Xenome--a tool for classifying reads from xenograft samples

Motivation Shotgun sequence read data derived from xenograft material contains a mixture of reads arising from the host and reads arising from the graft. Classifying the read mixture to separate the two allows for more precise analysis to be performed. Results We present a technique, with an associated tool Xenome, which performs fast, accurate and specific classification of xenograft-derived sequence read data. We have evaluated it on RNA-Seq data from human, mouse and human-in-mouse xenograft datasets.

Supporting residents’ expression of sexuality : the initial construction of a sexuality assessment tool for residential aged care facilities

Background: Sexuality is a key component of quality of life and well-being and a need to express one’s sexuality continues into old age. Staff and families in residential aged care facilities often find expressions of sexuality by residents, particularly those living with dementia, challenging and facilities often struggle to address individuals’ needs in this area. This paper describes the development of an assessment tool which enables residential aged care facilities to identify how supportive their organisation is of all residents’ expression of their sexuality, and thereby improve where required. Methods: Multi-phase design using qualitative methods and a Delphi technique. Tool items were derived from the literature and verified by qualitative interviews with aged care facility staff, residents and families. The final item pool was confirmed via a reactive Delphi process. Results: A final item pool of sixty-nine items grouped into seven key areas allows facilities to score their compliance with the areas identified as being supportive of older people’s expression of their sexuality in a residential aged care environment. Conclusions: The sexuality assessment tool (SexAT) guides practice to support the normalization of sexuality in aged care homes and assists facilities to identify where enhancements to the environment, policies, procedures and practices, information and education/training are required. The tool also enables facilities to monitor initiatives in these areas over time.

Human saliva as a tool to investigate intimate partner violence

Human saliva mirrors body’s health and well-being and many of the biomolecules present in blood or urine can also be found in salivary secretions. However, biomolecular concentrations in saliva are usually one tenth to one thousandth of the levels in blood (Pfaffe et al., 2011). Sensitive detection technology platforms are therefore required to detect biomolecules in saliva. Another road block to the advancement of salivary diagnostics is the lack of information related to healthy state saliva vs. a diseased saliva, baseline levels and reference ranges and diurnal variations. Saliva has numerous advantages over blood or urine as a diagnostic fluid: (a) the non-invasive nature of sample collection and the simple, safe, painless and cost-effective methods to collect it; (b) unskilled personnel can collect saliva samples at multiple time points; and (c) the total protein concentration is approximately a quarter of that is present in plasma, which makes it easier to investigate low abundance proteins (Pfaffe et al., 2011). Currently, saliva assays are routinely used to determine, diseases such as HIV, drugs and substances of abuse to provide information on exposure and give qualitative information on the type of illicit drug used (Kintz et al., 2009), cortisol levels for diagnosing Cushing’s syndrome (Doi et al., 2008), and use for biomonitoring of exposure to chemicals (Caporossi et al., 2010) to measure hormones (Gröschl, 2009)....

Near infrared spectroscopy for rapid determination of Mankin score components : a potential tool for quantitative characterization of articular cartilage at surgery

PURPOSE The purpose of this study was to demonstrate the potential of near infrared (NIR) spectroscopy for characterizing the health and degenerative state of articular cartilage based on the components of the Mankin score. METHODS Three models of osteoarthritic degeneration induced in laboratory rats by anterior cruciate ligament (ACL) transection, meniscectomy (MSX), and intra-articular injection of monoiodoacetate (1 mg) (MIA) were used in this study. Degeneration was induced in the right knee joint; each model group consisted of 12 rats (N = 36). After 8 weeks, the animals were euthanized and knee joints were collected. A custom-made diffuse reflectance NIR probe of 5-mm diameter was placed on the tibial and femoral surfaces, and spectral data were acquired from each specimen in the wave number range of 4,000 to 12,500 cm(-1). After spectral data acquisition, the specimens were fixed and safranin O staining (SOS) was performed to assess disease severity based on the Mankin scoring system. Using multivariate statistical analysis, with spectral preprocessing and wavelength selection technique, the spectral data were then correlated to the structural integrity (SI), cellularity (CEL), and matrix staining (SOS) components of the Mankin score for all the samples tested. RESULTS ACL models showed mild cartilage degeneration, MSX models had moderate degeneration, and MIA models showed severe cartilage degenerative changes both morphologically and histologically. Our results reveal significant linear correlations between the NIR absorption spectra and SI (R(2) = 94.78%), CEL (R(2) = 88.03%), and SOS (R(2) = 96.39%) parameters of all samples in the models. In addition, clustering of the samples according to their level of degeneration, with respect to the Mankin components, was also observed. CONCLUSIONS NIR spectroscopic probing of articular cartilage can potentially provide critical information about the health of articular cartilage matrix in early and advanced stages of osteoarthritis (OA). CLINICAL RELEVANCE This rapid nondestructive method can facilitate clinical appraisal of articular cartilage integrity during arthroscopic surgery.

Predicting the likelihood of non-healing : a venous leg ulcer risk assessment tool

This project developed, validated and tested reliability of a risk assessment tool to predict the risk of failure to heal of patients with venous leg ulcers within 24 weeks. The risk assessment tool will allow clinicians to be able to determine realistic outcomes for their patients, promote early healing and potentially avoid weeks of inappropriate therapy. The tool will also assist in addressing specific risk factors and guide decisions on early, alternative, tailored interventions.

Wood density : a tool to find complementary species for the design of mixed species plantations

The successful establishment and growth of mixed-species forest plantations requires that complementary or facilitatory species be identified. This can be difficult in many tropical areas because the growth characteristics of endemic species are often unknown, particularly when grown at potentially higher densities in plantations than in natural forests. Here, we investigate whether wood density is a useful and readily accessible trait for choosing complementary species for mixed species plantations. Wood density represents the carbon investment per unit volume of stem with a trade-off generally found between fast (low wood density) and slow (high wood density) growing species. To do this, we use data collected from 18 highly diverse mixed species plantations (4–23 mostly native species) aged from 6 to 11 years at the time of data collection located on Leyte Island, Philippines. We found significant negative correlations between wood densities and the height of the most abundant species, as well as with measures of overall stand growth and tree diameter size distribution. Not only do species with denser woods have slower growth rates, but also mixed-species plantations with higher average wood density and higher stem density were also less productive, at least in these young plantations. Similarly, stands with a high diversity in wood densities were less productive. There is growing interest in making greater use of native multi-species mixtures in smallholder and community planting programs in the tropics, and our results show databases of wood density values may help improve their design. In the early development stages of plantations, canopy closure and rapid height growth are usually key silvicultural targets, and wood density values can predict the rapid height development of species. If plantations are being grown for the livelihood of small landholders then the best target is to choose some species with different wood densities. This allows an early harvest of low-wood density species for early income, and will also reduce competition for slower growing trees with higher wood densities for later income generation.

A simple nutrition screening tool for paediatric inpatients

Background: Pediatric nutrition risk screening tools are not routinely implemented throughout many hospitals, despite prevalence studies demonstrating malnutrition is common in hospitalized children. Existing tools lack the simplicity of those used to assess nutrition risk in the adult population. This study reports the accuracy of a new, quick, and simple pediatric nutrition screening tool (PNST) designed to be used for pediatric inpatients. Materials and Methods: The pediatric Subjective Global Nutrition Assessment (SGNA) and anthropometric measures were used to develop and assess the validity of 4 simple nutrition screening questions comprising the PNST. Participants were pediatric inpatients in 2 tertiary pediatric hospitals and 1 regional hospital. Results: Two affirmative answers to the PNST questions were found to maximize the specificity and sensitivity to the pediatric SGNA and body mass index (BMI) z scores for malnutrition in 295 patients. The PNST identified 37.6% of patients as being at nutrition risk, whereas the pediatric SGNA identified 34.2%. The sensitivity and specificity of the PNST compared with the pediatric SGNA were 77.8% and 82.1%, respectively. The sensitivity of the PNST at detecting patients with a BMI z score of less than -2 was 89.3%, and the specificity was 66.2%. Both the PNST and pediatric SGNA were relatively poor at detecting patients who were stunted or overweight, with the sensitivity and specificity being less than 69%. Conclusion: The PNST provides a sensitive, valid, and simpler alternative to existing pediatric nutrition screening tools such as Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP), Screening Tool Risk on Nutritional status and Growth (STRONGkids), and Paediatric Yorkhill Malnutrition Score (PYMS) to ensure the early detection of hospitalized children at nutrition risk.

Machine learning for activity recognition : hip versus wrist data

Problem addressed Wrist-worn accelerometers are associated with greater compliance. However, validated algorithms for predicting activity type from wrist-worn accelerometer data are lacking. This study compared the activity recognition rates of an activity classifier trained on acceleration signal collected on the wrist and hip. Methodology 52 children and adolescents (mean age 13.7 +/- 3.1 year) completed 12 activity trials that were categorized into 7 activity classes: lying down, sitting, standing, walking, running, basketball, and dancing. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the right hip and the non-dominant wrist. Features were extracted from 10-s windows and inputted into a regularized logistic regression model using R (Glmnet + L1). Results Classification accuracy for the hip and wrist was 91.0% +/- 3.1% and 88.4% +/- 3.0%, respectively. The hip model exhibited excellent classification accuracy for sitting (91.3%), standing (95.8%), walking (95.8%), and running (96.8%); acceptable classification accuracy for lying down (88.3%) and basketball (81.9%); and modest accuracy for dance (64.1%). The wrist model exhibited excellent classification accuracy for sitting (93.0%), standing (91.7%), and walking (95.8%); acceptable classification accuracy for basketball (86.0%); and modest accuracy for running (78.8%), lying down (74.6%) and dance (69.4%). Potential Impact Both the hip and wrist algorithms achieved acceptable classification accuracy, allowing researchers to use either placement for activity recognition.

Prediction of activity type in preschool children using machine learning techniques

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children.

The Catlin Seaview Survey – kilometre-scale seascape assessment, and monitoring of coral reef ecosystems

1.Marine ecosystems provide critically important goods and services to society, and hence their accelerated degradation underpins an urgent need to take rapid, ambitious and informed decisions regarding their conservation and management. 2.The capacity, however, to generate the detailed field data required to inform conservation planning at appropriate scales is limited by time and resource consuming methods for collecting and analysing field data at the large scales required. 3.The ‘Catlin Seaview Survey’, described here, introduces a novel framework for large-scale monitoring of coral reefs using high-definition underwater imagery collected using customized underwater vehicles in combination with computer vision and machine learning. This enables quantitative and geo-referenced outputs of coral reef features such as habitat types, benthic composition, and structural complexity (rugosity) to be generated across multiple kilometre-scale transects with a spatial resolution ranging from 2 to 6 m2. 4.The novel application of technology described here has enormous potential to contribute to our understanding of coral reefs and associated impacts by underpinning management decisions with kilometre-scale measurements of reef health. 5.Imagery datasets from an initial survey of 500 km of seascape are freely available through an online tool called the Catlin Global Reef Record. Outputs from the image analysis using the technologies described here will be updated on the online repository as work progresses on each dataset. 6.Case studies illustrate the utility of outputs as well as their potential to link to information from remote sensing. The potential implications of the innovative technologies on marine resource management and conservation are also discussed, along with the accuracy and efficiency of the methodologies deployed.

Large scale multiuser digital mind mapping tool : a study in usability

Project work can involve multiple people from varying disciplines coming together to solve problems as a group. Large scale interactive displays are presenting new opportunities to support such interactions with interactive and semantically enabled cooperative work tools such as intelligent mind maps. In this paper, we present a novel digital, touch-enabled mind-mapping tool as a first step towards achieving such a vision. This first prototype allows an evaluation of the benefits of a digital environment for a task that would otherwise be performed on paper or flat interactive surfaces. Observations and surveys of 12 participants in 3 groups allowed the formulation of several recommendations for further research into: new methods for capturing text input on touch screens; inclusion of complex structures; multi-user environments and how users make the shift from single- user applications; and how best to navigate large screen real estate in a touch-enabled, co-present multi-user setting.

A continental-scale tool for acoustic identification of European bats

Summary 1. Acoustic methods are used increasingly to survey and monitor bat populations. However, the use of acoustic methods at continental scales can be hampered by the lack of standardized and objective methods to identify all species recorded. This makes comparable continent-wide monitoring difficult, impeding progress towards developing biodiversity indicators, transboundary conservation programmes and monitoring species distribution changes. 2. Here we developed a continental-scale classifier for acoustic identification of bats, which can be used throughout Europe to ensure objective, consistent and comparable species identifications. We selected 1350 full-spectrum reference calls from a set of 15 858 calls of 34 European species, from EchoBank, a global echolocation call library. We assessed 24 call parameters to evaluate how well they distinguish between species and used the 12 most useful to train a hierarchy of ensembles of artificial neural networks to distinguish the echolocation calls of these bat species. 3. Calls are first classified to one of five call-type groups, with a median accuracy of 97·6%. The median species-level classification accuracy is 83·7%, providing robust classification for most European species, and an estimate of classification error for each species. 4. These classifiers were packaged into an online tool, iBatsID, which is freely available, enabling anyone to classify European calls in an objective and consistent way, allowing standardized acoustic identification across the continent. 5. Synthesis and applications. iBatsID is the first freely available and easily accessible continental- scale bat call classifier, providing the basis for standardized, continental acoustic bat monitoring in Europe. This method can provide key information to managers and conservation planners on distribution changes and changes in bat species activity through time.

Human vs. machine : identification of bat species from their echolocation calls by humans and by artificial neural networks

Automated remote ultrasound detectors allow large amounts of data on bat presence and activity to be collected. Processing of such data involves identifying bat species from their echolocation calls. Automated species identification has the potential to provide more consistent, predictable, and potentially higher levels of accuracy than identification by humans. In contrast, identification by humans permits flexibility and intelligence in identification, as well as the incorporation of features and patterns that may be difficult to quantify. We compared humans with artificial neural networks (ANNs) in their ability to classify short recordings of bat echolocation calls of variable signal to noise ratios; these sequences are typical of those obtained from remote automated recording systems that are often used in large-scale ecological studies. We presented 45 recordings (1–4 calls) produced by known species of bats to ANNs and to 26 human participants with 1 month to 23 years of experience in acoustic identification of bats. Humans correctly classified 86% of recordings to genus and 56% to species; ANNs correctly identified 92% and 62%, respectively. There was no significant difference between the performance of ANNs and that of humans, but ANNs performed better than about 75% of humans. There was little relationship between the experience of the human participants and their classification rate. However, humans with <1 year of experience performed worse than others. Currently, identification of bat echolocation calls by humans is suitable for ecological research, after careful consideration of biases. However, improvements to ANNs and the data that they are trained on may in future increase their performance to beyond those demonstrated by humans.