Teachers as writers: a systematic review

This paper is a critical literature review of empirical work from 1990-2015 on teachers as writers. It interrogates the evidence on teachers’ attitudes to writing, their sense of themselves as writers and the potential impact of teacher writing on pedagogy or student outcomes in writing. The methodology was carried out in four stages. Firstly, educational databases keyword searches located 438 papers. Secondly, initial screening identified 159 for further scrutiny, 43 of which were found to specifically address teachers’ writing identities and practices. Thirdly, these sources were screened further using inclusion/exclusion criteria. Fourthly, the 22 papers judged to satisfy the criteria were subject to in-depth analysis and synthesis. The findings reveal that the evidence base in relation to teachers as writers is not strong, particularly with regard to the impact of teachers’ writing on student outcomes. The review indicates that teachers have narrow conceptions of what counts as writing and being a writer and that multiple tensions exist, relating to low self-confidence, negative writing histories, and the challenge of composing and enacting teacher and writer positions in school. However, initial training and professional development programmes do appear to afford opportunities for reformulation of attitudes and sense of self as writer

Electrochemical oxidation of amoxicillin in its pharmaceutical formulation at boron doped diamond (BDD) electrode

In this work, voltammetric andelectrolysis experiments have been carried out on a conductive boron dopeddiamond (BDD) electrode in solution containing amoxicillin in itspharmaceutical formulation. The physical characterization of the BDD surface byscanning electron microscopy (SEM) reveals a polycrystalline structure withgrain sizes ranging between 0.3 and 0.6 µm. With Raman spectroscopy, BDDsurface is composed of diamons (Csp3) type carbon (Csp3)and graphitic type carbon (Csp2). The electrochemical characterization of the BDD electrode in sulfuric acid electrolyte showed a wide potential window worthing 2.74 V. The oxidation of Amoxicillin showed an irreversible anodic wave on the voltammogram in the domain of water stability indicating a direct oxidation of amoxicillin at BDD surface. The treatment of Amoxicillin in the synthetic wastewaters under various constant current densities 20, 50, 100, 135 mA cm-2 on BDD showed that Amoxicillin is highly reducedunder 100 mA cm-2 reaching 92% of the Chemical Oxygen Demand (COD)removal after 5 h of electrolysis. Investigation performed in perchloric acidas supporting electrolyte led to 87% of COD removal after 5 h of electrolysis.Mineralization of amoxicillin occurs on BDD and the chemical oxygen demandremoval was higher in sulfuric acid than in perchloric acid owing to theinvolvement of the in-situ formed persulfate and perchlorate to the degradation process mainly in the bulkof the solution. The instantaneous current efficiency (ICE) presents anexponential decay indicating that the process was limited by diffusion. Thespecific energy consumed after 5h of the amoxicillin electrolysis was 0.096 kWh COD-1and 0.035 kWh COD-1 in sulfuric acid and in perchloric acidrespectively

Acute submaximal exercise does not impact aspects of cognition and BDNF in people with spinal cord injury: A pilot study

ObjectiveTo investigate the effect of acute submaximal exercise, based on the spinal cord injury (SCI) Exercise Guidelines, on cognition and brain-derived neurotrophic factor (BDNF) in people with SCI.DesignEight adults (7 males) with traumatic SCI volunteered in this pre-registered pilot study. In randomized order, participants completed submaximal intensity arm cycling (60% of measured peak-power output at 55–60 rpm) for 30 min or time-matched quiet rest (control condition) on separate days. Blood-borne BDNF was measured in serum and plasma at pre-intervention, 0 min and 90 min post-intervention. Cognition was assessed using the Stroop Test and Task-Switching Test on an electronic tablet pre- and 10 min post-intervention.ResultsSubmaximal exercise had no effect on plasma [F(2,12) = 1.09; P = 0.365; η² = 0.069] or serum BDNF [F(2,12) = 0.507; P = 0.614; η² = 0.024] at either 0 min or 90 min post-intervention. Similarly, there was no impact of exercise on either Stroop [F(1,7) = 2.05; P = 0.195; η² = 0.065] or Task-Switching performance [F(1,7) = 0.016; P = 0.903; η² < 0.001] compared to the control condition. Interestingly, there was a positive correlation between years since injury and resting levels of both plasma (r = 0.831; P = 0.011) and serum BDNF (r = 0.799; P = 0.023). However, there was not relationship between years since injury and the BDNF response to exercise.ConclusionsAcute guideline-based exercise did not increase BDNF or improve aspects of cognition in persons with SCI. This work establishes a foundation for continued investigations of exercise as a therapeutic approach to promoting brain health among persons with SCI

The Impact of Sub-maximal Exercise on Neuropathic Pain, Inflammation, and Affect Among Adults With Spinal Cord Injury: A Pilot Study

Introduction: Persons with spinal cord injury (SCI) often report high levels of neuropathic pain (NP) and poor well-being, which may result from increased inflammation. This study examined the impact of sub-maximal aerobic exercise on NP, inflammation and psychological affect among adults with SCI. Methods: Eight active adults with tetraplegia (n-4, AIS A-C) and paraplegia (n = 4, AIS A-C) performed 30-min of arm-crank aerobic exercise and reported their ratings of perceived exertion (RPE) each minute. Measures of NP, affect, and inflammatory cytokines (IL-6, IL-10, IL-1ra, TNF-α) were taken pre-(T0), immediately post-(T1), and 90-min post-exercise (T2). Results: NP decreased between T0 and T1 for tetraplegics (−60%, d = 0.47; CI = −0.32, 2.02) and paraplegics (−16%, d = 0.15; CI = −0.30, 0.90). Correlations between change in cytokines and change in NP were medium-to large for tetraplegics (rs ranged from −0.820 to 0.965) and paraplegics (rs ranged from −0.598 to 0.833). However, the pattern of correlations between change in cytokines and affect was inconsistent between groups. Lower baseline levels of IL-1ra predicted greater decreases in NP immediately post-exercise (r = 0.83, p = 0.01). Conclusion: Sub-maximal exercise can positively impact NP for some persons with SCI. Further experimental research should identify the optimal exercise intensity to reduce NP for persons with SCI, in addition to understanding biomarkers which may predict changes in NP. Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT03955523

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p

Perspectives on validation of high-throughput assays supporting 21st century toxicity testing

Summary In vitro high-throughput screening (HTS

A Genome-Wide Comparative Evolutionary Analysis of Herpes Simplex Virus Type 1 and Varicella Zoster Virus

Herpes simplex virus type 1 (HSV-1) and varicella zoster virus (VZV) are closely related viruses causing lifelong infections. They are typically associated with mucocutaneous or skin lesions, but may also cause severe neurological or ophthalmic diseases, possibly due to viral- and/or host-genetic factors. Although these viruses are well characterized, genome-wide evolutionary studies have hitherto only been presented for VZV. Here, we present a genome-wide study on HSV-1. We also compared the evolutionary characteristics of HSV-1 with those for VZV. We demonstrate that, in contrast to VZV for which only a few ancient recombination events have been suggested, all HSV-1 genomes contain mosaic patterns of segments with different evolutionary origins. Thus, recombination seems to occur extremely frequent for HSV-1. We conclude by proposing a timescale for HSV-1 evolution, and by discussing putative underlying mechanisms for why these otherwise biologically similar viruses have such striking evolutionary differences

Simulating Microdosimetry in a Virtual Hepatic Lobule

The liver plays a key role in removing harmful chemicals from the body and is therefore often the first tissue to suffer potentially adverse consequences. To protect public health it is necessary to quantitatively estimate the risk of long-term low dose exposure to environmental pollutants. Animal testing is the primary tool for extrapolating human risk but it is fraught with uncertainty, necessitating novel alternative approaches. Our goal is to integrate in vitro liver experiments with agent-based cellular models to simulate a spatially extended hepatic lobule. Here we describe a graphical model of the sinusoidal network that efficiently simulates portal to centrilobular mass transfer in the hepatic lobule. We analyzed the effects of vascular topology and metabolism on the cell-level distribution following oral exposure to chemicals. The spatial distribution of metabolically inactive chemicals was similar across different vascular networks and a baseline well-mixed compartment. When chemicals were rapidly metabolized, concentration heterogeneity of the parent compound increased across the vascular network. As a result, our spatially extended lobule generated greater variability in dose-dependent cellular responses, in this case apoptosis, than were observed in the classical well-mixed liver or in a parallel tubes model. The mass-balanced graphical approach to modeling the hepatic lobule is computationally efficient for simulating long-term exposure, modular for incorporating complex cellular interactions, and flexible for dealing with evolving tissues