An experience in modelling business process architecture

We present a mapping of a previously designed Business Process Architecture (BPA) meta-model onto ArchiMate, i.e., the de facto standard Enterprise Architecture (EA) modelling language. This construct mapping allows developing process maps, i.e., descriptions of (views of) the business process architecture of an organization. We demonstrate the development of these process maps using the Signavio Business Process Management (BPM) modelling platform. The developed process maps are part of the organization’s enterprise architecture model and are linked to BPMN process diagrams that detail the functional, control-flow, data and resource aspects of the business processes included in the process map. Our research contributes to the integration of BPM and EA by researching BPA as a concept common to both disciplines

Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks

Advanced data analysis tools are crucial for the application of ToF-SIMS analysis to biological samples. Here, we demonstrate that by using a training set approach principal components analysis (PCA) can be performed on large 3D ToF-SIMS images of neuronal cell cultures. The method readily provides access to sample component information and significantly improves the images’ signal-to-noise ratio (SNR)

A Phase 2 Study to Assess the Immunomodulatory Capacity of a Lecithin-based Delivery System of Curcumin in Endometrial Cancer

Curcumin is a botanical with anti-tumor and immunomodulatory properties. We hypothesized that curcumin supplementation might influence inflammatory biomarker levels in endometrial carcinoma (EC). In this open-label, non-randomized phase 2 study (NCT02017353), seven EC patients consumed 2 g/day Curcumin Phytosome (CP) orally for 2 weeks. Blood was taken at baseline, days 1, 7, 14, and 21. The following analytes were measured: curcuminoids and metabolites, 56 inflammatory biomarkers, COX-2, frequencies of myeloid-derived suppressor cells, dendritic cells and NK cells, expression of MHC molecules on leukocytes and monocytes and activation/memory status of T cells. Patients completed quality of life (QoL) questionnaires at baseline and end of treatment. Curcumin metabolites were detectable in plasma upon CP intake. CP downregulated MHC expression levels on leukocytes (P = 0.0313), the frequency of monocytes (P = 0.0114) and ICOS expression by CD8+ T cells (P = 0.0002). However, CP upregulated CD69 levels on CD16− NK cells (P = 0.0313). No differences were observed regarding inflammatory biomarkers, frequencies of other immune cell types, T cell activation and COX-2 expression. A non-significant trend to improved QoL was observed. Overall, CP-induced immunomodulatory effects in EC were modest without significant QoL changes. Given the small population and the observed variability in inter-patient biomarker levels, more research is necessary to explore whether benefits of CP can be obtained in EC by different supplementation regimens.Clinical Trial Registration:www.ClinicalTrials.gov, identifier NCT02017353; www.clinicaltrialsregister.eu, identifier 2013-001737-40

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Cow gait scores and kinematic gait data : can people see gait irregularities?

Increasing lameness problems associated with intensified dairy cattle production has lead to the development of several techniques to automatically detect these problems. Comparisons of these new measuring techniques of cow locomotion with the conventional subjective observer scoring are scarce. In order to better understand human observers' gait scoring, cows walking on a pressure-sensitive mat were evaluated for kinematic gait variables and a visual assessment of gait was also made via video recording. Forty of these videos were used for subjective gait scoring on a 3-point scale, and the observers were also asked to report any observed abnormalities (lameness indicators) that had influenced their scoring. Relationships between reported lameness indicators and subjective gait scores, between subjective gait scores and measured kinematic variables of cow locomotion and between reported lameness indicators and measured kinematic variables of cow locomotion were investigated. In general, observers based their gait score on reported indicators such as 'tenderness', 'arched back', 'irregular gait' and 'increased abduction'. All of these four reported lameness indicators were correlated with measured kinematic 'variables of asymmetry', 'stance time' or both, suggesting that human observers are capable of detecting changes within these lameness indicators as measured by the pressure-sensitive mat. 'Increased abduction' appeared harder to detect and was reported more frequently by observers already experienced with gait scoring. Also, the measured kinematic variables of 'stance time' and 'measures of asymmetry between left and right limbs' as measured by the pressure-sensitive mat, show potential in predicting the gait score given. These reported lameness indicators and measured kinematic variables-mutually correlated and both related to the gait scores-were considered promising for subjective gait scoring in general

Reliability of categorical versus continuous scoring of welfare indicators: lameness in cows as a case study

Many animal welfare traits vary on a continuous scale but are commonly scored using an ordinal scale with few categories. The rationale behind this practice is rarely stated but appears largely based on the debatable conviction that it increases data reliability. Using 54 observers of varying levels of expertise, inter-observer reliability (IOR) and user-satisfaction were compared between a 3-point ordinal scale (OS) and a continuous modified visual analogue scale with multiple anchors (VAS) for scoring lameness in dairy cattle from video. IOR was significantly better for the VAS than for the OS. IOR increased with self-reported level of expertise for the VAS, whereas for the OS it was highest for observers with a moderate level of expertise. The mean continuous scores and the mean categorical scores were highly correlated. Three times as many observers stated a preference for the VAS (n = 2 7) compared to the OS (n = 9) in investigating differences in lameness between herds. Contrary to common perception, these results illustrate that it is possible for a continuous cattle lameness score to be more reliable and to have greater user acceptability than a simple categorical scale. As continuous scales are also potentially more sensitive, and produce data more amenable to algebraic processing and more powerful parametric analyses, the scepticism against their application for assessing animal welfare traits should be reconsidered