Epistemic sabotage: The production and disqualification of evidence in disability benefit claims.

Disability benefits function by demarcating categories of need (the administrative category of disability) and determine eligibility using assessments of functioning. In the UK, these assessments are the Work Capability Assessment and PIP assessment. Inherently technical and abstruse processes, these assessments have been opportune sites for welfare reform in recent years. Disability benefits have also been a central point of contention between disability studies and sociology. Sociology has traditionally favoured an 'incomes approach' and called for more adequate financial support from the state. Early figures in the disabled people's movement rejected this position, and aligned with an oppression paradigm, argued for a more radical economic and social inclusion. We contend that this divide, set out in the Fundamental Principles of Disability, remains relevant for researching welfare reform today. This article treats benefits assessments as epistemic practices-interactional processes wherein claimants, their personal health professionals and commercial assessment providers come together in the production of knowledge about disability. Data include 50 in-depth interviews with benefit claimants and a discourse analysis of official texts directed at claimants, personal health professionals and commercial assessment providers. We outline a phenomenon we term 'epistemic sabotage', whereby the knowledge claims of claimants and their health professionals are systemically disqualified.

AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden.

The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their deployment is hindered by the high cost of labelling the crop images to provide data for model training. This study examines the capabilities of semi-supervised and active learning to minimise effort when labelling cotton lint samples while maintaining high classification accuracy. Random forest classification models were developed using supervised learning, semi-supervised learning, and active learning to determine Egyptian cotton grade. Compared to supervised learning (80.20-82.66%) and semi-supervised learning (81.39-85.26%), active learning models were able to achieve higher accuracy (82.85-85.33%) with up to 46.4% reduction in the volume of labelled data required. The primary obstacle when using machine learning for Egyptian cotton grading is the time required for labelling cotton lint samples. However, by applying active learning, this study successfully decreased the time needed from 422.5 to 177.5 min. The findings of this study demonstrate that active learning is a promising approach for developing accurate and efficient machine learning models for grading food and industrial crops.

'Do they ever think about people like us?': The experiences of people with learning disabilities in England and Scotland during the COVID-19 pandemic.

People with learning disabilities in England and Scotland have experienced an increased risk of illness and death during the COVID-19 pandemic. Drawing on data of a longitudinal qualitative study with 71 disabled people and 31 disability organisations, this article examines the experiences of 24 people with learning disabilities in England and Scotland during the pandemic, reflecting on what rendered them vulnerable and placed them at risk. Qualitative interviews were conducted with participants and key informants at two timepoints; June-August 2020 and February-April 2021. Findings emerged across four key themes: failure to plan for the needs of people with learning disabilities; the suspension and removal of social care; the impact of the pandemic on people's everyday routines; and lack of vaccine prioritisation. The inequalities experienced by people with learning disabilities in this study are not particular to the pandemic. We explore the findings in the context of theoretical frameworks of vulnerability, including Fineman's conceptualisation of a 'vulnerability paradigm'. We conclude that the structured marginalisation of people with disabilities, entrenched by government action and inaction, have created and exacerbated their vulnerability. Structures, policies and action must change.

Domain Adaptation for In-Line Allergen Classification of Agri-Food Powders Using Near-Infrared Spectroscopy.

The addition of incorrect agri-food powders to a production line due to human error is a large safety concern in food and drink manufacturing, owing to incorporation of allergens in the final product. This work combines near-infrared spectroscopy with machine-learning models for early detection of this problem. Specifically, domain adaptation is used to transfer models from spectra acquired under stationary conditions to moving samples, thereby minimizing the volume of labelled data required to collect on a production line. Two deep-learning domain-adaptation methodologies are used: domain-adversarial neural networks and semisupervised generative adversarial neural networks. Overall, accuracy of up to 96.0% was achieved using no labelled data from the target domain moving spectra, and up to 99.68% was achieved when incorporating a single labelled data instance for each material into model training. Using both domain-adaptation methodologies together achieved the highest prediction accuracies on average, as did combining measurements from two near-infrared spectroscopy sensors with different wavelength ranges. Ensemble methods were used to further increase model accuracy and provide quantification of model uncertainty, and a feature-permutation method was used for global interpretability of the models.

Performance of the AusSCORE II and STS Score for Coronary Artery Bypass Grafting in a New Zealand Population.

BACKGROUND: The updated Australian System for Cardiac Operative Risk Evaluation (AusSCORE II) and the Society of Thoracic Surgeons (STS) Score are well-established tools in cardiac surgery for estimating operative mortality risk. No validation analysis of both risk models has been undertaken for a contemporary New Zealand population undergoing isolated coronary bypass surgery. We therefore aimed to assess the efficacy of these models in predicting mortality for New Zealand patients receiving isolated coronary artery bypass grafting (CABG). MATERIAL AND METHODS: A prospective database was maintained of patients undergoing isolated CABG at a major tertiary referral centre in New Zealand between September 2014 and September 2017. This database collected the patients' demographic, clinical, biochemical, operative and mortality data. The primary outcome measure was the correlation between the predicted AusSCORE II and STS Score mortality risks and the observed 30-day mortality events for all patients in the database using discrimination and calibration statistics. Discrimination and calibration were assessed using receiver operating characteristic (ROC) curves and the Hosmer-Lemeshow test respectively. RESULTS: A total of 933 patients underwent isolated CABG during the 3-year study period. There were seven deaths in the study cohort occurring within 30 days of surgery. Discrimination analysis demonstrated the area under the ROC curve (AUC) of the AusSCORE II and STS Score as 88.2% (95% CI: 85.9-90.2, p<0.0001) and 92.1% (95% CI: 90.2-93.7, p<0.0001) respectively. Calibration analysis revealed Hosmer-Lemeshow test p-values for the AusSCORE II and STS Score as 0.696 and 0.294 respectively. DISCUSSION: ROC curve analysis produced very high and statistically significant AUC values for the AusSCORE II and STS Score. Hosmer-Lemeshow test analysis revealed that both risk scoring tools are well calibrated for our study cohort. Therefore, the AusSCORE II and STS Score are both strongly predictive of 30-day mortality for isolated coronary artery bypass grafting surgery in our New Zealand patient population. Both risk models have performed with excellent discrimination and calibration. There is, however, a need to consider the performance of these risk stratification models in other cardiac surgical procedures outside isolated coronary bypass surgery where appropriate.

Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning.

Mixing is one of the most common processes across food, chemical, and pharmaceutical manufacturing. Real-time, in-line sensors are required for monitoring, and subsequently optimising, essential processes such as mixing. Ultrasonic sensors are low-cost, real-time, in-line, and applicable to characterise opaque systems. In this study, a non-invasive, reflection-mode ultrasonic measurement technique was used to monitor two model mixing systems. The two systems studied were honey-water blending and flour-water batter mixing. Classification machine learning models were developed to predict if materials were mixed or not mixed. Regression machine learning models were developed to predict the time remaining until mixing completion. Artificial neural networks, support vector machines, long short-term memory neural networks, and convolutional neural networks were tested, along with different methods for engineering features from ultrasonic waveforms in both the time and frequency domain. Comparisons between using a single sensor and performing multisensor data fusion between two sensors were made. Classification accuracies of up to 96.3% for honey-water blending and 92.5% for flour-water batter mixing were achieved, along with R2 values for the regression models of up to 0.977 for honey-water blending and 0.968 for flour-water batter mixing. Each prediction task produced optimal performance with different algorithms and feature engineering methods, vindicating the extensive comparison between different machine learning approaches.

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression.

Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes.

The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods.

: Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments.

Enhanced Clean-In-Place Monitoring Using Ultraviolet Induced Fluorescence and Neural Networks.

Clean-in-place (CIP) processes are extensively used to clean industrial equipment without the need for disassembly. In food manufacturing, cleaning can account for up to 70% of water use and is also a heavy user of energy and chemicals. Due to a current lack of real-time in-process monitoring, the non-optimal control of the cleaning process parameters and durations result in excessive resource consumption and periods of non-productivity. In this paper, an optical monitoring system is designed and realized to assess the amount of fouling material remaining in process tanks, and to predict the required cleaning time. An experimental campaign of CIP tests was carried out utilizing white chocolate as fouling medium. During the experiments, an image acquisition system endowed with a digital camera and ultraviolet light source was employed to collect digital images from the process tank. Diverse image segmentation techniques were considered to develop an image processing procedure with the aim of assessing the area of surface fouling and the fouling volume throughout the cleaning process. An intelligent decision-making support system utilizing nonlinear autoregressive models with exogenous inputs (NARX) Neural Network was configured, trained and tested to predict the cleaning time based on the image processing results. Results are discussed in terms of prediction accuracy and a comparative study on computation time against different image resolutions is reported. The potential benefits of the system for resource and time efficiency in food manufacturing are highlighted.

Readiness for Delivering Digital Health at Scale: Lessons From a Longitudinal Qualitative Evaluation of a National Digital Health Innovation Program in the United Kingdom.

BACKGROUND: Digital health has the potential to support care delivery for chronic illness. Despite positive evidence from localized implementations, new technologies have proven slow to become accepted, integrated, and routinized at scale. OBJECTIVE: The aim of our study was to examine barriers and facilitators to implementation of digital health at scale through the evaluation of a £37m national digital health program: ?Delivering Assisted Living Lifestyles at Scale" (dallas) from 2012-2015. METHODS: The study was a longitudinal qualitative, multi-stakeholder, implementation study. The methods included interviews (n=125) with key implementers, focus groups with consumers and patients (n=7), project meetings (n=12), field work or observation in the communities (n=16), health professional survey responses (n=48), and cross program documentary evidence on implementation (n=215). We used a sociological theory called normalization process theory (NPT) and a longitudinal (3 years) qualitative framework analysis approach. This work did not study a single intervention or population. Instead, we evaluated the processes (of designing and delivering digital health), and our outcomes were the identified barriers and facilitators to delivering and mainstreaming services and products within the mixed sector digital health ecosystem. RESULTS: We identified three main levels of issues influencing readiness for digital health: macro (market, infrastructure, policy), meso (organizational), and micro (professional or public). Factors hindering implementation included: lack of information technology (IT) infrastructure, uncertainty around information governance, lack of incentives to prioritize interoperability, lack of precedence on accountability within the commercial sector, and a market perceived as difficult to navigate. Factors enabling implementation were: clinical endorsement, champions who promoted digital health, and public and professional willingness. CONCLUSIONS: Although there is receptiveness to digital health, barriers to mainstreaming remain. Our findings suggest greater investment in national and local infrastructure, implementation of guidelines for the safe and transparent use and assessment of digital health, incentivization of interoperability, and investment in upskilling of professionals and the public would help support the normalization of digital health. These findings will enable researchers, health care practitioners, and policy makers to understand the current landscape and the actions required in order to prepare the market and accelerate uptake, and use of digital health and wellness services in context and at scale.

The feasibility of a randomised controlled trial of Dyadic Developmental Psychotherapy.

BACKGROUND: Maltreated children have significant and complex problems which clinicians find difficult to diagnose and treat. Previous US pilot work suggests that Dyadic Developmental Psychotherapy (DDP) may be effective; however, rigorous evidence from a randomised controlled trial (RCT) is lacking. The purpose of this study is to establish the feasibility of an RCT of DDP by exploring the ways that DDP is operating across different UK sites and the impacts of current practice on the potential set-up of an RCT. METHODS: Qualitative methods (interviews, focus groups and teleconferences) were used to explore trial feasibility with therapists and service managers from teams implementing both DDP and possible control interventions. Data were analysed thematically and related to various aspects of trial design. RESULTS: DDP was commonly regarded as having a particular congruence with the complexity of maltreatment-associated problems and a common operating model of DDP was evident across sites. A single control therapy was harder to establish, however, and it is likely to be a non-specific and context-dependent intervention/s offered within mainstream Child and Adolescent Mental Health Services (CAMHS). Because a 'gold standard' Treatment as Usual (TAU) does not currently exist, randomisation between DDP and TAU (CAMHS) therefore looks feasible and ethical. The nature of family change during DDP was regarded as multi-faceted, non-linear and relationship-based. Assessment tools need to be carefully considered in terms of their ability to capture change that covers both individual child and family-based functioning. CONCLUSIONS: An RCT of DDP is feasible and timely. This study has demonstrated widespread interest, support and engagement regarding an RCT and permissions have been gained from sites that have shown readiness to participate. As maltreated children are among the most vulnerable in society, and as there are currently no treatments with RCT evidence, such a trial would be a major advance in the field.

Examining the feasibility of an economic analysis of dyadic developmental psychotherapy for children with maltreatment associated psychiatric problems in the United Kingdom.

BACKGROUND: Children with maltreatment associated psychiatric problems are at increased risk of developing behavioural or mental health disorders. Dyadic Developmental Psychotherapy (DDP) was proposed as treatment for children with maltreatment histories in the USA, however, being new to the UK little is known of its effectiveness or cost-effectiveness. As part of an exploratory study, this paper explores the feasibility of undertaking economic analysis of DDP in the UK. METHODS: Feasibility for economic analysis was determined by ensuring such analysis could meet key criteria for economic evaluation. Phone interviews were conducted with professionals (therapists trained and accredited or in the process of becoming accredited DDP practitioners). Three models were developed to represent alternative methods of DDP service delivery. Once appropriate comparators were determined, economic scenarios were constructed. Cost analyses were undertaken from a societal perspective. Finally, appropriate outcome measurement was explored through clinical opinion, literature and further discussions with clinical experts. RESULTS: Three DDP models were constructed: DDP Full-Basic, DDP Home-Based and DDP Long-Term. Two potential comparator interventions were identified and defined as Consultation with Carers and Individual Psychotherapy. Costs of intervention completion per case were estimated to be: £6,700 (DDP Full-Basic), £7,100 (Consultations with Carers), £7,200 (DDP Home-Based), £11,400 (Individual Psychotherapy) and £14,500 (DDP Long-Term). None of the models of service delivery were found to currently measure effectiveness consistently. The Strengths and Difficulties Questionnaire (SDQ) was deemed an appropriate primary outcome measure, however, it does not cover all disorders DDP intends to treat and the SDQ is not a direct measure of health gain. Inclusion of quality of life measurement is required for comprehensive economic analysis. CONCLUSIONS: Economic analysis of DDP in the UK is feasible if vital next steps are taken to measure intervention outcomes consistently, ideally with a quality of life measurement. An economic analysis using the models constructed could determine the potential cost-effectiveness of DDP in the UK and identify the most efficient mode of service delivery.

Promoting equality, diversity and inclusion in research and funding: reflections from a digital manufacturing research network.

BACKGROUND: Equal, diverse, and inclusive teams lead to higher productivity, creativity, and greater problem-solving ability resulting in more impactful research. However, there is a gap between equality, diversity, and inclusion (EDI) research and practices to create an inclusive research culture. Research networks are vital to the research ecosystem, creating valuable opportunities for researchers to develop their partnerships with both academics and industrialists, progress their careers, and enable new areas of scientific discovery. A feature of a network is the provision of funding to support feasibility studies - an opportunity to develop new concepts or ideas, as well as to 'fail fast' in a supportive environment. The work of networks can address inequalities through equitable allocation of funding and proactive consideration of inclusion in all of their activities. METHODS: This study proposes a strategy to embed EDI within research network activities and funding review processes. This paper evaluates 21 planned mitigations introduced to address known inequalities within research events and how funding is awarded. EDI data were collected from researchers engaging in a digital manufacturing network activities and funding calls to measure the impact of the proposed method. RESULTS: Quantitative analysis indicates that the network's approach was successful in creating a more ethnically diverse network, engaging with early career researchers, and supporting researchers with care responsibilities. However, more work is required to create a gender balance across the network activities and ensure the representation of academics who declare a disability. Preliminary findings suggest the network's anonymous funding review process has helped address inequalities in funding award rates for women and those with care responsibilities, more data are required to validate these observations and understand the impact of different interventions individually and in combination. CONCLUSIONS: In summary, this study offers compelling evidence regarding the efficacy of a research network's approach in advancing EDI within research and funding. The network hopes that these findings will inform broader efforts to promote EDI in research and funding and that researchers, funders, and other stakeholders will be encouraged to adopt evidence-based strategies for advancing this important goal.

Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms.

Apples are usually bagged during the growing process, which can effectively improve the quality. Establishing an in situ nondestructive testing model for in-tree apples is very important for fruit companies in selecting raw apple materials for valuation. Low-maturity apples and high-maturity apples were acquired separately by a handheld tester for the internal quality assessment of apples developed by our group, and the effects of the two maturity levels on the soluble solids content (SSC) detection of apples were compared. Four feature selection algorithms, like ant colony optimization (ACO), were used to reduce the spectral complexity and improve the apple SSC detection accuracy. The comparison showed that the diffuse reflectance spectra of high-maturity apples better reflected the internal SSC information of the apples. The diffuse reflectance spectra of the high-maturity apples combined with the ACO algorithm achieved the best results for SSC prediction, with a prediction correlation coefficient (Rp) of 0.88, a root mean square error of prediction (RMSEP) of 0.5678 °Brix, and a residual prediction deviation (RPD) value of 2.466. Additionally, the fruit maturity was predicted using PLS-LDA based on color data, achieveing accuracies of 99.03% and 99.35% for low- and high-maturity fruits, respectively. These results suggest that in-tree apple in situ detection has great potential to enable improved robustness and accuracy in modeling apple quality.

A feasibility randomised controlled trial of the New Orleans intervention for infant mental health: a study protocol.

Child maltreatment is associated with life-long social, physical, and mental health problems. Intervening early to provide maltreated children with safe, nurturing care can improve outcomes. The need for prompt decisions about permanent placement (i.e., regarding adoption or return home) is internationally recognised. However, a recent Glasgow audit showed that many maltreated children "revolve" between birth families and foster carers. This paper describes the protocol of the first exploratory randomised controlled trial of a mental health intervention aimed at improving placement permanency decisions for maltreated children. This trial compares an infant's mental health intervention with the new enhanced service as usual for maltreated children entering care in Glasgow. As both are new services, the trial is being conducted from a position of equipoise. The outcome assessment covers various fields of a child's neurodevelopment to identify problems in any ESSENCE domain. The feasibility, reliability, and developmental appropriateness of all outcome measures are examined. Additionally, the potential for linkage with routinely collected data on health and social care and, in the future, education is explored. The results will inform a definitive randomised controlled trial that could potentially lead to long lasting benefits for the Scottish population and which may be applicable to other areas of the world. This trial is registered with ClinicalTrials.gov (NC01485510).

A review of ultrasonic sensing and machine learning methods to monitor industrial processes.

Supervised machine learning techniques are increasingly being combined with ultrasonic sensor measurements owing to their strong performance. These techniques also offer advantages over calibration procedures of more complex fitting, improved generalisation, reduced development time, ability for continuous retraining, and the correlation of sensor data to important process information. However, their implementation requires expertise to extract and select appropriate features from the sensor measurements as model inputs, select the type of machine learning algorithm to use, and find a suitable set of model hyperparameters. The aim of this article is to facilitate implementation of machine learning techniques in combination with ultrasonic measurements for in-line and on-line monitoring of industrial processes and other similar applications. The article first reviews the use of ultrasonic sensors for monitoring processes, before reviewing the combination of ultrasonic measurements and machine learning. We include literature from other sectors such as structural health monitoring. This review covers feature extraction, feature selection, algorithm choice, hyperparameter selection, data augmentation, domain adaptation, semi-supervised learning and machine learning interpretability. Finally, recommendations for applying machine learning to the reviewed processes are made.

Disabled people in Britain and the impact of the COVID-19 pandemic.

This paper reports on in-depth qualitative interviews conducted with 69 disabled people in England and Scotland, and with 28 key informants from infrastructure organisations in the voluntary and statutory sectors, about the impact of COVID-19, and measures taken to control it. Participants were recruited through voluntary organisations. As with everyone, the Pandemic has had a huge impact: we discuss the dislocations it has caused in everyday life; the failures of social care; the use of new technologies; and participants' view on leadership and communication. We conclude with suggestions for urgent short term and medium term responses, so that the United Kingdom and other countries can respond better to this and other pandemics, and build a more inclusive world.

Mechanism of Visible Light-Mediated Alkene Aminoarylation with Arylsulfonylacetamides.

Alkene aminoarylation with arylsulfonylacetamides via a visible-light mediated radical Smiles-Truce rearrangement represents a convenient approach to the privileged arylethylamine pharmacaphore traditionally generated by circuitous, multi-step sequences. Herein, we report detailed synthetic, spectroscopic, kinetic, and computational studies designed to interrogate the proposed mechanism, including the key aryl transfer event. The data are consistent with a rate-limiting 1,4-aryl migration occurring either via a stepwise process involving a radical Meisenheimer-like intermediate or in a concerted fashion dependent on both arene electronics and alkene sterics. Our efforts to probe the mechanism have significantly expanded the substrate scope of the transformation with respect to the migrating aryl group and provide further credence to the synthetic potential of radical aryl migrations.

The Best Services Trial (BeST?): a cluster randomised controlled trial comparing the clinical and cost-effectiveness of New Orleans Intervention Model with services as usual (SAU) for infants and young children entering care.

BACKGROUND: Abused and neglected children are at increased risk of health problems throughout life, but negative effects may be ameliorated by nurturing family care. It is not known whether it is better to place these children permanently with substitute (foster or adoptive) families or to attempt to reform their birth families. Previously, we conducted a feasibility randomised controlled trial (RCT) of the New Orleans Intervention Model (NIM) for children aged 0-60 months coming into foster care in Glasgow. NIM is delivered by a multidisciplinary health and social care team and offers families, whose child has been taken into foster care, a structured assessment of family relationships followed by a trial of treatment aiming to improve family functioning. A recommendation is then made for the child to return home or for adoption. In the feasibility RCT, families were willing to be randomised to NIM or optimised social work services as usual and equipoise was maintained. Here we present the protocol of a substantive RCT of NIM including a new London site. METHODS: The study is a multi-site, pragmatic, single-blind, parallel group, cluster randomised controlled superiority trial with an allocation ratio of 1:1. We plan to recruit approximately 390 families across the sites, including those recruited in our feasibility RCT. They will be randomly allocated to NIM or optimised services as usual and followed up to 2.5 years post-randomisation. The principal outcome measure will be child mental health, and secondary outcomes will be child quality of life, the time taken for the child to be placed in permanent care (rehabilitation home or adoption) and the quality of the relationship with the primary caregiver. DISCUSSION: The study is novel in that infant mental health professionals rarely have a role in judicial decisions about children's care placements, and RCTs are rare in the judicial context. The trial will allow us to determine whether NIM is clinically and cost-effective in the UK and findings may have important implications for the use of mental health assessment and treatment as part of the decision-making about children in the care system.

Intratumoral T cell infiltration, MHC class I and STAT1 as biomarkers of good prognosis in colorectal cancer.

OBJECTIVE: To evaluate immunosurveillance/editing in colorectal cancer. DESIGN: Transformation stimulates the production of interferon gamma (IFNgamma) which signals via the IFNgamma receptor (IFNGR1) on tumours. This results in stimulation of nuclear STAT1 (nSTAT1), inhibition of tumour growth and upregulation of major histocompatibility complex (MHC) while promoting T cell extravasation. In contrast, downregulation of MHC class I by allele loss results in loss of T cell recognition. A tissue microarray of 462 colorectal tumours with mean follow-up of 42 months (range 1-116) was stained by immunohistochemistry for markers which predict immunosurveillance/editing. RESULTS: The presence of a high level of intratumoral T cells (ITTC) correlated with improved survival compared with a low level of ITTC, with a mean difference in survival of 16.3 months (p=0.006). There was a direct correlation between nSTAT1 expression and ITTC (p<0.001). Patients whose tumours had a high level of ITTC and nSTAT1 survived 20 months longer than those whose tumours had a low level of ITTC and no nSTAT1. A strong correlation was seen between ITTC and MHC class I expression (p=0.0002). A mean survival advantage of 26.1 months was seen in patients whose tumours had strong MHC I expression and high levels of ITTC over those who had weak MHC I and low levels of ITTC (log-rank test=12.023, p=0.034). Both MHC I and ITTC are independent predictors of good survival. CONCLUSIONS: ITTC, nSTAT1 and strong MHC class I expression on tumours identify patients with improved survival and an intact tumour immune system that may benefit from immunotherapy. Conversely, loss of these markers identifies patients whose tumours have escaped immunosurveillance and are unlikely to benefit from immunotherapy.