Automated nonlinear system modelling with multiple neural networks

This article discusses the identification of nonlinear dynamic systems using multi-layer perceptrons (MLPs). It focuses on both structure uncertainty and parameter uncertainty, which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. First, an automated network structure selection procedure is proposed within a fixed time interval for a given network construction criterion. Then, the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope with structure uncertainty, a hysteresis strategy is proposed to enable neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and a simulation example show the efficacy of the proposed method.

Automated versus non-automated weaning for reducing the duration of mechanical ventilation for critically ill adults and children (Review)

Abstract
Background: Automated closed loop systems may improve adaptation of the mechanical support to a patient's ventilatory needs and
facilitate systematic and early recognition of their ability to breathe spontaneously and the potential for discontinuation of
ventilation.

Objectives: To compare the duration of weaning from mechanical ventilation for critically ill ventilated adults and children when managed
with automated closed loop systems versus non-automated strategies. Secondary objectives were to determine differences
in duration of ventilation, intensive care unit (ICU) and hospital length of stay (LOS), mortality, and adverse events.

Search methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library 2011, Issue 2); MEDLINE (OvidSP) (1948 to August 2011); EMBASE (OvidSP) (1980 to August 2011); CINAHL (EBSCOhost) (1982 to August 2011); and the Latin American and Caribbean Health Sciences Literature (LILACS). In addition we received and reviewed auto-alerts for our search strategy in MEDLINE, EMBASE, and CINAHL up to August 2012. Relevant published reviews were sought using the Database of Abstracts of Reviews of Effects (DARE) and the Health Technology Assessment Database (HTA Database). We also searched the Web of Science Proceedings; conference proceedings; trial registration websites; and reference lists of relevant articles.

Selection criteria: We included randomized controlled trials comparing automated closed loop ventilator applications to non-automated weaning
strategies including non-protocolized usual care and protocolized weaning in patients over four weeks of age receiving invasive mechanical ventilation in an intensive care unit (ICU).

Data collection and analysis: Two authors independently extracted study data and assessed risk of bias. We combined data into forest plots using random-effects modelling. Subgroup and sensitivity analyses were conducted according to a priori criteria.

Main results: Pooled data from 15 eligible trials (14 adult, one paediatric) totalling 1173 participants (1143 adults, 30 children) indicated that automated closed loop systems reduced the geometric mean duration of weaning by 32% (95% CI 19% to 46%, P =0.002), however heterogeneity was substantial (I2 = 89%, P < 0.00001). Reduced weaning duration was found with mixed or
medical ICU populations (43%, 95% CI 8% to 65%, P = 0.02) and Smartcare/PS™ (31%, 95% CI 7% to 49%, P = 0.02) but not in surgical populations or using other systems. Automated closed loop systems reduced the duration of ventilation (17%, 95% CI 8% to 26%) and ICU length of stay (LOS) (11%, 95% CI 0% to 21%). There was no difference in mortality rates or hospital LOS. Overall the quality of evidence was high with the majority of trials rated as low risk.

Authors' conclusions: Automated closed loop systems may result in reduced duration of weaning, ventilation, and ICU stay. Reductions are more
likely to occur in mixed or medical ICU populations. Due to the lack of, or limited, evidence on automated systems other than Smartcare/PS™ and Adaptive Support Ventilation no conclusions can be drawn regarding their influence on these outcomes. Due to substantial heterogeneity in trials there is a need for an adequately powered, high quality, multi-centre randomized
controlled trial in adults that excludes 'simple to wean' patients. There is a pressing need for further technological development and research in the paediatric population.

Robust automated tumour segmentation on histological and immunohistochemical tissue images

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.

The Critical Reading of the Images Associated with Science-Related News Reports: Establishing a knowledge, skills, and attitudes framework

For the majority of adults, the media constitute their main source of information about science and science-related matters impacting on society. To help prepare young people to engage with science in the media, teachers are being exhorted to equip their students with the knowledge, skills, and attitudes to respond critically to science-related news reports. Typically, such reports comprise not only text, but also visual elements. These images are not simply adjuncts to the written word; they are integral to meaning-making. Though science teachers make considerable use of newspaper images, they tend to view these representations unproblematically, underestimating their potential ambiguity, complexity, and role in framing media messages. They rarely aim to develop students’ ability to ‘read’, critically, such graphics. Moreover, research into how this might be achieved is limited and, consequently, research-informed guidance which could support this instruction is lacking. This paper describes a study designed to formulate a framework for such teaching. Science communication scholars, science journalists and media educators with acknowledged relevant expertise were surveyed to ascertain what knowledge, skills, and attitudes they deemed useful to engagement with science related news images. Their proposals were recast as learning intentions (instructional objectives), and science and English teachers collaborated to suggest which could be addressed with secondary school students and the age group best suited to their introduction. The outcome is an inventory of learning intentions on which teachers could draw to support their planning of instructional sequences aimed at developing students’ criticality in respect of the totality of science news reports.

The effects of a volunteer mentoring programme on reading outcomes among eight- to nine-year-old children: A follow up randomized controlled trial

This article presents the findings of a randomized controlled trial evaluation of the effects of a revised version of the volunteer mentoring programme, Time to Read. Participating children received two 30-minute mentoring sessions per week from volunteer mentors who carried out paired reading activities with the children. The current trial involved 512 children aged eight to nine years from 50 primary schools. The programme was found to be effective in improving decoding skills (d=+.15), reading rate (d=+.22) and reading fluency (d=+.14) and there was some evidence of a positive effect in relation to the children’s aspirations for the future (d=+.11). However, no evidence was found of the programme having an effect on reading comprehension or reading confidence and enjoyment of reading. The article concludes by suggesting that mentoring programmes using non-specialist volunteers can be effective in improving foundational reading skills but would appear to be less effective in terms of improving higher-order skills such as comprehension. The article also suggests that such programmes are likely to be most effective if concentrating on core reading activities rather than attempting to address reading outcomes indirectly through improving children’s confidence or wider enjoyment of reading.