Remote detection of invasive alien species

The spread of invasive alien species (IAS) is recognized as the most severe threat to biodiversity outside of climate change and anthropogenic habitat destruction. IAS negatively impact ecosystems, local economies, and residents. They are especially problematic because once established, they give rise to positive feedbacks, increasing the likelihood of further invasions and spread. The integration of remote sensing (RS) to the study of invasion, in addition to contributing to our understanding of invasion processes and impacts to biodiversity, has enabled managers to monitor invasions and predict the spread of IAS, thus supporting biodiversity conservation and management action. This chapter focuses on RS capabilities to detect and monitor invasive plant species across terrestrial, riparian, aquatic, and human-modified ecosystems. All of these environments have unique species assemblages and their own optimal methodology for effective detection and mapping, which we discuss in detail

Effect of three types of horseshoes and unshod feet on selected non-podal forelimb kinematic variables measured by an extremity mounted inertial measurement unit sensor system in sound horses at the trot under conditions of treadmill and soft geotextile surface exercise.

Therapeutic farriery is part of the management of certain orthopaedic conditions. Non-podal parameters are important as most horses shod with therapeutic shoes are expected to perform again and the choice of shoe type may be influenced by the effects they may have on gait. The aim of this prospective study was to evaluate the effects of three different shoe designs and unshod front feet on forelimb non-podal kinematic variables using an extremity mounted inertial measurement unit (IMU) system under conditions of treadmill and overground exercise on a soft geotextile surface at the trot. Ten sound horses with no underlying orthopaedic problem were instrumented with eight IMUs at distal radii, tibia and third metacarpal/tarsal regions. Measurements were performed during four consecutive days. During the first three days, the three shoe types were randomly selected per horse and day. On the fourth day, all horses were tested unshod. Data were collected at the trot on a treadmill, and on a soft geotextile surface. Specifically designed software and a proprietary algorithm processed the accelerometer and gyroscope signals to obtain orientation and temporal data to describe selected kinematic variables predetermined by the system. Repeated-measures analysis of variance (ANOVA) was used to assess differences between shoe type and surface. The presence of shoes produced significant changes in spatiotemporal variables which seemed to be related to shoe mass rather than shoe design as there were no significant differences found between different shoe types. Shod horses showed a gait characterised by an increased range of motion (ROM) of the fore limbs. Previously reported effects of the investigated shoes on podal kinematics do not seem to affect the investigated kinematic variables indicating perhaps a compensatory effect occurring at some level in the extremity

Melanoma sentinel node biopsy and prediction models for relapse and overall survival

BACKGROUND:To optimise predictive models for sentinal node biopsy (SNB) positivity, relapse and survival, using clinico-pathological characteristics and osteopontin gene expression in primary melanomas.METHODS:A comparison of the clinico-pathological characteristics of SNB positive and negative cases was carried out in 561 melanoma patients. In 199 patients, gene expression in formalin-fixed primary tumours was studied using Illumina's DASL assay. A cross validation approach was used to test prognostic predictive models and receiver operating characteristic curves were produced.RESULTS:Independent predictors of SNB positivity were Breslow thickness, mitotic count and tumour site. Osteopontin expression best predicted SNB positivity (P=2.4 × 10??), remaining significant in multivariable analysis. Osteopontin expression, combined with thickness, mitotic count and site, gave the best area under the curve (AUC) to predict SNB positivity (72.6%). Independent predictors of relapse-free survival were SNB status, thickness, site, ulceration and vessel invasion, whereas only SNB status and thickness predicted overall survival. Using clinico-pathological features (thickness, mitotic count, ulceration, vessel invasion, site, age and sex) gave a better AUC to predict relapse (71.0%) and survival (70.0%) than SNB status alone (57.0, 55.0%). In patients with gene expression data, the SNB status combined with the clinico-pathological features produced the best prediction of relapse (72.7%) and survival (69.0%), which was not increased further with osteopontin expression (72.7, 68.0%).CONCLUSION:Use of these models should be tested in other data sets in order to improve predictive and prognostic data for patients