Cluster analyses of the TCGA and a TMA dataset using the coexpression of HSP27 and CRYAB improves alignment with clinical-pathological parameters of breast cancer and suggests different epichaperome influences for each sHSP.

Our cluster analysis of the Cancer Genome Atlas for co-expression of HSP27 and CRYAB in breast cancer patients identified three patient groups based on their expression level combination (high HSP27 + low CRYAB; low HSP27 + high CRYAB; similar HSP27 + CRYAB). Our analyses also suggest that there is a statistically significant inverse relationship between HSP27 and CRYAB and known clinicopathological markers in breast cancer. Screening an unbiased 248 breast cancer patient tissue microarray (TMA) for the protein expression of HSP27 and phosphorylated HSP27 (HSP27-82pS) with CRYAB also identified three patient groups based on HSP27 and CRYAB expression levels. TMA24 also had recorded clinical-pathological parameters, such as ER and PR receptor status, patient survival, and TP53 mutation status. High HSP27 protein levels were significant with ER and PR expression. HSP27-82pS associated with the best patient survival (Log Rank test). High CRYAB expression in combination with wild-type TP53 was significant for patient survival, but a different patient outcome was observed when mutant TP53 was combined with high CRYAB expression. Our data suggest that HSP27 and CRYAB have different epichaperome influences in breast cancer, but more importantly evidence the value of a cluster analysis that considers their coexpression. Our approach can deliver convergence for archival datasets as well as those from recent treatment and patient cohorts and can align HSP27 and CRYAB expression to important clinical-pathological features of breast cancer.

Pan-cancer analysis reveals TAp63-regulated oncogenic lncRNAs that promote cancer progression through AKT activation.

The most frequent genetic alterations across multiple human cancers are mutations in TP53 and the activation of the PI3K/AKT pathway, two events crucial for cancer progression. Mutations in TP53 lead to the inhibition of the tumour and metastasis suppressor TAp63, a p53 family member. By performing a mouse-human cross species analysis between the TAp63 metastatic mammary adenocarcinoma mouse model and models of human breast cancer progression, we identified two TAp63-regulated oncogenic lncRNAs, TROLL-2 and TROLL-3. Further, using a pan-cancer analysis of human cancers and multiple mouse models of tumour progression, we revealed that these two lncRNAs induce the activation of AKT to promote cancer progression by regulating the nuclear to cytoplasmic translocation of their effector, WDR26, via the shuttling protein NOLC1. Our data provide preclinical rationale for the implementation of these lncRNAs and WDR26 as therapeutic targets for the treatment of human tumours dependent upon mutant TP53 and/or the PI3K/AKT pathway.

Are Liaison and Diversion interventions in policing delivering the planned impact: A longitudinal evaluation in two constabularies?

Liaison and Diversion (L&D) has twin objectives: improving mental health outcomes and reducing re-offending. Early diversion from police custody seems promising, but evidence of benefit is required to sustain such programmes. To test the hypothesis that contact with L&D services while in police custody would lead to improved mental health outcomes and a reduction in type and level of offending, we used a pre-post service use design. National Health Service (NHS) records in two counties were searched for evidence that patients had been involved with L&D services while in police custody during the period July 2009-December 2017. We defined January 2009-July 2014 as the pre-intervention period and any time after contact as the post-intervention period. Data from the Police National Computer were gathered for each period for these individuals, to assess their pre-post L&D contact offending histories. NHS Trust data were similarly gathered to assess their pre-post use of mental health legislation. 4,462 individuals were identified who had used L&D services in police custody. There were statistically significant reductions in the amount of offending following contact with the L&D service (whether one or two contacts), regardless of offence type. Statistically significant reductions were also observed in use of the four most commonly used legislative powers for detaining patients in hospital on mental disorder grounds, regardless of offending status (prolific/non-prolific). Our results indicate positive associations between the L&D interventions and change in offending and use of compulsory hospital detention. Whilst our research does not allow a direct causal relationship to be established in either area, the findings go beyond other impact assessments of L&D which have either been with small samples or relied only on qualitative data or expert opinion.

Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep.

Lameness in sheep is the biggest cause of concern regarding poor health and welfare among sheep-producing countries. Best practice for lameness relies on rapid treatment, yet there are no objective measures of lameness detection. Accelerometers and gyroscopes have been widely used in human activity studies and their use is becoming increasingly common in livestock. In this study, we used 23 datasets (10 non-lame and 13 lame sheep) from an accelerometer- and gyroscope-based ear sensor with a sampling frequency of 16 Hz to develop and compare algorithms that can differentiate lameness within three different activities (walking, standing and lying). We show for the first time that features extracted from accelerometer and gyroscope signals can differentiate between lame and non-lame sheep while standing, walking and lying. The random forest algorithm performed best for classifying lameness with an accuracy of 84.91% within lying, 81.15% within standing and 76.83% within walking and overall correctly classified over 80% sheep within activities. Both accelerometer- and gyroscope-based features ranked among the top 10 features for classification. Our results suggest that novel behavioural differences between lame and non-lame sheep across all three activities could be used to develop an automated system for lameness detection.

Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep.

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

Police interactions and interventions with suspects flagged as experiencing mental health problems.

BACKGROUND: Interactions between individuals experiencing mental health (MH) problems and the police are complex, and effectiveness of innovative support and diversion models in England and Wales not yet fully evaluated. AIMS/HYPOTHESES: Our aims were to examine police interactions with suspects and to measure the immediate effectiveness of police/NHS MH interventions, including liaison and diversion and embedded staff in police contact and control rooms. We hypothesised that those with an MH flag would have significantly greater benefit from such interventions than those without in terms of how far they are taken down the criminal justice pathway and how long they spend in police custody. METHODS: We examined police interactions with suspects with and without flagged MH problems in relation to key outcome measures over a 15-month period, overall or when flagged by nurses alone. 'MH flagging' is defined as the presence of a marker on police systems, including both historical and current information, that alerts control room staff and response officers that the call may involve an individual/s with MH problems. Serial cross-sectional analysis of material from a database of individual cases integrating information from three police sources (N = 13,472) was used to test for associations between 'mental health flagging' and outcomes. RESULTS: Individuals with an MH flag have almost identical police dispatch response profiles to those without; they were arrested for and charged with similar offences. Those with an MH flag were significantly more likely to be charged with a criminal offence, less likely to receive a caution and spent longer periods in police custody than people under similar accusations but no MH flag. CONCLUSIONS: MH flagging appeared to disadvantage the people flagged, despite the presence of theoretically appropriate interventions. Further research is needed to understand this. It may be that indicating this form of vulnerability if the person is not judged to qualify for a MH service is discriminatory and may even account for excessive rates of mental disorder among prisoners.

Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour.

Automated behavioural classification and identification through sensors has the potential to improve health and welfare of the animals. Position of a sensor, sampling frequency and window size of segmented signal data has a major impact on classification accuracy in activity recognition and energy needs for the sensor, yet, there are no studies in precision livestock farming that have evaluated the effect of all these factors simultaneously. The aim of this study was to evaluate the effects of position (ear and collar), sampling frequency (8, 16 and 32 Hz) of a triaxial accelerometer and gyroscope sensor and window size (3, 5 and 7 s) on the classification of important behaviours in sheep such as lying, standing and walking. Behaviours were classified using a random forest approach with 44 feature characteristics. The best performance for walking, standing and lying classification in sheep (accuracy 95%, F-score 91%-97%) was obtained using combination of 32 Hz, 7 s and 32 Hz, 5 s for both ear and collar sensors, although, results obtained with 16 Hz and 7 s window were comparable with accuracy of 91%-93% and F-score 88%-95%. Energy efficiency was best at a 7 s window. This suggests that sampling at 16 Hz with 7 s window will offer benefits in a real-time behavioural monitoring system for sheep due to reduced energy needs.