Transnational authority in the knowledge-based economy: Who sets the standards of ICT training and certification?

This article examines the extent and limits of nonstate forms of authority in international relations. It analyzes how the information and communication technology (ICT) infrastructure for the tradability of services in a global knowledge-based economy relies on informal regulatory practices for the adjustment of ICT-related skills. By focusing on the challenge that highly volatile and short-lived cycles of demands for this type of knowledge pose for ensuring the right qualification of the labor force, the article explores how companies and associations provide training and certification programs as part of a growing market for educational services setting their own standards. The existing literature on non-conventional forms of authority in the global political economy has emphasized that the consent of actors, subject to informal rules and some form of state support, remains crucial for the effectiveness of those new forms of power. However, analyses based on a limited sample of actors tend toward a narrow understanding of the issues concerned and fail to fully explore the differentiated space in which non state authority is emerging. This article develops a three-dimensional analytical framework that brings together the scope of the issues involved, the range of nonstate actors concerned, and the spatial scope of their authority. The empirical findings highlight the limits of these new forms of nonstate authority and shed light on the role of the state and international governmental organizations in this new context.

Meta-analysis of the effects of MI training on clinicians' behavior.

MI-based interventions are widely used with a number of different clinical populations and their efficacy has been well established. However, the clinicians' training has not traditionally been the focus of empirical investigations. We conducted a meta-analytic review of clinicians' MI-training and MI-skills findings. Fifteen studies were included, involving 715 clinicians. Pre-post training effect sizes were calculated (13 studies) as well as group contrast effect sizes (7 studies). Pre-post training comparisons showed medium to large ES of MI training, which are maintained over a short period of time. When compared to a control group, our results also suggested higher MI proficiency in the professionals trained in MI than in nontrained ones (medium ES). However, this estimate of ES may be affected by a publication bias and therefore, should be considered with caution. Methodological limitations and potential sources of heterogeneity of the studies included in this meta-analysis are discussed.

Training medical students to conduct motivational interviewing: a randomized controlled trial.

OBJECTIVE: To examine the effectiveness of motivational interviewing (MI) training among medical students. METHODS: All students (n=131) (year 5) at Lausanne Medical School, Switzerland were randomized into an experimental or a control group. After a training in basic communication skills (control condition), an 8-h MI training was completed by 84.8% students in the exprimental group. One week later, students in both groups were invited to meet with two standardized patients. MI skills were coded by blinded research assistants using the Motivational Interviewing Treatment Integrity 3.0. RESULTS: Superior MI performance was shown for trained versus control students, as demonstrated by higher scores for "Empathy" [p<0.001] and "MI Spirit" [p<0.001]. Scores were similar between groups for "Direction", indicating that students in both groups invited the patient to talk about behavior change. Behavior counts assessment demonstrated better performance in MI in trained versus untrained students regarding occurences of MI-adherent behavior [p<0.001], MI non-adherent behavior [p<0.001], Closed questions [p<0.001], Open questions [p=0.001], simple reflections [p=0.03], and Complex reflections [p<0.001]. Occurrences were similar between groups regarding "Giving information". CONCLUSION: An 8-h training workshop was associated with improved MI performance. PRACTICE IMPLICATIONS: These findings lend support for the implementation of MI training in medical schools.

Microsatellites can be misleading: an empirical and simulation study.

It has been long recognized that highly polymorphic genetic markers can lead to underestimation of divergence between populations when migration is low. Microsatellite loci, which are characterized by extremely high mutation rates, are particularly likely to be affected. Here, we report genetic differentiation estimates in a contact zone between two chromosome races of the common shrew (Sorex araneus), based on 10 autosomal microsatellites, a newly developed Y-chromosome microsatellite, and mitochondrial DNA. These results are compared to previous data on proteins and karyotypes. Estimates of genetic differentiation based on F- and R-statistics are much lower for autosomal microsatellites than for all other genetic markers. We show by simulations that this discrepancy stems mainly from the high mutation rate of microsatellite markers for F-statistics and from deviations from a single-step mutation model for R-statistics. The sex-linked genetic markers show that all gene exchange between races is mediated by females. The absence of male-mediated gene flow most likely results from male hybrid sterility.

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Introduction: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints. Methods: We used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set. Results: A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models. Conclusions: We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.

Effect of intermittent hypoxic training on HIF gene expression in human skeletal muscle and leukocytes

Intermittent hypoxic exposure with exercise training is based on the assumption that brief exposure to hypoxia is sufficient to induce beneficial muscular adaptations mediated via hypoxia-inducible transcription factors (HIF). We previously demonstrated (Mounier et al. Med Sci Sports Exerc 38:1410-1417, 2006) that leukocytes respond to hypoxia with a marked inter-individual variability in HIF-1alpha mRNA. This study compared the effects of 3 weeks of intermittent hypoxic training on hif gene expression in both skeletal muscle and leukocytes. Male endurance athletes (n = 19) were divided into an Intermittent Hypoxic Exposure group (IHE) and a Normoxic Training group (NT) with each group following a similar 3-week exercise training program. After training, the amount of HIF-1alpha mRNA in muscle decreased only in IHE group (-24.7%, P < 0.05) whereas it remained unchanged in leukocytes in both groups. The levels of vEGF(121) and vEGF(165) mRNA in skeletal muscle increased significantly after training only in the NT group (+82.5%, P < 0.05 for vEGF(121); +41.2%, P < 0.05 for vEGF(165)). In leukocytes, only the IHE group showed a significant change in vEGF(165) (-28.2%, P < 0.05). The significant decrease in HIF-1alpha mRNA in skeletal muscle after hypoxic training suggests that transcriptional and post-transcriptional regulations of the hif-1alpha gene are different in muscle and leukocytes.

Effects of Intermittent Training on Anaerobic Performance and MCT Transporters in Athletes.

This study examined the effects of intermittent hypoxic training (IHT) on skeletal muscle monocarboxylate lactate transporter (MCT) expression and anaerobic performance in trained athletes. Cyclists were assigned to two interventions, either normoxic (N; n&#8202;=&#8202;8; 150 mmHg PIO2) or hypoxic (H; n&#8202;=&#8202;10; &#8764;3000 m, 100 mmHg PIO2) over a three week training (5×1 h-1h30.week-1) period. Prior to and after training, an incremental exercise test to exhaustion (EXT) was performed in normoxia together with a 2 min time trial (TT). Biopsy samples from the vastus lateralis were analyzed for MCT1 and MCT4 using immuno-blotting techniques. The peak power output (PPO) increased (p<0.05) after training (7.2% and 6.6% for N and H, respectively), but VO2max showed no significant change. The average power output in the TT improved significantly (7.3% and 6.4% for N and H, respectively). No differences were found in MCT1 and MCT4 protein content, before and after the training in either the N or H group. These results indicate there are no additional benefits of IHT when compared to similar normoxic training. Hence, the addition of the hypoxic stimulus on anaerobic performance or MCT expression after a three-week training period is ineffective.

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

Climate-based empirical models show biased predictions of butterfly communities along environmental gradients

A better understanding of the factors that mould ecological community structure is required to accurately predict community composition and to anticipate threats to ecosystems due to global changes. We tested how well stacked climate-based species distribution models (S-SDMs) could predict butterfly communities in a mountain region. It has been suggested that climate is the main force driving butterfly distribution and community structure in mountain environments, and that, as a consequence, climate-based S-SDMs should yield unbiased predictions. In contrast to this expectation, at lower altitudes, climate-based S-SDMs overpredicted butterfly species richness at sites with low plant species richness and underpredicted species richness at sites with high plant species richness. According to two indices of composition accuracy, the Sorensen index and a matching coefficient considering both absences and presences, S-SDMs were more accurate in plant-rich grasslands. Butterflies display strong and often specialised trophic interactions with plants. At lower altitudes, where land use is more intense, considering climate alone without accounting for land use influences on grassland plant richness leads to erroneous predictions of butterfly presences and absences. In contrast, at higher altitudes, where climate is the main force filtering communities, there were fewer differences between observed and predicted butterfly richness. At high altitudes, even if stochastic processes decrease the accuracy of predictions of presence, climate-based S-SDMs are able to better filter out butterfly species that are unable to cope with severe climatic conditions, providing more accurate predictions of absences. Our results suggest that predictions should account for plants in disturbed habitats at lower altitudes but that stochastic processes and heterogeneity at high altitudes may limit prediction success of climate-based S-SDMs.