Analyzing Incomplete Categorical Data: Revisiting Maximum Likelihood Estimation (Mle) Procedure

Incomplete data poses formidable difficulties in the application of statistical techniques and requires special procedures to handle. The most common ways to solve this problem are by ignoring, truncating, censoring or collapsing those data, but these may lead to inappropriate conclusions because those data might contain important information. Most of the research for estimating cell probabilities involving incomplete categorical data is based on the EM algorithm. A likelihood approach is employed for estimating cell probabilities for missing values and makes comparisons between maximum likelihood estimation (MLE) and the EM algorithm. The MLE can provide almost the same estimates as that of the EM algorithm without any loss of properties. Results are compared for different distributional assumptions. Using clinical trial results from a group of 59 epileptics, results from the application of MLE and EM algorithm are compared and the advantages of MLE are highlighted

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhibits previous segmentation methods from achieving high accuracies, especially compared to other organs such as the liver, heart or kidneys. In this paper, we present a probabilistic bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans, using multi-level deep convolutional networks (ConvNets). We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i.e. superpixels. We first present a dense labeling of local image patches via $P{-}\mathrm{ConvNet}$ and nearest neighbor fusion. Then we describe a regional ConvNet ($R_1{-}\mathrm{ConvNet}$) that samples a set of bounding boxes around each image superpixel at different scales of contexts in a "zoom-out" fashion. Our ConvNets learn to assign class probabilities for each superpixel region of being pancreas. Last, we study a stacked $R_2{-}\mathrm{ConvNet}$ leveraging the joint space of CT intensities and the $P{-}\mathrm{ConvNet}$ dense probability maps. Both 3D Gaussian smoothing and 2D conditional random fields are exploited as structured predictions for post-processing. We evaluate on CT images of 82 patients in 4-fold cross-validation. We achieve a Dice Similarity Coefficient of 83.6$\pm$6.3% in training and 71.8$\pm$10.7% in testing.Comment: To be presented at MICCAI 2015 - 18th International Conference on Medical Computing and Computer Assisted Interventions, Munich, German

An empirical method to cluster objective nebulizer adherence data among adults with cystic fibrosis

Background: The purpose of using preventative inhaled treatments in cystic fibrosis is to improve health outcomes. Therefore, understanding the relationship between adherence to treatment and health outcome is crucial. Temporal variability, as well as absolute magnitude of adherence affects health outcomes, and there is likely to be a threshold effect in the relationship between adherence and outcomes. We therefore propose a pragmatic algorithm-based clustering method of objective nebulizer adherence data to better understand this relationship, and potentially, to guide clinical decisions. Methods to cluster adherence data: This clustering method consists of three related steps. The first step is to split adherence data for the previous 12 months into four 3-monthly sections. The second step is to calculate mean adherence for each section and to score the section based on mean adherence. The third step is to aggregate the individual scores to determine the final cluster (“cluster 1” = very low adherence; “cluster 2” = low adherence; “cluster 3” = moderate adherence; “cluster 4” = high adherence), and taking into account adherence trend as represented by sequential individual scores. The individual scores should be displayed along with the final cluster for clinicians to fully understand the adherence data. Three illustrative cases: We present three cases to illustrate the use of the proposed clustering method. Conclusion: This pragmatic clustering method can deal with adherence data of variable duration (ie, can be used even if 12 months’ worth of data are unavailable) and can cluster adherence data in real time. Empirical support for some of the clustering parameters is not yet available, but the suggested classifications provide a structure to investigate parameters in future prospective datasets in which there are accurate measurements of nebulizer adherence and health outcomes

Duration of intravenous antibiotic therapy in people with cystic fibrosis

Background Progressive lung damage from recurrent exacerbations is the major cause of mortality and morbidity in cystic fibrosis. Life expectancy of people with cystic fibrosis has increased dramatically in the last 40 years. One of the major reasons for this increase is the mounting use of antibiotics to treat chest exacerbations caused by bacterial infections. The optimal duration of intravenous antibiotic therapy is not clearly defined. Individuals usually receive intravenous antibiotics for 14 days, but treatment may range from 10 to 21 days. A shorter duration of antibiotic treatment risks inadequate clearance of infection which could lead to further lung damage. Prolonged courses of intravenous antibiotics are expensive and inconvenient. The risk of systemic side effects such as allergic reactions to antibiotics also increases with prolonged courses and the use of aminoglycosides requires frequent monitoring to minimise some of their side effects. However, some organisms which infect people with cystic fibrosis are known to be multi‐resistant to antibiotics, and may require a longer course of treatment. This is an update of previously published reviews. Objectives To assess the optimal duration of intravenous antibiotic therapy for treating chest exacerbations in people with cystic fibrosis. Search methods We searched the Cochrane Cystic Fibrosis and Genetic Disorders Group Trials Register which comprises references identified from comprehensive electronic database searches, handsearches of relevant journals, abstract books and conference proceedings. Most recent search of the Group's Cystic Fibrosis Trials Register: 30 May 2019. We also searched online trials registries. Most recent search of the ClinicalTrials.gov and WHO International Clinical Trials Registry Platform (ICTRP) portal: 06 January 2019. Selection criteria Randomised and quasi‐randomised controlled trials comparing different durations of intravenous antibiotic courses for acute respiratory exacerbations in people with CF, either with the same drugs at the same dosage, the same drugs at a different dosage or frequency or different antibiotics altogether, including studies with additional therapeutic agents. Data collection and analysis No eligible trials were identified for inclusion. A trial looking at the standardised treatment of pulmonary exacerbations is currently ongoing and will be included when the results are published. Main results No eligible trials were included. Authors' conclusions There are no clear guidelines on the optimum duration of intravenous antibiotic treatment. Duration of treatment is currently based on unit policies and response to treatment. Shorter duration of treatment should improve quality of life and adherence, result in a reduced incidence of drug reactions and be less costly. However, the shorter duration may not be sufficient to clear a chest infection and may result in an early recurrence of an exacerbation. This systematic review identifies the need for a multicentre, randomised controlled trial comparing different durations of intravenous antibiotic treatment as it has important clinical and financial implications. The currently ongoing STOP2 trial is expected to provide some guidance on these questions when published

Anatomy-specific classification of medical images using deep convolutional nets

Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. "Deep learning" methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ- or body-part-specific anatomical classification of medical images acquired using computed tomography (CT) with ConvNets. We train a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical classes. Key-images were mined from a hospital PACS archive, using a set of 1,675 patients. We show that a data augmentation approach can help to enrich the data set and improve classification performance. Using ConvNets and data augmentation, we achieve anatomy-specific classification error of 5.9 % and area-under-the-curve (AUC) values of an average of 0.998 in testing. We demonstrate that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis.Comment: Presented at: 2015 IEEE International Symposium on Biomedical Imaging, April 16-19, 2015, New York Marriott at Brooklyn Bridge, NY, US

The impact of Feedback on student attainment: a systematic review

Abstract Meta-syntheses have reported positive impacts of feedback for student achievement at different stages of education and have been influential in establishing feedback as an effective strategy to support student learning. However, these syntheses combine studies of a variety of different feedback approaches, combine studies where feedback is one of a number of intervention components and have several methodological limitations. For example the lack of quality appraisal of the included studies. There is also still more research needed to investigate the impact of different types of feedback on different students in different settings. Objective This systematic review was conducted at the request of the Education Endowment Foundation to provide more precise estimates of the impact of different types of feedback in different contexts for different learners aged between 5 and 18. The review analysis sought to explore potential variations in the impact of feedback through subgroup analysis of the characteristics of the feedback, the educational setting, the learners and the subject. This review provides evidence that can be used to support the development of guidance for teachers and schools about feedback practices. Methods design A systematic review was undertaken in two stages. First, a systematic map identified and characterised a subset of studies that investigated the attainment impacts of feedback. Second, an in-depth review comprising of a meta-analysis was performed to answer the review questions about the impact of interventions that comprised of feedback only and to explore the variety of characteristics that may influence the impact of feedback. Methods search We used the Microsoft Academic Graph (MAG) dataset hosted in EPPI-Reviewer to conduct a semantic network analysis to identify records related to a set of pre-identified study references. The MAG search identified 23,725 potential studies for screening. Methods study selection Studies were selected using a set of pre-specified selection criterion. Semi-automated priority screening was used to screen the title and abstract of studies using bespoke systematic review software EPPI-Reviewer. The title and abstract screening was stopped after 3,028 studies and 745 were identified for full-text screening. Reviewers carried out a moderation exercise, all screening a selection of the same titles to develop consistency of screening. Thereafter, single reviewer screening was used with referral for a second reviewer opinion in cases of uncertainty. Methods data collection Studies were coded using a bespoke data extraction tool developed by the EEF Database Project. Study quality was assessed using a bespoke risk of bias assessment adapted from the ROBINS-I tool. The review team undertook a moderation exercise coding the same set of studies to develop consistency. Thereafter, single reviewer coding was used, based on the full text with referral for a second opinion in cases of uncertainty. Methods synthesis Data from the studies was used to calculate standardised effect sizes (Standardised Mean Difference- Hedge’s g). Effect sizes from each study were combined to produce a pooled estimate of effect using Random Effects Meta-analysis. Statistical Heterogeneity tests were carried out for each synthesis. Sensitivity analysis was carried out for assessed study quality. Subgroup analysis was completed using meta-analysis to explore outcomes according to the different characteristics of feedback, context and subjects. Main results The full text screening identified 304 studies to include in the initial systematic map, of which 171 studies investigated feedback only. After applying final selection criteria, 43 papers with 51 studies published in and after the year 2000 were included. The 51 studies had approximately 14,400 students. Forty studies were experiments with random allocation to groups and 11 were prospective quantitative experimental design studies. The overall ecological validity was assessed as moderate to high in 40 studies and the overall risk of bias assessed as low to moderate in 44 studies. The interventions took place in curriculum subjects including literacy, mathematics, science, social studies, and languages, and tested other cognitive outcomes. The source of feedback included teacher, researcher, digital, or automated means. Feedback to individual students is reported in 48 studies and feedback to group or class is reported in four studies. Feedback took the form of spoken verbal, non-verbal, written verbal, and written non-verbal. Different studies investigated feedback that took place immediately after the task, during the task and up to one week after the task (delayed feedback). Most of the feedback interventions gave the learner feedback about the outcome and the process/strategy. Some provided feedback on outcome only and two provided feedback about task/strategy only. On the main research question, the pooled estimate of effect of synthesis of all studies with a low or moderate risk of bias indicated that students who received feedback had better performance than students who did not receive feedback or experienced usual practice (g = 0.17, 95% C.I. 0.09 to 0.25). However, there is statistically significant heterogeneity between these studies (I2 = 44%, Test for Heterogeneity: Q(df = 37) = 65.92, p = 0.002), which suggests that this may not be a useful indicator of the general impact of feedback on attainment when compared to no feedback or usual practice. The heterogeneity analysis suggested considerable heterogeneity between studies in the main synthesis and all the subgroup synthesis, and in the majority of the cases the heterogeneity is statistically significant. This means caution is required when considering the results of the synthesis. The results of the subgroup synthesis suggest that a variety of student and context factors may have an effect on the impact of feedback. Conclusions The results of the review may be considered broadly consistent with claims made on the basis of previous synthesis and meta-synthesis, suggesting that feedback interventions, on average, have a positive impact on attainment when compared to no feedback or usual practice. The limitations in the study reports and the comparatively small number of studies within each subgroup synthesis meant that the review was not able to provide very much more certainty about the factors that affect variation in the impact of single component feedback interventions within different contexts and with different students. More research is needed in this area to consider what may moderate the impact of feedback. However, the findings further support the conclusion made by previous studies that feedback, on average, has a positive impact on attainment; moreover, this is based on a more precise and robust analysis than previous syntheses. This suggests that feedback may have a role to play in raising attainment alongside other effective interventions. Findings were further interpreted by a panel of expert practitioners and academics to produce the EEF’s Teacher feedback to improve pupil learning guidance report. 1. Background and review rationale Feedback can be defined as information communicated to the learner that is intended to modify the learner’s thinking or behaviour for the purpose of improving learning. Meta-syntheses have reported positive impacts of feedback, with effect sizes ranging from d = 0.70 to d = 0.79 for student achievement at different stages of education and have been influential in establishing feedback as highly effective with regards to student learning. For example, the EEF Teaching and Learning Toolkit meta-synthesis suggests that feedback may have ‘very high’ impact (equivalent to eight months’ additional progress) for relatively low cost. However, caution is necessary when interpreting the findings of these meta-syntheses for a number of reasons. Firstly, the average effect size reported in the EEF Toolkit is based on combining the estimates from existing meta-analyses of individual studies, which may contain limitations of various kinds (see the list below for examples) that may mean that average effect sizes identified are overestimates. Second, some studies included in syntheses (such as Kluger and DeNisi’s meta-analysis ) suggest that some feedback interventions may, in fact, negatively impact pupils. Third, previous meta-syntheses have not explored in detail the impact of potential moderating factors, such as different types of feedback. As Ekecrantz has argued, there is still a need to better understand how and under what circumstances teacher feedback on student performance promotes learning as well as, to question the generalised claim (that feedback improves attainment) itself. For example, a recent meta-analysis that re-analysed studies included in the original synthesis by Hattie and Timperley revised down the average effect size from the estimates of the effects of feedback from their originally published Standardised Mean Difference of d = 0.79 to d = 0.48. In the revised meta-analysis, 17% of the effect sizes from individual studies were negative. The confidence interval ranged from d = 0.48 to d = 0.62, and the authors found a wide range of effect sizes. Different moderators were also investigated to explore the impact of different characteristics of context and feedback. Whilst this meta-analysis offers improvements over previous meta-syntheses, it has a number of limitations, including: • It only included studies drawn from 36 existing meta-analyses, the most recent of which was published in 2015. Eligible studies published after 2015 or not included in these meta-analyses would not have been included. • All comparative study designs were included. Less robust study designs may have overestimated the positive effect of feedback. • There was no reported study quality assessment/moderation or sensitivity analysis, which may have led to an overestimation of the pooled effect sizes. • The meta-analyses included studies with high levels of heterogeneity, I2 = 80% or more (in the main and moderator analysis). This suggests that the synthesis may be combining studies/comparing feedback practices inappropriately. • The meta-analysis did not consider all potentially relevant moderating factors. It may also be the case that the impact of feedback depends on factors other than those analysed, including the ability of the learner, the learning context, and/or the frequency, duration, timing, and type of feedback. This systematic review was conducted at the request of the EEF to try and provide more accurate and precise estimates of the impact of different types of feedback in different schooling contexts. The review examines the impact of single component feedback, in different contexts, and for different learners with a greater degree of granularity and precision than is currently available via the EEF Teaching and Learning Toolkit strand on ‘Feedback’. For EEF, the purpose of the systematic review is to provide evidence that can be used to inform guidance for teachers and schools about effective feedback practices. The systematic review methods and processes were developed and carried out conterminously with the EEF Database project with a view to facilitating the future use of the produced resources and supporting the ongoing work of the Database project. 1.1 Domain being studied: Feedback approaches This review focuses on interventions that provide feedback from teachers to learners in mainstream educational settings. Feedback is defined in accordance with the EEF toolkit definition: ‘Feedback is information given to the learner and/or teacher about the learner’s performance relative to learning goals or outcomes. It should aim to produce (and be capable of) producing improvement in students’ learning. Feedback redirects or refocuses either the teacher’s or the learner’s actions to achieve a goal, by aligning effort and activity with an outcome. It can be about the output of the activity, the process of the activity, the student’s management of their learning or self-regulation, or them as individuals. This feedback can be verbal or written or can be given through tests or via digital technology. It can come from a teacher or someone taking a teaching role, or from ‘peers’.’ This initial broad definition, whilst conceptually coherent, does create challenges both in practice for teachers and in terms of identifying and distinguishing between practices when considering research evidence. For example, what is the difference between small group learning and ‘peer feedback’? It seems perfectly reasonable to assume that small group learning must contain conversations between students about their work and the task they have been asked to complete and thus is ‘feedback’. However, in practice, this may not be what teachers think of as ‘feedback’ and in the research literature, ‘small group learning’ is investigated both as a unique pedagogical strategy and as a component of a number of other pedagogical strategies. As the development of the understanding of the scope of the review evolved, the working definition of feedback for the review became modified practically through the exclusion of certain categories of intervention, even though they may contain an element of feedback practice. The inclusion criteria in the methods section outlines the revised definition that the review team used. 1.2 Conceptual framework/Theory of Change There are several ways in which feedback is conceptualised as improving learner performance—i.e. as a Theory of Change. The ‘Feedback’ strand in the EEF Teaching and Learning Toolkit draws most explicitly on the conceptualisation of Hattie and Timperley’s (2007) model. This model emphasises the importance of systems of feedback where the teacher provides feedback to the specific needs of individual students. The searching processes used in this review are consistent with this model as the studies used in the Feedback strand of the EEF Teaching and Learning Toolkit were used to ‘seed’ the search. However, they did not preclude the inclusion of studies that may draw on other ‘models’ of feedback which, though similar to Hattie and Timperley (2007), may be argued to place more emphasis on, for example: developing learner self-regulation (Nicole and Macfarlane-Dick, 2006); students’ intrinsic motivation (Dweck, 2016); and/or are subject specific—for example, ‘Thinking Mathematically’ (Mason, Burton and Stacey, 2010). The coding tools used in the review were informed by the model (in terms of coding about the source and content of the feedback; see Appendix 3). 1.3 Review design A systematic review approach was used to investigate the research questions. The review was undertaken in two stages. First, a systematic map identified and described the feedback characteristics of a subset of studies that investigated the attainment impacts of feedback. The map was used to make decisions about focusing the analysis in the second in-depth systematic review stage. At the second stage an in-depth review, including meta-analysis, was performed on a subset of the studies identified in the map to answer the review questions and explore the variety of intervention and context characteristics that may influence the impact of feedback. This systematic review was designed to complement the work of the EEF Database project. The EEF Database project is currently undertaking a programme to extract and code the individual studies from the meta-synthesis used in the EEF Teaching and Learning Toolkit. The search strategy used in this review was ‘seeded’ from studies identified as being about ‘feedback’ in the database, and this systematic review used the coding tools developed by the Database team (see Appendix 3). The studies newly identified in this review will be subsequently included in the EEF Database. This systematic review was also designed to provide additional research evidence for use in guidance on feedback developed for schools produced by the EEF, and therefore to fit with a particular time window for the review’s production. The results of the meta-analyses were presented to an advisory panel of academics and teaching practitioners, who used the results , their own expertise, a review of practice undertaken by the University of Oxford, and conceptual models (such as Hattie and Timperley) to draft recommendations for practice

The SNARE Protein Syntaxin 3 Confers Specificity for Polarized Axonal Trafficking in Neurons.

Cell polarity and precise subcellular protein localization are pivotal to neuronal function. The SNARE machinery underlies intracellular membrane fusion events, but its role in neuronal polarity and selective protein targeting remain unclear. Here we report that syntaxin 3 is involved in orchestrating polarized trafficking in cultured rat hippocampal neurons. We show that syntaxin 3 localizes to the axonal plasma membrane, particularly to axonal tips, whereas syntaxin 4 localizes to the somatodendritic plasma membrane. Disruption of a conserved N-terminal targeting motif, which causes mislocalization of syntaxin 3, results in coincident mistargeting of the axonal cargos neuron-glia cell adhesion molecule (NgCAM) and neurexin, but not transferrin receptor, a somatodendritic cargo. Similarly, RNAi-mediated knockdown of endogenous syntaxin 3 leads to partial mistargeting of NgCAM, demonstrating that syntaxin 3 plays an important role in its targeting. Additionally, overexpression of syntaxin 3 results in increased axonal growth. Our findings suggest an important role for syntaxin 3 in maintaining neuronal polarity and in the critical task of selective trafficking of membrane protein to axons