Ecophysiological analysis reveals distinct environmental preferences in closely related Baltic Sea picocyanobacteria.

Cluster 5 picocyanobacteria significantly contribute to primary productivity in aquatic ecosystems. Estuarine populations are highly diverse and consist of many co-occurring strains, but their physiology remains largely understudied. In this study, we characterized 17 novel estuarine picocyanobacterial strains. Phylogenetic analysis of the 16S rRNA and pigment genes (cpcB and cpeBA) uncovered multiple estuarine and freshwater-related clusters and pigment types. Assays with five representative strains (three phycocyanin rich and two phycoerythrin rich) under temperature (10-30°C), light (10-190 µmol photons m-2 s-1 ), and salinity (2-14 PSU) gradients revealed distinct growth optima and tolerance, indicating that genetic variability was accompanied by physiological diversity. Adaptability to environmental conditions was associated with differential pigment content and photosynthetic performance. Amplicon sequence variants at a coastal and an offshore station linked population dynamics with phylogenetic clusters, supporting that strains isolated in this study represent key ecotypes within the Baltic Sea picocyanobacterial community. The functional diversity found within strains with the same pigment type suggests that understanding estuarine picocyanobacterial ecology requires analysis beyond the phycocyanin and phycoerythrin divide. This new knowledge of the environmental preferences in estuarine picocyanobacteria is important for understanding and evaluating productivity in current and future ecosystems.

Time-to-event surrogate endpoint validation using mediation analysis and meta-analytic data.

With the ongoing development of treatments and the resulting increase in survival in oncology, clinical trials based on endpoints such as overall survival may require long follow-up periods to observe sufficient events and ensure adequate statistical power. This increase in follow-up time may compromise the feasibility of the study. The use of surrogate endpoints instead of final endpoints may be attractive for these studies. However, before a surrogate can be used in a clinical trial, it must be statistically validated. In this article, we propose an approach to validate surrogates when both the surrogate and final endpoints are censored event times. This approach is developed for meta-analytic data and uses a mediation analysis to decompose the total effect of the treatment on the final endpoint as a direct effect and an indirect effect through the surrogate. The meta-analytic nature of the data is accounted for in a joint model with random effects at the trial level. The proportion of the indirect effect over the total effect of the treatment on the final endpoint can be computed from the parameters of the model and used as a measure of surrogacy. We applied this method to investigate time-to-relapse as a surrogate endpoint for overall survival in resectable gastric cancer.

On the use of extreme value tail modeling for generalized pairwise comparisons with censored outcomes.

In randomized clinical trials, methods of pairwise comparisons such as the 'Net Benefit' or the 'win ratio' have recently gained much attention when interests lies in assessing the effect of a treatment as compared to a standard of care. Among other advantages, these methods are usually praised for delivering a treatment measure that can easily handle multiple outcomes of different nature, while keeping a meaningful interpretation for patients and clinicians. For time-to-event outcomes, a recent suggestion emerged in the literature for estimating these treatment measures by providing a natural handling of censored outcomes. However, this estimation procedure may lead to biased estimates when tails of survival functions cannot be reliably estimated using Kaplan-Meier estimators. The problem then extrapolates to the other outcomes incorporated in the pairwise comparison construction. In this work, we suggest to extend the procedure by the consideration of a hybrid survival function estimator that relies on an extreme value tail model through the Generalized Pareto distribution. We provide an estimator of treatment effect measures that notably improves on bias and remains easily apprehended for practical implementation. This is illustrated in an extensive simulation study as well as in an actual trial of a new cancer immunotherapy.

Using an interim analysis based exclusively on an early outcome in a randomized clinical trial with a long-term clinical endpoint.

In RCTs with an interest in a long-term efficacy endpoint, the follow-up time necessary to observe the endpoint may be substantial. In order to reduce the expected duration of such trials, early-outcome data may be collected to enrich an interim analysis aimed at stopping the trial early for efficacy. We propose to extend such a design with an additional interim analysis using solely early-outcome data in order to expedite the evaluation of treatment's efficacy. We evaluate the potential gain in operating characteristics (power, expected trial duration, and expected sample size) when introducing such an early interim analysis, in function of the properties of the early outcome as a surrogate for the long-term endpoint. In the context of a longitudinal age-related macular degeneration (ARMD) ophthalmology trial, results show potentially substantial gains in both the expected trial duration and the expected sample size. A prerequisite, though, is that the treatment effect on the early outcome has to be strongly correlated with the treatment effect on the long-term endpoint, that is, that the early outcome is a validated surrogate for the long-term endpoint.

A new measure of treatment effect in clinical trials involving competing risks based on generalized pairwise comparisons.

In survival analysis with competing risks, the treatment effect is typically expressed using cause-specific or subdistribution hazard ratios, both relying on proportional hazards assumptions. This paper proposes a nonparametric approach to analyze competing risks data based on generalized pairwise comparisons (GPC). GPC estimate the net benefit, defined as the probability that a patient from the treatment group has a better outcome than a patient from the control group minus the probability of the opposite situation, by comparing all pairs of patients taking one patient from each group. GPC allow using clinically relevant thresholds and simultaneously analyzing multiple prioritized endpoints. We show that under proportional subdistribution hazards, the net benefit for competing risks settings can be expressed as a decreasing function of the subdistribution hazard ratio, taking a value 0 when the latter equals 1. We propose four net benefit estimators dealing differently with censoring. Among them, the Péron estimator uses the Aalen-Johansen estimator of the cumulative incidence functions to classify the pairs for which the patient with the best outcome could not be determined due to censoring. We use simulations to study the bias of these estimators and the size and power of the tests based on the net benefit. The Péron estimator was approximately unbiased when the sample size was large and the censoring distribution's support sufficiently wide. With one endpoint, our approach showed a comparable power to a proportional subdistribution hazards model even under proportional subdistribution hazards. An application of the methodology in oncology is provided.

Flexible parametric approach to classical measurement error variance estimation without auxiliary data.

Measurement error in the continuous covariates of a model generally yields bias in the estimators. It is a frequent problem in practice, and many correction procedures have been developed for different classes of models. However, in most cases, some information about the measurement error distribution is required. When neither validation nor auxiliary data (e.g., replicated measurements) are available, this specification turns out to be tricky. In this article, we develop a flexible likelihood-based procedure to estimate the variance of classical additive error of Gaussian distribution, without additional information, when the covariate has compact support. The performance of this estimator is investigated both in an asymptotic way and through finite sample simulations. The usefulness of the obtained estimator when using the simulation extrapolation (SIMEX) algorithm, a widely used correction method, is then analyzed in the Cox proportional hazards model through other simulations. Finally, the whole procedure is illustrated on real data.

The single-index/Cox mixture cure model.

In survival analysis, it often happens that a certain fraction of the subjects under study never experience the event of interest, that is, they are considered "cured." In the presence of covariates, a common model for this type of data is the mixture cure model, which assumes that the population consists of two subpopulations, namely the cured and the non-cured ones, and it writes the survival function of the whole population given a set of covariates as a mixture of the survival function of the cured subjects (which equals one), and the survival function of the non-cured ones. In the literature, one usually assumes that the mixing probabilities follow a logistic model. This is, however, a strong modeling assumption, which might not be met in practice. Therefore, in order to have a flexible model which at the same time does not suffer from curse-of-dimensionality problems, we propose in this paper a single-index model for the mixing probabilities. For the survival function of the non-cured subjects we assume a Cox proportional hazards model. We estimate this model using a maximum likelihood approach. We also carry out a simulation study, in which we compare the estimators under the single-index model and under the logistic model for various model settings, and we apply the new model and estimation method on a breast cancer data set.

Spring and Late Summer Phytoplankton Biomass Impact on the Coastal Sediment Microbial Community Structure.

Two annual Baltic Sea phytoplankton blooms occur in spring and summer. The bloom intensity is determined by nutrient concentrations in the water, while the period depends on weather conditions. During the course of the bloom, dead cells sink to the sediment where their degradation consumes oxygen to create hypoxic zones (< 2 mg/L dissolved oxygen). These zones prevent the establishment of benthic communities and may result in fish mortality. The aim of the study was to determine how the spring and autumn sediment chemistry and microbial community composition changed due to degradation of diatom or cyanobacterial biomass, respectively. Results from incubation of sediment cores showed some typical anaerobic microbial processes after biomass addition such as a decrease in NO2- + NO3- in the sediment surface (0-1 cm) and iron in the underlying layer (1-2 cm). In addition, an increase in NO2- + NO3- was observed in the overlying benthic water in all amended and control incubations. The combination of NO2- + NO3- diffusion plus nitrification could not account for this increase. Based on 16S rRNA gene sequences, the addition of cyanobacterial biomass during autumn caused a large increase in ferrous iron-oxidizing archaea while diatom biomass amendment during spring caused minor changes in the microbial community. Considering that OTUs sharing lineages with acidophilic microorganisms had a high relative abundance during autumn, it was suggested that specific niches developed in sediment microenvironments. These findings highlight the importance of nitrogen cycling and early microbial community changes in the sediment due to sinking phytoplankton before potential hypoxia occurs.

Incidence and risk factors for adverse events during monitored anaesthesia care for gastrointestinal endoscopy in children: A prospective observational study.

BACKGROUND: Better understanding of risk factors for adverse events during monitored anaesthesia care (MAC) for paediatric gastrointestinal endoscopy may improve outcome in children. OBJECTIVES: To identify the prevalence and predictors of adverse events during MAC for paediatric endoscopy. DESIGN: An observational study. SETTING: Tertiary university hospital, single-centre cohort, from January 2010 to August 2016. PATIENTS: The prospectively collected electronic anaesthetic records of 3435 children aged up to 16 years who underwent diagnostic gastrointestinal endoscopy under MAC were analysed retrospectively. Children with an American Society of Anesthesiologists' physical status at least 4, and those requiring mechanical ventilation and therapeutic or urgent endoscopy were excluded. MAIN OUTCOME MEASURES: The prevalence and predictors of adverse events during MAC for paediatric gastrointestinal endoscopy, with particular reference to the use of different anaesthetic or sedative agents. RESULTS: Mean ±â€ŠSD age of the children was 8.5 ±â€Š4.4 years. The incidences of adverse events and adverse respiratory events were 3.4 and 3.3%, respectively. Multivariate analysis identified 12 independent predictors: age [odds ratio (OR) 0.92, P = 0.002], children's size for example underweight (OR 1.78, P = 0.039), overweight (OR 2.20, P = 0.039), (morbid) obesity (OR 4.25, P = 0.006), presence of respiratory comorbidities (OR 8.18, P < 0.001), recent respiratory infection (OR 23.55, P < 0.001) or both (OR 17.46, P < 0.001), neurological comorbidities (OR 2.18, P = 0.007), upper gastrointestinal endoscopy (OR 5.66, P < 0.001), propofol co-administration with ketamine (OR 10.34, P < 0. 001) or after sevoflurane induction (OR 44.95, P < 0.001), and propofol induction dose (OR 18.97, P < 0.001). Posthoc secondary analyses revealed a significantly higher risk of adverse events (OR 3.9, P < 0.0001) and also significantly more respiratory comorbidities and respiratory infections (P < 0.0001) in children aged less than 2 years when compared with children aged at least 2 years. No cardiovascular events were observed and outcome was uneventful. CONCLUSION: The present cohort demonstrated the feasibility and safety of MAC for paediatric gastrointestinal endoscopy by an experienced team. Although adverse events occurred rarely, their predictive factors were clinically identifiable. Applying this information in risk assessment and modifying anaesthetic management accordingly could improve outcome. TRIAL REGISTRATION: ISRCTN70362666.

A note on tests for relevant differences with extremely large sample sizes.

A well-known problem in classical two-tailed hypothesis testing is that P-values go to zero when the sample size goes to infinity, irrespectively of the effect size. This pitfall can make the testing of data consisting of large sample sizes potentially unreliable. In this note, we propose to test for relevant differences to overcome this issue. We illustrate the proposed test a on real data set of about 40 million privately insured patients.

Vertical modeling: analysis of competing risks data with a cure fraction.

In this paper, we extend the vertical modeling approach for the analysis of survival data with competing risks to incorporate a cure fraction in the population, that is, a proportion of the population for which none of the competing events can occur. The proposed method has three components: the proportion of cure, the risk of failure, irrespective of the cause, and the relative risk of a certain cause of failure, given a failure occurred. Covariates may affect each of these components. An appealing aspect of the method is that it is a natural extension to competing risks of the semi-parametric mixture cure model in ordinary survival analysis; thus, causes of failure are assigned only if a failure occurs. This contrasts with the existing mixture cure model for competing risks of Larson and Dinse, which conditions at the onset on the future status presumably attained. Regression parameter estimates are obtained using an EM-algorithm. The performance of the estimators is evaluated in a simulation study. The method is illustrated using a melanoma cancer data set.

Metapopulation theory identifies biogeographical patterns among core and satellite marine bacteria scaling from tens to thousands of kilometers.

Metapopulation theory developed in terrestrial ecology provides applicable frameworks for interpreting the role of local and regional processes in shaping species distribution patterns. Yet, empirical testing of metapopulation models on microbial communities is essentially lacking. We determined regional bacterioplankton dynamics from monthly transect sampling in the Baltic Sea Proper using 16S rRNA gene sequencing. A strong positive trend was found between local relative abundance and occupancy of populations. Notably, the occupancy-frequency distributions were significantly bimodal with a satellite mode of rare endemic populations and a core mode of abundant cosmopolitan populations (e.g. Synechococcus, SAR11 and SAR86 clade members). Temporal changes in population distributions supported several theoretical frameworks. Still, bimodality was found among bacterioplankton communities across the entire Baltic Sea, and was also frequent in globally distributed datasets. Datasets spanning waters with widely different physicochemical characteristics or environmental gradients typically lacked significant bimodal patterns. When such datasets were divided into subsets with coherent environmental conditions, bimodal patterns emerged, highlighting the importance of positive feedbacks between local abundance and occupancy within specific biomes. Thus, metapopulation theory applied to microbial biogeography can provide novel insights into the mechanisms governing shifts in biodiversity resulting from natural or anthropogenically induced changes in the environment.

Incorporation of nested frailties into semiparametric multi-state models.

Proportional hazards models are among the most popular regression models in survival analysis. Multi-state models generalize them by jointly considering different types of events and their interrelations, whereas frailty models incorporate random effects to account for unobserved risk factors, possibly shared by clusters of subjects. The integration of multi-state and frailty methodology is an interesting way to control for unobserved heterogeneity in the presence of complex event history structures and is particularly appealing for multicenter clinical trials. We propose the incorporation of correlated frailties in the transition-specific hazard function, thanks to a nested hierarchy. We studied a semiparametric estimation approach based on maximum integrated partial likelihood. We show in a simulation study that the nested frailty multi-state model improves the estimation of the effect of covariates, as well as the coverage probability of their confidence intervals. We present a case study concerning a prostate cancer multicenter clinical trial. The multi-state nature of the model allows us to evidence the effect of treatment on death taking into account intermediate events.

Variable selection in a flexible parametric mixture cure model with interval-censored data.

In standard survival analysis, it is generally assumed that every individual will experience someday the event of interest. However, this is not always the case, as some individuals may not be susceptible to this event. Also, in medical studies, it is frequent that patients come to scheduled interviews and that the time to the event is only known to occur between two visits. That is, the data are interval-censored with a cure fraction. Variable selection in such a setting is of outstanding interest. Covariates impacting the survival are not necessarily the same as those impacting the probability to experience the event. The objective of this paper is to develop a parametric but flexible statistical model to analyze data that are interval-censored and include a fraction of cured individuals when the number of potential covariates may be large. We use the parametric mixture cure model with an accelerated failure time regression model for the survival, along with the extended generalized gamma for the error term. To overcome the issue of non-stable and non-continuous variable selection procedures, we extend the adaptive LASSO to our model. By means of simulation studies, we show good performance of our method and discuss the behavior of estimates with varying cure and censoring proportion. Lastly, our proposed method is illustrated with a real dataset studying the time until conversion to mild cognitive impairment, a possible precursor of Alzheimer's disease.

Statistical monitoring of data quality and consistency in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial.

INTRODUCTION: Data quality may impact the outcome of clinical trials; hence, there is a need to implement quality control strategies for the data collected. Traditional approaches to quality control have primarily used source data verification during on-site monitoring visits, but these approaches are hugely expensive as well as ineffective. There is growing interest in central statistical monitoring (CSM) as an effective way to ensure data quality and consistency in multicenter clinical trials. METHODS: CSM with SMART™ uses advanced statistical tools that help identify centers with atypical data patterns which might be the sign of an underlying quality issue. This approach was used to assess the quality and consistency of the data collected in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial, involving 1495 patients across 232 centers in Japan. RESULTS: In the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial, very few atypical data patterns were found among the participating centers, and none of these patterns were deemed to be related to a quality issue that could significantly affect the outcome of the trial. DISCUSSION: CSM can be used to provide a check of the quality of the data from completed multicenter clinical trials before analysis, publication, and submission of the results to regulatory agencies. It can also form the basis of a risk-based monitoring strategy in ongoing multicenter trials. CSM aims at improving data quality in clinical trials while also reducing monitoring costs.

Chemical and Genetic Diversity of Nodularia spumigena from the Baltic Sea.

Nodularia spumigena is a toxic, filamentous cyanobacterium occurring in brackish waters worldwide, yet forms extensive recurrent blooms in the Baltic Sea. N. spumigena produces several classes of non-ribosomal peptides (NRPs) that are active against several key metabolic enzymes. Previously, strains from geographically distant regions showed distinct NRP metabolic profiles. In this work, conspecific diversity in N. spumigena was studied using chemical and genetic approaches. NRP profiles were determined in 25 N. spumigena strains isolated in different years and from different locations in the Baltic Sea using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Genetic diversity was assessed by targeting the phycocyanin intergenic spacer and flanking regions (cpcBA-IGS). Overall, 14 spumigins, 5 aeruginosins, 2 pseudaeruginosins, 2 nodularins, 36 anabaenopeptins, and one new cyanopeptolin-like peptide were identified among the strains. Seven anabaenopeptins were new structures; one cyanopeptolin-like peptide was discovered in N. spumigena for the first time. Based on NRP profiles and cpcBA-IGS sequences, the strains were grouped into two main clusters without apparent influence of year and location, indicating persistent presence of these two subpopulations in the Baltic Sea. This study is a major step in using chemical profiling to explore conspecific diversity with a higher resolution than with a sole genetic approach.

[Hospital and general therapeutic patient education practices: example of diabetes and obesity in Belgium]. / Pratiques hospitalières et de médecine générale d'éducation thérapeutique : l'exemple du diabète et de l'obésité en Belgique.

Introduction/Objectives: Therapeutic Patient Education (TPE) is now part of the new medical landscape, including for the prevention and treatment of diabetes and obesity. While some countries, such as France, have decided to give priority to TPE in public health policy by means of legal recognition, no such framework has been developed in Belgium. The purpose of this article is to describe the actual TPE practices of health professionals (HP) in this environment.Methods: 82 face-to-face interviews (20 GPs and 62 hospital healthcare professionals) were conducted according to a semi-structured interview guide developed from the French Health Authority guidelines. Qualitative content analysis was performed on the data collected.Results/Discussion: For the majority of respondents, TPE is limited to brief information about the disease, its risk factors and complications. This representation of TPE determines the practical modalities of the educational activity right from the educational diagnosis. The possibilities for developing the patient's capacities are limited. Practices reflect a lack of pedagogical structuring and do not correspond to a real multidisciplinary approach.Conclusion: Healthcare professionals must develop a clear vision of the implications of the concept of TPE and must therefore receive adequate training to ensure good quality development and implementation.

Disentangling seasonal bacterioplankton population dynamics by high-frequency sampling.

Multiyear comparisons of bacterioplankton succession reveal that environmental conditions drive community shifts with repeatable patterns between years. However, corresponding insight into bacterioplankton dynamics at a temporal resolution relevant for detailed examination of variation and characteristics of specific populations within years is essentially lacking. During 1 year, we collected 46 samples in the Baltic Sea for assessing bacterial community composition by 16S rRNA gene pyrosequencing (nearly twice weekly during productive season). Beta-diversity analysis showed distinct clustering of samples, attributable to seemingly synchronous temporal transitions among populations (populations defined by 97% 16S rRNA gene sequence identity). A wide spectrum of bacterioplankton dynamics was evident, where divergent temporal patterns resulted both from pronounced differences in relative abundance and presence/absence of populations. Rates of change in relative abundance calculated for individual populations ranged from 0.23 to 1.79 day(-1) . Populations that were persistently dominant, transiently abundant or generally rare were found in several major bacterial groups, implying evolution has favoured a similar variety of life strategies within these groups. These findings suggest that high temporal resolution sampling allows constraining the timescales and frequencies at which distinct populations transition between being abundant or rare, thus potentially providing clues about physical, chemical or biological forcing on bacterioplankton community structure.

Influenza vaccine efficacy trials: a simulation approach to understand failures from the past.

The success of a seasonal influenza vaccine efficacy trial depends not only upon the design but also upon the annual epidemic characteristics. In this context, simulation methods are an essential tool in evaluating the performances of study designs under various circumstances. However, traditional methods for simulating time-to-event data are not suitable for the simulation of influenza vaccine efficacy trials because of the seasonality and heterogeneity of influenza epidemics. Instead, we propose a mathematical model parameterized with historical surveillance data, heterogeneous frailty among the subjects, survey-based heterogeneous number of daily contact, and a mixed vaccine protection mechanism. We illustrate our methodology by generating multiple-trial data similar to a large phase III trial that failed to show additional relative vaccine efficacy of an experimental adjuvanted vaccine compared with the reference vaccine. We show that small departures from the designing assumptions, such as a smaller range of strain protection for the experimental vaccine or the chosen endpoint, could lead to smaller probabilities of success in showing significant relative vaccine efficacy.

Combined effects of nitrogen concentration and seasonal changes on the production of lipids in Nannochloropsis oculata.

Instead of sole nutrient starvation to boost algal lipid production, we addressed nutrient limitation at two different seasons (autumn and spring) during outdoor cultivation in flat panel photobioreactors. Lipid accumulation, biomass and lipid productivity and changes in fatty acid composition of Nannochloropsis oculata were investigated under nitrogen (N) limitation (nitrate:phosphate N:P 5, N:P 2.5 molar ratio). N. oculata was able to maintain a high biomass productivity under N-limitation compared to N-sufficiency (N:P 20) at both seasons, which in spring resulted in nearly double lipid productivity under N-limited conditions (0.21 g L⁻¹ day⁻¹) compared to N-sufficiency (0.11 g L⁻¹ day⁻¹). Saturated and monounsaturated fatty acids increased from 76% to nearly 90% of total fatty acids in N-limited cultures. Higher biomass and lipid productivity in spring could, partly, be explained by higher irradiance, partly by greater harvesting rate (~30%). Our results indicate the potential for the production of algal high value products (i.e., polyunsaturated fatty acids) during both N-sufficiency and N-limitation. To meet the sustainability challenges of algal biomass production, we propose a dual-system process: Closed photobioreactors producing biomass for high value products and inoculum for larger raceway ponds recycling waste/exhaust streams to produce bulk chemicals for fuel, feed and industrial material.