Moisture Detection in Tree Trunks in Semiarid Lands Using Low-Cost Non-Invasive Capacitive Sensors with Statistical Based Anomaly Detection Approach.

This paper focuses on building a non-invasive, low-cost sensor that can be fitted over tree trunks growing in a semiarid land environment. It also proposes a new definition that characterizes tree trunks' water retention capabilities mathematically. The designed sensor measures the variations in capacitance across its probes. It uses amplification and filter stages to smooth the readings, requires little power, and is operational over a 100 kHz frequency. The sensor sends data via a Long Range (LoRa) transceiver through a gateway to a processing unit. Field experiments showed that the system provides accurate readings of the moisture content. As the sensors are non-invasive, they can be fitted to branches and trunks of various sizes without altering the structure of the wood tissue. Results show that the moisture content in tree trunks increases exponentially with respect to the measured capacitance and reflects the distinct differences between different tree types. Data of known healthy trees and unhealthy trees and defective sensor readings have been collected and analysed statistically to show how anomalies in sensor reading baseds on eigenvectors and eigenvalues of the fitted curve coefficient matrix can be detected.

Wales Institute of Digital Information (WIDI): A Model of Collaborative Health Education, CPD, Research and Innovation.

The Wales Institute of Digital Information has developed a flexible model of education, CPD, research and innovation for the Welsh health and care sectors, in the digital arena. The co-produced model had produced significant benefits for both health employers and the Universities involved in the partnership. The model is continuing to develop collaborative educational provision from level 2 to level 8 and is concentrating on expanding its digital research and innovation offering to the health and care sector in a similar co-developed collaborative way.

Independent breakdown events of the Brassica napus Rlm7 resistance gene including via the off-target impact of a dual-specificity avirulence interaction.

Protection of many crops is achieved through the use of genetic resistance. Leptosphaeria maculans, the causal agent of blackleg disease of Brassica napus, has emerged as a model for understanding gene-for-gene interactions that occur between plants and pathogens. Whilst many of the characterized avirulence effector genes interact with a single resistance gene in the host, the AvrLm4-7 avirulence gene is recognized by two resistance genes, Rlm4 and Rlm7. Here, we report the "breakdown" of the Rlm7 resistance gene in Australia, under two different field conditions. The first, and more typical, breakdown probably resulted from widescale use of Rlm7-containing cultivars whereby selection has led to an increase of individuals in the L. maculans population that have undergone repeat-induced point (RIP) mutations at the AvrLm4-7 locus. This has rendered the AvrLm4-7 gene ineffective and therefore these isolates have become virulent towards both Rlm4 and Rlm7. The second, more atypical, situation was the widescale use of Rlm4 cultivars. Whilst a single-nucleotide polymorphism is the more common mechanism of virulence towards Rlm4, in this field situation, RIP mutations have been selected leading to the breakdown of resistance for both Rlm4 and Rlm7. This is an example of a resistance gene being rendered ineffective without having grown cultivars with the corresponding resistance gene due to the dual specificity of the avirulence gene. These findings highlight the value of pathogen surveillance in the context of expanded knowledge about potential complexities for Avr-R interactions for the deployment of appropriate resistance gene strategies.

Human Activity and Motion Pattern Recognition within Indoor Environment Using Convolutional Neural Networks Clustering and Naive Bayes Classification Algorithms.

Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path.

Using machine learning tools to investigate factors associated with trends in 'no-shows' in outpatient appointments.

Missed appointments are estimated to cost the UK National Health Service (NHS) approximately £1 billion annually. Research that leads to a fuller understanding of the types of factors influencing spatial and temporal patterns of these so-called "Did-Not-Attends" (DNAs) is therefore timely. This research articulates the results of a study that uses machine learning approaches to investigate whether these factors are consistent across a range of medical specialities. A predictive model was used to determine the risk-increasing and risk-mitigating factors associated with missing appointments, which were then used to assign a risk score to patients on an appointment-by-appointment basis for each speciality. Results show that the best predictors of DNAs include the patient's age, appointment history, and the deprivation rank of their area of residence. Findings have been analysed at both a geographical and medical speciality level, and the factors associated with DNAs have been shown to differ in terms of both importance and association. This research has demonstrated how machine learning techniques have real value in informing future intervention policies related to DNAs that can help reduce the burden on the NHS and improve patient care and well-being.

A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images.

BACKGROUND: Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images. METHOD: Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm. RESULTS: The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of '98.88%, 99.28% and 97.47%', '97.64%, 98.08% and 97.16%' and '95.00%, 100% and 90.00%' were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab. CONCLUSIONS: The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5-10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.

A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images.

BACKGROUND AND OBJECTIVE: Early diagnosis and classification of a cancer type can help facilitate the subsequent clinical management of the patient. Cervical cancer ranks as the fourth most prevalent cancer affecting women worldwide and its early detection provides the opportunity to help save life. To that end, automated diagnosis and classification of cervical cancer from pap-smear images has become a necessity as it enables accurate, reliable and timely analysis of the condition's progress. This paper presents an overview of the state of the art as articulated in prominent recent publications focusing on automated detection of cervical cancer from pap-smear images. METHODS: The survey reviews publications on applications of image analysis and machine learning in automated diagnosis and classification of cervical cancer from pap-smear images spanning 15 years. The survey reviews 30 journal papers obtained electronically through four scientific databases (Google Scholar, Scopus, IEEE and Science Direct) searched using three sets of keywords: (1) segmentation, classification, cervical cancer; (2) medical imaging, machine learning, pap-smear; (3) automated system, classification, pap-smear. RESULTS: Most of the existing algorithms facilitate an accuracy of nearly 93.78% on an open pap-smear data set, segmented using CHAMP digital image software. K-nearest-neighbors and support vector machines algorithms have been reported to be excellent classifiers for cervical images with accuracies of over 99.27% and 98.5% respectively when applied to a 2-class classification problem (normal or abnormal). CONCLUSION: The reviewed papers indicate that there are still weaknesses in the available techniques that result in low accuracy of classification in some classes of cells. Moreover, most of the existing algorithms work either on single or on multiple cervical smear images. This accuracy can be increased by varying various parameters such as the features to be extracted, improvement in noise removal, using hybrid segmentation and classification techniques such of multi-level classifiers. Combining K-nearest-neighbors algorithm with other algorithm(s) such as support vector machines, pixel level classifications and including statistical shape models can also improve performance. Further, most of the developed classifiers are tested on accurately segmented images using commercially available software such as CHAMP software. There is thus a deficit of evidence that these algorithms will work in clinical settings found in developing countries (where 85% of cervical cancer incidences occur) that lack sufficient trained cytologists and the funds to buy the commercial segmentation software.

Relations between violence, calendar events and ambient conditions.

National assault injury surveillance has identified major seasonal variation, but it is not clear whether assault injury is a seasonal problem in large cities. Relationships between community violence, calendar events and ambient conditions were investigated with reference to prospective, Accident and Emergency (A&E) derived information obtained from people injured in assaults in Cardiff between 1 May 1995 and 30 April 2000. Records of daily local ambient conditions included data relating to temperature, rainfall and sunshine hours and data of major local sporting events and annual holidays were studied. Pearson correlation coefficients were used to evaluate associations between variables. Overall, 19,264 assault-related A&E attendances were identified over the 5-year period. Almost three-quarters were males. Violence was clustered predominantly on Saturdays and Sundays, New Year and rugby international days. Temperature, rainfall and sunlight hours did not correlate significantly with violence (P > 0.05). The findings indicate that injury reduction effort should be intensified at the known risk times for violence and that in a capital city/regional centre violence cannot be predicted on the basis of ambient conditions.