A Role-Based Taxonomy of Human Resource Organizations

[Excerpt] An empirically-derived classification (taxonomy) of human resource departments , based on a few fundamental roles played in organizations, was developed as an alternative to the mostly speculative existing typologies. Four types emerged: the strategic partner, the strategic advisor, the operational partner, and the operational administrator. The stability of the solution and the relationships with variables not used to generate it were found satisfactory. The types show some similarities with those identified in the literature

Le thon : enjeux et stratégies pour l'océan Indien

Les indices de prise par unité d'effort (PUE) sont souvent utilisés pour étalonner les analyses de population séquentielles. Ces indices ne sont pas applicables dans tous les contextes et l'utilisation d'un indice inapproprié peut suggérer des tendances erronées. Il est donc nécessaire d'identifier une unité de mesure de PUE appropriée et d'effectuer des campagnes de sondage afin de compenser le manque de couverture obtenue par les carnets de bord, les déficiences des séries temporelles et l'incertitude associée aux éléments clés de la dynamique d'une pêcherie. (Résumé d'auteur

Platelet-derived transforming growth factor-β1 promotes keratinocyte proliferation in cutaneous wound healing.

Platelets are a recognised potent source of transforming growth factor-β1 (TGFβ1), a cytokine known to promote wound healing and regeneration by stimulating dermal fibroblast proliferation and extracellular matrix deposition. Platelet lysate has been advocated as a novel personalised therapeutic to treat persistent wounds, although the precise platelet-derived growth factors responsible for these beneficial effects have not been fully elucidated. The aim of this study was to investigate the specific role of platelet-derived TGFβ1 in cutaneous wound healing. Using a transgenic mouse with a targeted deletion of TGFβ1 in megakaryocytes and platelets (TGFβ1fl/fl .PF4-Cre), we show for the first time that platelet-derived TGFβ1 contributes to epidermal and dermal thickening and cellular turnover after excisional skin wounding. In vitro studies demonstrate that human dermal fibroblasts stimulated with platelet lysate containing high levels of platelet-derived TGFβ1 did not exhibit enhanced collagen deposition or proliferation, suggesting that platelet-derived TGFβ1 is not a key promoter of these wound healing processes. Interestingly, human keratinocytes displayed enhanced TGFβ1-driven proliferation in response to platelet lysate, reminiscent of our in vivo findings. In summary, our novel findings define and emphasise an important role of platelet-derived TGFβ1 in epidermal remodelling and regeneration processes during cutaneous wound healing

Structured waves near the plasma frequency observed in three auroral rocket flights

We present observations of waves at and just above the plasma frequency (<i>f<sub>pe</sub></i>) from three high frequency electric field experiments on three recent rockets launched to altitudes of 300–900 km in active aurora. The predominant observed HF waves just above <i>f<sub>pe</sub></i> are narrowband, short-lived emissions with amplitudes ranging from <1 mV/m to 20 mV/m, often associated with structured electron density. The nature of these HF waves, as determined from frequency-time spectrograms, is highly variable: in some cases, the frequency decreases monotonically with time as in the "HF-chirps" previously reported (McAdams and LaBelle, 1999), but in other cases rising frequencies are observed, or features which alternately rise and fall in frequency. They exhibit two timescales of amplitude variation: a short timescale, typically 50–100 ms, associated with individual discrete features, and a longer timescale associated with the general decrease in the amplitudes of the emissions as the rocket moves away from where the condition <i>f</i>~<i>f<sub>pe</sub></i> holds. The latter timescale ranges from 0.6 to 6.0 s, corresponding to distances of 2–7 km, assuming the phenomenon to be stationary and using the rocket velocity to convert time to distance

Treating Homeless Opioid Dependent Patients with Buprenorphine in an Office-Based Setting

CONTEXT Although office-based opioid treatment with buprenorphine (OBOT-B) has been successfully implemented in primary care settings in the US, its use has not been reported in homeless patients. OBJECTIVE To characterize the feasibility of OBOT-B in homeless relative to housed patients. DESIGN A retrospective record review examining treatment failure, drug use, utilization of substance abuse treatment services, and intensity of clinical support by a nurse care manager (NCM) among homeless and housed patients in an OBOT-B program between August 2003 and October 2004. Treatment failure was defined as elopement before completing medication induction, discharge after medication induction due to ongoing drug use with concurrent nonadherence with intensified treatment, or discharge due to disruptive behavior. RESULTS Of 44 homeless and 41 housed patients enrolled over 12 months, homeless patients were more likely to be older, nonwhite, unemployed, infected with HIV and hepatitis C, and report a psychiatric illness. Homeless patients had fewer social supports and more chronic substance abuse histories with a 3- to 6-fold greater number of years of drug use, number of detoxification attempts and percentage with a history of methadone maintenance treatment. The proportion of subjects with treatment failure for the homeless (21%) and housed (22%) did not differ (P=.94). At 12 months, both groups had similar proportions with illicit opioid use [Odds ratio (OR), 0.9 (95% CI, 0.5–1.7) P=.8], utilization of counseling (homeless, 46%; housed, 49%; P=.95), and participation in mutual-help groups (homeless, 25%; housed, 29%; P=.96). At 12 months, 36% of the homeless group was no longer homeless. During the first month of treatment, homeless patients required more clinical support from the NCM than housed patients. CONCLUSIONS Despite homeless opioid dependent patients' social instability, greater comorbidities, and more chronic drug use, office-based opioid treatment with buprenorphine was effectively implemented in this population comparable to outcomes in housed patients with respect to treatment failure, illicit opioid use, and utilization of substance abuse treatment

Analysis of scoliosis trunk deformities using ICA

This paper describes a method for analyzing scoliosis trunk deformities using Independent Component Analysis (ICA). Our hypothesis is that ICA can capture the scoliosis deformities visible on the trunk. Unlike Principal Component Analysis (PCA), ICA gives local shape variation and assumes that the data distribution is not normal. 3D torso images of 56 subjects including 28 patients with adolescent idiopathic scoliosis and 28 healthy subjects are analyzed using ICA. First, we remark that the independent components capture the local scoliosis deformities as the shoulder variation, the scapula asymmetry and the waist deformation. Second, we note that the different scoliosis curve types are characterized by different combinations of specific independent components.CIHR (Canadian Institutes of Health Research

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.IRSC / CIH

Towards Non Invasive Diagnosis of Scoliosis Using Semi-supervised Learning Approach

Collection : Lecture notes in computer science ; vol. 6112In this paper, a new methodology for the prediction of scoliosis curve types from non invasive acquisitions of the back surface of the trunk is proposed. One hundred and fifty-nine scoliosis patients had their back surface acquired in 3D using an optical digitizer. Each surface is then characterized by 45 local measurements of the back surface rotation. Using a semi-supervised algorithm, the classifier is trained with only 32 labeled and 58 unlabeled data. Tested on 69 new samples, the classifier succeeded in classifying correctly 87.0% of the data. After reducing the number of labeled training samples to 12, the behavior of the resulting classifier tends to be similar to the reference case where the classifier is trained only with the maximum number of available labeled data. Moreover, the addition of unlabeled data guided the classifier towards more generalizable boundaries between the classes. Those results provide a proof of feasibility for using a semi-supervised learning algorithm to train a classifier for the prediction of a scoliosis curve type, when only a few training data are labeled. This constitutes a promising clinical finding since it will allow the diagnosis and the follow-up of scoliotic deformities without exposing the patient to X-ray radiations.CIHR / IRS

Scoliosis Follow-Up Using Noninvasive Trunk Surface Acquisition

Adolescent idiopathic scoliosis (AIS) is a musculoskeletal pathology. It is a complex spinal curvature in a 3-D space that also affects the appearance of the trunk. The clinical follow-up of AIS is decisive for its management. Currently, the Cobb angle, which is measured from full spine radiography, is the most common indicator of the scoliosis progression. However, cumulative exposure to X-rays radiation increases the risk for certain cancers. Thus, a noninvasive method for the identification of the scoliosis progression from trunk shape analysis would be helpful. In this study, a statistical model is built from a set of healthy subjects using independent component analysis and genetic algorithm. Based on this model, a representation of each scoliotic trunk from a set of AIS patients is computed and the difference between two successive acquisitions is used to determine if the scoliosis has progressed or not. This study was conducted on 58 subjects comprising 28 healthy subjects and 30 AIS patients who had trunk surface acquisitions in upright standing posture. The model detects 93% of the progressive cases and 80% of the nonprogressive cases. Thus, the rate of false negatives, representing the proportion of undetected progressions, is very low, only 7%. This study shows that it is possible to perform a scoliotic patient's follow-up using 3-D trunk image analysis, which is based on a noninvasive acquisition technique.IRSC / CIH