Reimagining the General Health Questionnaire as a measure of emotional wellbeing: A study of postpartum women in Malta

Background: Postpartum health has been subject to a focus on psychological morbidity, despite positive associations between postpartum recovery and maternal emotional wellbeing. There are currently many validated tools to measure wellbeing and related concepts, including non-psychiatric morbidity. The General Health Questionnaire, 12 items (GHQ-12) is one such instrument, widely used and validated in several languages. Its use in postpartum settings has been documented with disagreement about the instrument's utility in this population, particularly in relation to scoring method and threshold. The GHQ-12 has never been translated into Maltese. This study explored the psychometric properties of the GHQ-12 in a Maltese postpartum population to consider if the use of a different scoring method (visual analogue scale) in the GHQ-12 can determine postpartum wellbeing. Methods: One hundred and twenty-four postpartum women recruited from one hospital in Malta completed the translated and adapted GHQ-12 as a wellbeing measure (GHQ-12(WB)) at four postpartum time points. The psychometric properties of the GHQ-12(WB) were explored using confirmatory factor analysis, discriminant and divergent validity and reliability analysis. Results: The GHQ-12(WB) demonstrated good divergent and known-groups validity and internal consistency. No models offered a good fit to the data. The overall consistent best-fit to the data was an eight item, two factor model (GHQ-8). Model fit improved across all models in terms of CFI at 13 weeks. Conclusion: Findings generally support the reliability and validity of the Maltese version of the GHQ-12(WB). Model fit changes over time reflect the dynamic nature of postpartum recovery. Further evaluation of the GHQ-8(WB) is recommended. © 2013 Australian College of Midwives

How linear features alter predator movement and the functional\ud response

In areas of oil and gas exploration, seismic lines have been reported to alter the movement patterns of wolves (Canis lupus). We developed a mechanistic first passage time model, based on an anisotropic elliptic partial differential equation, and used this to explore how wolf movement responses to seismic lines influence the encounter rate of the wolves with their prey. The model was parametrized using 5 min GPS location data. These data showed that wolves travelled faster on seismic lines and had a higher probability of staying on a seismic line once they were on it. We simulated wolf movement on a range of seismic line densities and drew implications for the rate of predator–prey interactions as described by the functional response. The functional response exhibited a more than linear increase with respect to prey density (type III) as well as interactions with seismic line density. Encounter rates were significantly higher in landscapes with high seismic line density and were most pronounced at low prey densities. This suggests that prey at low population densities are at higher risk in environments with a high seismic line density unless they learn to avoid them

Longitudinal MRI assessment: the identification of relevant features in the development of posterior fossa syndrome in children

Up to 25% of children who undergo brain tumour resection surgery in the posterior fossa develop posterior fossa syndrome (PFS). This syndrome is characterised by mutism and disturbance in speech. Our hypothesis is that there is a correlation between PFS and the occurrence of hypertrophic olivary degeneration (HOD) in lobes within the posterior fossa, known as the inferior olivary nuclei (ION). HOD is exhibited as an increase in size and intensity of the ION on an MR image. Intra-operative MRI (IoMRI) is used during surgical procedures at the Alder Hey Children’s Hospital, Liverpool, England, in the treatment of Posterior Fossa tumours and allows visualisation of the brain during surgery. The final MR scan on the IoMRI allows early assessment of the ION immediately after the surgical procedure. The longitudinal MRI data of 28 patients was analysed in a collaborative study with Alder Hey Children’s Hospital, in order to identify the most relevant imaging features that relate to the development of PFS, specifically related to HOD. A semi-automated segmentation process was carried out to delineate the ION on each MRI. Feature selection techniques were used to identify the most relevant features amongst the MRI data, demographics and clinical data provided by the hospital. A support vector machine (SVM) was used to analyse the discriminative ability of the selected features. The results indicate the presence of HOD as the most efficient feature that correlates with the development of PFS, followed by the change in intensity and size of the ION and whether HOD occurred bilaterally or unilaterally

A New Attraction Model For Evaluating The Effectiveness Of Selling Effort

Deployment analysis evaluates the allocation of selling effort directed towards achieving the firm’s sales objectives. While recent improvements in salesforce automation have resulted in more optimal levels of sales call effort, managerial judgments are still crucial to effectively deploy a salesforce. Moreover, the availability of large databases of target profiles, especially in consumer markets and some business markets like pharmaceuticals, has led to better targeting of accounts. Nevertheless, a robust model is still needed to post-hoc analyze the effectiveness of all these efforts. An analytical framework, based on the user-friendly Portfolio Model, has been found to provide more accurate diagnostic insights when the ideal “perfect-knowledge” benchmark is used as a comparison. The difference in this approach, which this paper calls the Attraction Model, is in the treatment of the variables. They have been operationalized to take advantage of “perfect-knowledge” in market shares, growth rates and account usage patterns obtained from a large random sample. In this way the frequency of sales calls, as a measure of the sales deployment efforts, can be scrutinized and the level of success evaluated when compared to the ideal effort.  Strategic corrections can then be made for the next promotional period

Relationship between pre-season testing performance and playing time among NCAA DII basketball players

Purpose: The purpose of this study was to investigate the relationships between pre-season testing performance and playing time within a men’s Division II basketball team. Methods: Archival data from pre-season athletic performance testing for ten (n=10) male NCAA Division II basketball players was collected and analyzed to determine if there was a relationship between anthropometric data (height, weight, wingspan), physical performance tests (vertical jump height, lane agility test, 5 and 20 m sprint time, National Basketball League (NBA) line drill and 20 m multi-stage fitness test (MSFT)), and playing time in the subsequent collegiate season. Results: Pearson’s product moment correlations revealed significant correlations were observed between playing time and predicted 1-RM bench press (r≥0.71) and 1-RM back squat (r≥0.74). Conclusion: These results reveal the importance of upper and lower body strength to determine playing time for Division II basketball players. Based on these results, coaches should emphasize the importance of resistance training to develop upper and lower body strength to increase playing time in Division II collegiate athletes

Identifying quantitative imaging features of posterior fossa syndrome in longitudinal MRI

Up to 25% of children who undergo brain tumor resection surgery in the posterior fossa develop posterior fossa syndrome (PFS). This syndrome is characterized by mutism and disturbance in speech. Our hypothesis is that there is a correlation between PFS and the occurrence of hypertrophic olivary degeneration (HOD) in structures within the posterior fossa, known as the inferior olivary nuclei (ION). HOD is exhibited as an increase in size and intensity of the ION on an MR image. Longitudinal MRI datasets of 28 patients were acquired consisting of pre-, intra-, and postoperative scans. A semiautomated segmentation process was used to segment the ION on each MR image. A full set of imaging features describing the first- and second-order statistics and size of the ION were extracted for each image. Feature selection techniques were used to identify the most relevant features among the MRI features, demographics, and data based on neuroradiological assessment. A support vector machine was used to analyze the discriminative features selected by a generative k-nearest neighbor algorithm. The results indicate the presence of hyperintensity in the left ION as the most diagnostically relevant feature, providing a statistically significant improvement in the classification of patients (p=0.01) when using this feature alone

Culture of airway epithelial cells from neonates sampled within 48-hours of birth

Peer reviewedPublisher PD

Comparison between PI and PR Current Controllers in Grid Connected PV Inverters

This paper presents a comparison between Proportional Integral (PI) and Proportional Resonant (PR) current controllers used in Grid Connected Photovoltaic (PV) Inverters. Both simulation and experimental results will be presented. A 3kW Grid-Connected PV Inverter was designed and constructed for this research

Fully-automated identification of imaging biomarkers for post-operative cerebellar mutism syndrome using longitudinal paediatric MRI

Post-operative cerebellar mutism syndrome (POPCMS) in children is a post- surgical complication which occurs following the resection of tumors within the brain stem and cerebellum. High resolution brain magnetic resonance (MR) images acquired at multiple time points across a patient’s treatment allow the quantification of localized changes caused by the progression of this syndrome. However, MR images are not necessarily acquired at regular intervals throughout treatment and are often not volumetric. This restricts the analysis to 2D space and causes difficulty in intra- and inter-subject comparison. To address these challenges, we have developed an automated image processing and analysis pipeline. Multi-slice 2D MR image slices are interpolated in space and time to produce a 4D volumetric MR image dataset providing a longitudinal representation of the cerebellum and brain stem at specific time points across treatment. The deformations within the brain over time are represented using a novel metric known as the Jacobian of deformations determinant. This metric, together with the changing grey-level intensity of areas within the brain over time, are analyzed using machine learning techniques in order to identify biomarkers that correspond with the development of POPCMS following tumor resection. This study makes use of a fully automated approach which is not hypothesis-driven. As a result, we were able to automatically detect six potential biomarkers that are related to the development of POPCMS following tumor resection in the posterior fossa