Starrfuller7473

Stepped wedge cluster randomized trials (SW-CRTs) with binary outcomes are increasingly used in prevention and implementation studies. Marginal models represent a flexible tool for analyzing SW-CRTs with population-averaged interpretations, but the joint estimation of the mean and intraclass correlation coefficients (ICCs) can be computationally intensive due to large cluster-period sizes. Motivated by the need for marginal inference in SW-CRTs, we propose a simple and efficient estimating equations approach to analyze cluster-period means. We show that the quasi-score for the marginal mean defined from individual-level observations can be reformulated as the quasi-score for the same marginal mean defined from the cluster-period means. An additional mapping of the individual-level ICCs into correlations for the cluster-period means further provides a rigorous justification for the cluster-period approach. The proposed approach addresses a long-recognized computational burden associated with estimating equations defined based on individual-level observations, and enables fast point and interval estimation of the intervention effect and correlations. We further propose matrix-adjusted estimating equations to improve the finite-sample inference for ICCs. By providing a valid approach to estimate ICCs within the class of generalized linear models for correlated binary outcomes, this article operationalizes key recommendations from the CONSORT extension to SW-CRTs, including the reporting of ICCs.Functional magnetic resonance imaging (fMRI) data have become increasingly available and are useful for describing functional connectivity (FC), the relatedness of neuronal activity in regions of the brain. This FC of the brain provides insight into certain neurodegenerative diseases and psychiatric disorders, and thus is of clinical importance. To help inform physicians regarding patient diagnoses, unsupervised clustering of subjects based on FC is desired, allowing the data to inform us of groupings of patients based on shared features of connectivity. Since heterogeneity in FC is present even between patients within the same group, it is important to allow subject-level differences in connectivity, while still pooling information across patients within each group to describe group-level FC. To this end, we propose a random covariance clustering model (RCCM) to concurrently cluster subjects based on their FC networks, estimate the unique FC networks of each subject, and to infer shared network features. click here Although current methods exist for estimating FC or clustering subjects using fMRI data, our novel contribution is to cluster or group subjects based on similar FC of the brain while simultaneously providing group- and subject-level FC network estimates. The competitive performance of RCCM relative to other methods is demonstrated through simulations in various settings, achieving both improved clustering of subjects and estimation of FC networks. Utility of the proposed method is demonstrated with application to a resting-state fMRI data set collected on 43 healthy controls and 61 participants diagnosed with schizophrenia.In studies of maternal exposure to air pollution, a children's health outcome is regressed on exposures observed during pregnancy. The distributed lag nonlinear model (DLNM) is a statistical method commonly implemented to estimate an exposure-time-response function when it is postulated the exposure effect is nonlinear. Previous implementations of the DLNM estimate an exposure-time-response surface parameterized with a bivariate basis expansion. However, basis functions such as splines assume smoothness across the entire exposure-time-response surface, which may be unrealistic in settings where the exposure is associated with the outcome only in a specific time window. We propose a framework for estimating the DLNM based on Bayesian additive regression trees. Our method operates using a set of regression trees that each assume piecewise constant relationships across the exposure-time space. In a simulation, we show that our model outperforms spline-based models when the exposure-time surface is not smooth, while both methods perform similarly in settings where the true surface is smooth. Importantly, the proposed approach is lower variance and more precisely identifies critical windows during which exposure is associated with a future health outcome. We apply our method to estimate the association between maternal exposures to PM$_2.5$ and birth weight in a Colorado, USA birth cohort.The generation interval (the time between infection of primary and secondary cases) and its often used proxy, the serial interval (the time between symptom onset of primary and secondary cases) are critical parameters in understanding infectious disease dynamics. Because it is difficult to determine who infected whom, these important outbreak characteristics are not well understood for many diseases. We present a novel method for estimating transmission intervals using surveillance or outbreak investigation data that, unlike existing methods, does not require a contact tracing data or pathogen whole genome sequence data on all cases. We start with an expectation maximization algorithm and incorporate relative transmission probabilities with noise reduction. We use simulations to show that our method can accurately estimate the generation interval distribution for diseases with different reproductive numbers, generation intervals, and mutation rates. We then apply our method to routinely collected surveillance data from Massachusetts (2010-2016) to estimate the serial interval of tuberculosis in this setting.

To define the incidence and prevalence of Behçet's syndrome (BS) in children and young people (CYP) up to the age of 16 years in the United Kingdom (UK) and Republic of Ireland (ROI).

A prospective epidemiological study was undertaken with the support of the British Paediatric Surveillance Unit (BPSU) and the British Society of Paediatric Dermatologists (BSPD). Consultants reported anonymised cases of BS seen. A follow-up study at one year examined progression of disease and treatment.

Over a two-year period, 56 cases met International Criteria for Behçet's Disease. For children under 16 years of age, the two-year period prevalence estimate was 4.2 per million (95% CI 3.2-5.4) and the incidence was 0.96 per million person years (95% CI 0.66-1.41). Mucocutaneous disease was the most common phenotype (56/100%), with ocular (10/56; 17.9%), neurological (2/56; 3.6%) and vascular involvement (3/56; 5.4%) being less common. Median age at onset was 6.34 years and at diagnosis was 11.72 years. There were slightly more female than male children reported (32/56; 55.6%). The majority of cases (85.7%) were white Caucasian. Apart from genital ulcers, which were more common in females, there were no significant differences in frequency of manifestations between male or females, nor between ethnicities. Over 83% of cases had three or more non-primary care healthcare professionals involved in their care.

BS is extremely rare in CYP in the UK and ROI and most have mucocutaneous disease. Healthcare needs are complex, and coordinated care is key.

BS is extremely rare in CYP in the UK and ROI and most have mucocutaneous disease. Healthcare needs are complex, and coordinated care is key.Measuring photosynthesis is critical for quantifying and modeling leaf to regional scale productivity of managed and natural ecosystems. This review explores existing and novel advances in photosynthesis measurements that are certain to provide innovative directions in plant science research. First, we address gas exchange approaches from leaf to ecosystem scales. Leaf level gas exchange is a mature method but recent improvements to the user interface and environmental controls of commercial systems have resulted in faster and higher quality data collection. Canopy chamber and micrometeorological methods have also become more standardized tools and have an advanced understanding of ecosystem functioning under a changing environment and through long time series data coupled with community data sharing. Second, we review proximal and remote sensing approaches to measure photosynthesis, including hyperspectral reflectance- and fluorescence-based techniques. These techniques have long been used with aircraft and orbiting satellites, but lower-cost sensors and improved statistical analyses are allowing these techniques to become applicable at smaller scales to quantify changes in the underlying biochemistry of photosynthesis. Within the past decade measurements of chlorophyll fluorescence from earth-orbiting satellites have measured Solar Induced Fluorescence (SIF) enabling estimates of global ecosystem productivity. Finally, we highlight that stronger interactions of scientists across disciplines will benefit our capacity to accurately estimate productivity at regional and global scales. Applying the multiple techniques outlined in this review at scales from the leaf to the globe are likely to advance understanding of plant functioning from the organelle to the ecosystem.

Is there any association between the appearance of smooth endoplasmic reticulum aggregates (SERa) in oocytes and ovarian stimulation, embryological, clinical and neonatal outcomes of ICSI and IVF cycles?

A suboptimal prolonged ovarian stimulation is detrimental to oocytes by inducing the occurrence of SERa, which reduces the reproductive potential of oocytes.

Controlled ovarian stimulation recruits oocytes of different qualities. Based on current evidence, it was agreed that non-homogeneous cytoplasm may represent the normal variability among oocytes rather than a dysmorphism with developmental significance. The only exception is the appearance of SERa within the ooplasm. Owing to the lack of univocal evidence in this literature about the safety of injecting oocytes with SERa and the mechanism responsible for the occurrence of SERa, this topic is still a matter of debate.

A retrospective, longitudinal cohort study performed at a tertiary level public infertility center. We included 1662 cycles (180 SEroductive potential. This raises the question of whether some stimulation regimens could be worse than others and a change in stimulation protocol would reduce the possibility of producing oocytes with suboptimal maturation. In particular, our data highlight the importance of correct timing of the trigger in order to maximize oocyte collection, not only in terms of numerosity but also their reproductive potential.

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N/A.Neuroinflammation is involved in the aetiology of many neurodegenerative disorders including Alzheimer's disease, Parkinson's disease and motor neuron disease. Whether neuroinflammation also plays an important role in the pathophysiology of frontotemporal dementia is less well known. Frontotemporal dementia is a heterogeneous classification that covers many subtypes, with the main pathology known as frontotemporal lobar degeneration. The disease can be categorized with respect to the identity of the protein that causes the frontotemporal lobar degeneration in the brain. The most common subgroup describes diseases caused by frontotemporal lobar degeneration associated with tau aggregation, also known as primary tauopathies. Evidence suggests that neuroinflammation may play a role in primary tauopathies with genome-wide association studies finding enrichment of genetic variants associated with specific inflammation-related gene loci. These loci are related to both the innate immune system, including brain resident microglia, and the adaptive immune system through possible peripheral T-cell involvement.