere Prädiktoren berücksichtigt. Es wurde die Hypothese getestet, dass die dispositionelle Aufmerksamkeitsregulationskapazität über die dispositionelle Selbstwirksamkeitserwartung und das dispositionelle Selbstwertgefühl hinaus Veränderungen in der kognitiven Angstmanifestation während Mathematiktests in einer Wirtschaftsschülerstichprobe vorhersagt. Es wurde des Weiteren davon ausgegangen, dass eine indirekte Verbindung zwischen der Aufmerksamkeitsregulationskapazität und der Veränderung in den Mathematiknoten, vermittelt über die Veränderung in der kognitiven Angstmanifestation, besteht. Methode: Einhundertachtundfünfzig Wirtschaftsschüler bearbeiteten im September 2011 (T1) einen Fragebogen, der die folgenden Messungen enthielt:-Subskala Kognitive Angstmanifestation aus dem Differentiellen Leistungsangstinventar (Rost & Schermer, 1997) bezogen auf Mathematiktests (Sparfeldt, Schilling, Rost, Stelzl & Peipert, 2005); Alpha = .90; -Skala zur dispositionellen Aufmerksamkeitsregulationskapazität (Bertrams & Englert, 2013); Alpha = .88; -Skala zur Selbstwirksamkeitserwartung (Schwarzer & Jerusalem, 1995); Alpha = .83; -Skala zum Selbstwertgefühl (von Collani & Herzberg, 2003); Alpha = .83; -Angabe der letzten Mathematikzeugnisnote. Im Februar 2012 (T2), also nach 5 Monaten und kurz nach dem Erhalt des Halbjahreszeugnisses, gaben die Schüler erneut ihre kognitive Angstmanifestation während Mathematiktests (Alpha = .93) und ihre letzte Mathematikzeugnisnote an. Ergebnisse: Die Daten wurden mittels Korrelationsanalyse, multipler Regressionsanalyse und Bootstrapping ausgewertet. Die Aufmerksamkeitsregulationskapazität, die Selbstwirksamkeitserwartung und das Selbstwertgefühl (alle zu T1) waren positiv interkorreliert, r= .50/.59/.59. Diese Variablen wurden gemeinsam als Prädiktoren in ein Regressionsmodell zur Vorhersage der kognitiven Angstmanifestation zu T2 eingefügt. Gleichzeitig wurde die kognitive Angstmanifestation zu T1 konstant gehalten. Es zeigte sich, dass die Aufmerksamkeitsregulationskapazität erwartungskonform die Veränderungen in der kognitiven Angstmanifestation vorhersagte, Beta = -.21, p= .02. Das heißt, dass höhere Aufmerksamkeitsregulationskapazität zu T1 mit verringerter kognitiver Angstmanifestation zu T2 einherging. Die Selbstwirksamkeitserwartung, Beta = .12, p= .14, und das Selbstwertgefühl, Beta = .05, p= .54, hatten hingegen keinen eigenen Vorhersagewert für die Veränderungen in der kognitiven Angstmanifestation. Des Weiteren ergab eine Mediationsanalyse mittels Bootstrapping (bias-corrected bootstrap 95% confidence interval, 5000 resamples; siehe Hayes & Scharkow, in press), dass die Aufmerksamkeitsregulationskapazität (T1), vermittelt über die Veränderung in der kognitiven Angstmanifestation, indirekt mit der Veränderung in der Mathematikleistung verbunden war (d.h. das Bootstrap-Konfidenzintervall schloss nicht die Null ein; CI [0.01, 0.24]). Für die Selbstwirksamkeitserwartung und das Selbstwertgefühl fand sich keine analoge indirekte Verbindung zur Mathematikleistung. Fazit: Die Befunde verweisen auf die Bedeutsamkeit der Aufmerksamkeitsregulationskapazität für die Bewältigung kognitiver Angstreaktionen während schulischer Tests. Losgelöst von der Aufmerksamkeitsregulationskapazität scheinen positive Erwartungen und ein positives Selbstbild keine protektive Wirkung hinsichtlich der leistungsbeeinträchtigenden kognitiven Angstmanifestation während Mathematiktests zu besitzen.

Range-based Weighted-likelihood Particle Filter for RSS-based Indoor Tracking

Attractive business cases in various application fields contribute to the sustained long-term interest in indoor localization and tracking by the research community. Location tracking is generally treated as a dynamic state estimation problem, consisting of two steps: (i) location estimation through measurement, and (ii) location prediction. For the estimation step, one of the most efficient and low-cost solutions is Received Signal Strength (RSS)-based ranging. However, various challenges - unrealistic propagation model, non-line of sight (NLOS), and multipath propagation - are yet to be addressed. Particle filters are a popular choice for dealing with the inherent non-linearities in both location measurements and motion dynamics. While such filters have been successfully applied to accurate, time-based ranging measurements, dealing with the more error-prone RSS based ranging is still challenging. In this work, we address the above issues with a novel, weighted likelihood, bootstrap particle filter for tracking via RSS-based ranging. Our filter weights the individual likelihoods from different anchor nodes exponentially, according to the ranging estimation. We also employ an improved propagation model for more accurate RSS-based ranging, which we suggested in recent work. We implemented and tested our algorithm in a passive localization system with IEEE 802.15.4 signals, showing that our proposed solution largely outperforms a traditional bootstrap particle filter.

HIV-1 Transmission During Recent Infection and During Treatment Interruptions as Major Drivers of New Infections in the Swiss HIV Cohort Study.

BACKGROUND  Reducing the fraction of transmissions during recent human immunodeficiency virus (HIV) infection is essential for the population-level success of "treatment as prevention". METHODS  A phylogenetic tree was constructed with 19 604 Swiss sequences and 90 994 non-Swiss background sequences. Swiss transmission pairs were identified using 104 combinations of genetic distance (1%-2.5%) and bootstrap (50%-100%) thresholds, to examine the effect of those criteria. Monophyletic pairs were classified as recent or chronic transmission based on the time interval between estimated seroconversion dates. Logistic regression with adjustment for clinical and demographic characteristics was used to identify risk factors associated with transmission during recent or chronic infection. FINDINGS  Seroconversion dates were estimated for 4079 patients on the phylogeny, and comprised between 71 (distance, 1%; bootstrap, 100%) to 378 transmission pairs (distance, 2.5%; bootstrap, 50%). We found that 43.7% (range, 41%-56%) of the transmissions occurred during the first year of infection. Stricter phylogenetic definition of transmission pairs was associated with higher recent-phase transmission fraction. Chronic-phase viral load area under the curve (adjusted odds ratio, 3; 95% confidence interval, 1.64-5.48) and time to antiretroviral therapy (ART) start (adjusted odds ratio 1.4/y; 1.11-1.77) were associated with chronic-phase transmission as opposed to recent transmission. Importantly, at least 14% of the chronic-phase transmission events occurred after the transmitter had interrupted ART. CONCLUSIONS  We demonstrate a high fraction of transmission during recent HIV infection but also chronic transmissions after interruption of ART in Switzerland. Both represent key issues for treatment as prevention and underline the importance of early diagnosis and of early and continuous treatment.

Minimum count sums for charcoalconcentration estimates in pollen slides: accuracy and potential errors

Charcoal particles in pollen slides are often abundant, and thus analysts are faced with the problem of setting the minimum counting sum as small as possible in order to save time. We analysed the reliability of charcoal-concentration estimates based on different counting sums, using simulated low-to high-count samples. Bootstrap simulations indicate that the variability of inferred charcoal concentrations increases progressively with decreasing sums. Below 200 items (i.e., the sum of charcoal particles and exotic marker grains), reconstructed fire incidence is either too high or too low. Statistical comparisons show that the means of bootstrap simulations stabilize after 200 counts. Moreover, a count of 200-300 items is sufficient to produce a charcoal-concentration estimate with less than+5% error if compared with high-count samples of 1000 items for charcoal/marker grain ratios 0.1-0.91. If, however, this ratio is extremely high or low (> 0.91 or < 0.1) and if such samples are frequent, we suggest that marker grains are reduced or added prior to new sample processing.

Passively Track WiFi Users with an Enhanced Particle Filter using Power-based Ranging

Passive positioning systems produce user location information for third-party providers of positioning services. Since the tracked wireless devices do not participate in the positioning process, passive positioning can only rely on simple, measurable radio signal parameters, such as timing or power information. In this work, we provide a passive tracking system for WiFi signals with an enhanced particle filter using fine-grained power-based ranging. Our proposed particle filter provides an improved likelihood function on observation parameters and is equipped with a modified coordinated turn model to address the challenges in a passive positioning system. The anchor nodes for WiFi signal sniffing and target positioning use software defined radio techniques to extract channel state information to mitigate multipath effects. By combining the enhanced particle filter and a set of enhanced ranging methods, our system can track mobile targets with an accuracy of 1.5m for 50% and 2.3m for 90% in a complex indoor environment. Our proposed particle filter significantly outperforms the typical bootstrap particle filter, extended Kalman filter and trilateration algorithms.

Prediction of improvement in skin fibrosis in diffuse cutaneous systemic sclerosis: a EUSTAR analysis

OBJECTIVES Improvement of skin fibrosis is part of the natural course of diffuse cutaneous systemic sclerosis (dcSSc). Recognising those patients most likely to improve could help tailoring clinical management and cohort enrichment for clinical trials. In this study, we aimed to identify predictors for improvement of skin fibrosis in patients with dcSSc. METHODS We performed a longitudinal analysis of the European Scleroderma Trials And Research (EUSTAR) registry including patients with dcSSc, fulfilling American College of Rheumatology criteria, baseline modified Rodnan skin score (mRSS) ≥7 and follow-up mRSS at 12±2 months. The primary outcome was skin improvement (decrease in mRSS of >5 points and ≥25%) at 1 year follow-up. A respective increase in mRSS was considered progression. Candidate predictors for skin improvement were selected by expert opinion and logistic regression with bootstrap validation was applied. RESULTS From the 919 patients included, 218 (24%) improved and 95 (10%) progressed. Eleven candidate predictors for skin improvement were analysed. The final model identified high baseline mRSS and absence of tendon friction rubs as independent predictors of skin improvement. The baseline mRSS was the strongest predictor of skin improvement, independent of disease duration. An upper threshold between 18 and 25 performed best in enriching for progressors over regressors. CONCLUSIONS Patients with advanced skin fibrosis at baseline and absence of tendon friction rubs are more likely to regress in the next year than patients with milder skin fibrosis. These evidence-based data can be implemented in clinical trial design to minimise the inclusion of patients who would regress under standard of care.

CHEOPS Performance for Exomoons: The Detectability of Exomoons by Using Optimal Decision Algorithm

Many attempts have already been made to detect exomoons around transiting exoplanets, but the first confirmed discovery is still pending. The experiences that have been gathered so far allow us to better optimize future space telescopes for this challenge already during the development phase. In this paper we focus on the forthcoming CHaraterising ExOPlanet Satellite (CHEOPS), describing an optimized decision algorithm with step-by-step evaluation, and calculating the number of required transits for an exomoon detection for various planet moon configurations that can be observable by CHEOPS. We explore the most efficient way for such an observation to minimize the cost in observing time. Our study is based on PTV observations (photocentric transit timing variation) in simulated CHEOPS data, but the recipe does not depend on the actual detection method, and it can be substituted with, e.g., the photodynamical method for later applications. Using the current state-of-the-art level simulation of CHEOPS data we analyzed transit observation sets for different star planet moon configurations and performed a bootstrap analysis to determine their detection statistics. We have found that the detection limit is around an Earth-sized moon. In the case of favorable spatial configurations, systems with at least a large moon and a Neptune-sized planet, an 80% detection chance requires at least 5-6 transit observations on average. There is also a nonzero chance in the case of smaller moons, but the detection statistics deteriorate rapidly, while the necessary transit measurements increase quickly. After the CoRoT and Kepler spacecrafts, CHEOPS will be the next dedicated space telescope that will observe exoplanetary transits and characterize systems with known Doppler-planets. Although it has a smaller aperture than Kepler (the ratio of the mirror diameters is about 1/3) and is mounted with a CCD that is similar to Kepler's, it will observe brighter stars and operate with larger sampling rate; therefore, the detection limit for an exomoon can be the same as or better, which will make CHEOPS a competitive instruments in the quest for exomoons.

Standard Errors for the Blinder-Oaxaca Decomposition

The decomposition technique introduced by Blinder (1973) and Oaxaca (1973) is widely used to study outcome differences between groups. For example, the technique is commonly applied to the analysis of the gender wage gap. However, despite the procedure's frequent use, very little attention has been paid to the issue of estimating the sampling variances of the decomposition components. We therefore suggest an approach that introduces consistent variance estimators for several variants of the decomposition. The accuracy of the new estimators under ideal conditions is illustrated with the results of a Monte Carlo simulation. As a second check, the estimators are compared to bootstrap results obtained using real data. In contrast to previously proposed statistics, the new method takes into account the extra variation imposed by stochastic regressors.

MOREMATA: Stata module (Mata) to provide various functions

This package includes various Mata functions. kern(): various kernel functions; kint(): kernel integral functions; kdel0(): canonical bandwidth of kernel; quantile(): quantile function; median(): median; iqrange(): inter-quartile range; ecdf(): cumulative distribution function; relrank(): grade transformation; ranks(): ranks/cumulative frequencies; freq(): compute frequency counts; histogram(): produce histogram data; mgof(): multinomial goodness-of-fit tests; collapse(): summary statistics by subgroups; _collapse(): summary statistics by subgroups; gini(): Gini coefficient; sample(): draw random sample; srswr(): SRS with replacement; srswor(): SRS without replacement; upswr(): UPS with replacement; upswor(): UPS without replacement; bs(): bootstrap estimation; bs2(): bootstrap estimation; bs_report(): report bootstrap results; jk(): jackknife estimation; jk_report(): report jackknife results; subset(): obtain subsets, one at a time; composition(): obtain compositions, one by one; ncompositions(): determine number of compositions; partition(): obtain partitions, one at a time; npartitionss(): determine number of partitions; rsubset(): draw random subset; rcomposition(): draw random composition; colvar(): variance, by column; meancolvar(): mean and variance, by column; variance0(): population variance; meanvariance0(): mean and population variance; mse(): mean squared error; colmse(): mean squared error, by column; sse(): sum of squared errors; colsse(): sum of squared errors, by column; benford(): Benford distribution; cauchy(): cumulative Cauchy-Lorentz dist.; cauchyden(): Cauchy-Lorentz density; cauchytail(): reverse cumulative Cauchy-Lorentz; invcauchy(): inverse cumulative Cauchy-Lorentz; rbinomial(): generate binomial random numbers; cebinomial(): cond. expect. of binomial r.v.; root(): Brent's univariate zero finder; nrroot(): Newton-Raphson zero finder; finvert(): univariate function inverter; integrate_sr(): univariate function integration (Simpson's rule); integrate_38(): univariate function integration (Simpson's 3/8 rule); ipolate(): linear interpolation; polint(): polynomial inter-/extrapolation; plot(): Draw twoway plot; _plot(): Draw twoway plot; panels(): identify nested panel structure; _panels(): identify panel sizes; npanels(): identify number of panels; nunique(): count number of distinct values; nuniqrows(): count number of unique rows; isconstant(): whether matrix is constant; nobs(): number of observations; colrunsum(): running sum of each column; linbin(): linear binning; fastlinbin(): fast linear binning; exactbin(): exact binning; makegrid(): equally spaced grid points; cut(): categorize data vector; posof(): find element in vector; which(): positions of nonzero elements; locate(): search an ordered vector; hunt(): consecutive search; cond(): matrix conditional operator; expand(): duplicate single rows/columns; _expand(): duplicate rows/columns in place; repeat(): duplicate contents as a whole; _repeat(): duplicate contents in place; unorder2(): stable version of unorder(); jumble2(): stable version of jumble(); _jumble2(): stable version of _jumble(); pieces(): break string into pieces; npieces(): count number of pieces; _npieces(): count number of pieces; invtokens(): reverse of tokens(); realofstr(): convert string into real; strexpand(): expand string argument; matlist(): display a (real) matrix; insheet(): read spreadsheet file; infile(): read free-format file; outsheet(): write spreadsheet file; callf(): pass optional args to function; callf_setup(): setup for mm_callf().

SPECTRAL DOMAIN-OPTICAL COHERENCE TOMOGRAPHY IMAGE CONTRAST AND BACKGROUND COLOR SETTINGS INFLUENCE IDENTIFICATION OF RETINAL STRUCTURES.

PURPOSE To evaluate image contrast and color setting on assessment of retinal structures and morphology in spectral-domain optical coherence tomography. METHODS Two hundred and forty-eight Spectralis spectral-domain optical coherence tomography B-scans of 62 patients were analyzed by 4 readers. B-scans were extracted in 4 settings: W + N = white background with black image at normal contrast 9; W + H = white background with black image at maximum contrast 16; B + N = black background with white image at normal contrast 12; B + H = black background with white image at maximum contrast 16. Readers analyzed the images to identify morphologic features. Interreader correlation was calculated. Differences between Fleiss-kappa correlation coefficients were examined using bootstrap method. Any setting with significantly higher correlation coefficient was deemed superior for evaluating specific features. RESULTS Correlation coefficients differed among settings. No single setting was superior for all respective spectral-domain optical coherence tomography parameters (P = 0.3773). Some variables showed no differences among settings. Hard exudates and subretinal fluid were best seen with B + H (κ = 0.46, P = 0.0237 and κ = 0.78, P = 0.002). Microaneurysms were best seen with W + N (κ = 0.56, P = 0.025). Vitreomacular interface, enhanced transmission signal, and epiretinal membrane were best identified using all color/contrast settings together (κ = 0.44, P = 0.042, κ = 0.57, P = 0.01, and κ = 0.62, P ≤ 0.0001). CONCLUSION Contrast and background affect the evaluation of retinal structures on spectral-domain optical coherence tomography images. No single setting was superior for all features, though certain changes were best seen with specific settings.