The impact of gender composition on team performance and decision-making: Evidence from the field

We investigate whether the gender composition of teams affect theireconomic performance. We study a large business game, played in groups ofthree, where each group takes the role of a general manager. There are twoparallel competitions, one involving undergraduates and the other involvingMBAs. Our analysis shows that teams formed by three women aresignificantly outperformed by any other gender combination, both at theundergraduate and MBA levels. Looking across the performancedistribution, we find that for undergraduates, three women teams areoutperformed throughout, but by as much as 10pp at the bottom and by only1pp at the top. For MBAs, at the top, the best performing group is two menand one woman. The differences in performance are explained bydifferences in decision-making. We observe that three women teams are lessaggressive in their pricing strategies, invest less in R&D, and invest more insocial sustainability initiatives, than any other gender combination teams.Finally, we find support for the hypothesis that it is poor work dynamicsamong the three women teams that drives the results.

Variation in performance across Swiss mammography screening programmes: the influence of reading strategy and experience.

[Abstract] Reading volume and mammography screening performance appear positively correlated. Performance was compared across organised Swiss screening programmes, which target relatively small populations. Except for accreditation of 2nd readers radiologists (restrictive vs non-restrictive strategy), Swiss programmes have similar screening regimen/procedures and duration, which maximises comparability. Variation in performance was explored in order to improve mammography practice and optimise screening performance. Indicators of quality and effectiveness were evaluated for about 200,000 screens performed over 4 screening rounds in the 3 longest-standing Swiss cantonal programmes (of Vaud, Geneva and Valais). Interval cancers were identified by linkage with cancer registries records. Most European standards of performance were met with a favourable cancer stage shift. Several performance indicators showed substantial variation across programmes. In subsequent rounds, compared with programmes (Vaud and Geneva) which accredited few 2nd readers to increase their individual reading volume, proportions of in situ lesions and of small cancers (? 1cm) were one third lower and halved, respectively, and the proportion of advanced lesions (stage II+) nearly 50% higher in the programme without a restrictive selection strategy. Discrepancy in second-year proportional incidence of interval cancers appears to be multicausal. Differences in performance could partly be explained by a selective strategy for 2nd readers and a prior experience in service screening, but not by the levels of opportunistic screening and programme attendance. This study provides clues for enhancing mammography screening performance in low-volume Swiss programmes.

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data.

Public infrastructure and the performance of manufacturing industries: short-and long-run

We present a theoretical framework for determining the short- and long-run effects of infrastructure. While the short-run effects have been the focus of most previous studies, here we derive long-run elasticities by taking into account the adjustment of quasi-fixed inputs to their optimum levels. By considering the impact of infrastructure on private investment decisions, we observe how, apart from the direct effect on costs in the short-run, infrastructure exerts an indirect source of influence in the long-run through their effect on private capital. The model is applied to manufacturing industries in the Spanish regions

Public infrastructure and the performance of manufacturing industries: short-and long-run

We present a theoretical framework for determining the short- and long-run effects of infrastructure. While the short-run effects have been the focus of most previous studies, here we derive long-run elasticities by taking into account the adjustment of quasi-fixed inputs to their optimum levels. By considering the impact of infrastructure on private investment decisions, we observe how, apart from the direct effect on costs in the short-run, infrastructure exerts an indirect source of influence in the long-run through their effect on private capital. The model is applied to manufacturing industries in the Spanish regions

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Assessment of driving capability through the use of clinical and psychomotor tests in relation to blood cannabinoids levels following oral administration of 20 mg dronabinol or of a cannabis decoction made with 20 or 60 mg Delta9-THC.

Delta(9)-Tetrahydrocannabinol (THC) is frequently found in the blood of drivers suspected of driving under the influence of cannabis or involved in traffic crashes. The present study used a double-blind crossover design to compare the effects of medium (16.5 mg THC) and high doses (45.7 mg THC) of hemp milk decoctions or of a medium dose of dronabinol (20 mg synthetic THC, Marinol on several skills required for safe driving. Forensic interpretation of cannabinoids blood concentrations were attempted using the models proposed by Daldrup (cannabis influencing factor or CIF) and Huestis and coworkers. First, the time concentration-profiles of THC, 11-hydroxy-Delta(9)-tetrahydrocannabinol (11-OH-THC) (active metabolite of THC), and 11-nor-9-carboxy-Delta(9)-tetrahydrocannabinol (THCCOOH) in whole blood were determined by gas chromatography-mass spectrometry-negative ion chemical ionization. Compared to smoking studies, relatively low concentrations were measured in blood. The highest mean THC concentration (8.4 ng/mL) was achieved 1 h after ingestion of the strongest decoction. Mean maximum 11-OH-THC level (12.3 ng/mL) slightly exceeded that of THC. THCCOOH reached its highest mean concentration (66.2 ng/mL) 2.5-5.5 h after intake. Individual blood levels showed considerable intersubject variability. The willingness to drive was influenced by the importance of the requested task. Under significant cannabinoids influence, the participants refused to drive when they were asked whether they would agree to accomplish several unimportant tasks, (e.g., driving a friend to a party). Most of the participants reported a significant feeling of intoxication and did not appreciate the effects, notably those felt after drinking the strongest decoction. Road sign and tracking testing revealed obvious and statistically significant differences between placebo and treatments. A marked impairment was detected after ingestion of the strongest decoction. A CIF value, which relies on the molar ratio of main active to inactive cannabinoids, greater than 10 was found to correlate with a strong feeling of intoxication. It also matched with a significant decrease in the willingness to drive, and it matched also with a significant impairment in tracking performances. The mathematic model II proposed by Huestis et al. (1992) provided at best a rough estimate of the time of oral administration with 27% of actual values being out of range of the 95% confidence interval. The sum of THC and 11-OH-THC blood concentrations provided a better estimate of impairment than THC alone. This controlled clinical study points out the negative influence on fitness to drive after medium or high dose oral THC or dronabinol.

SWEET SORGHUM PERFORMANCE AFFECTED BY SOIL COMPACTION AND SOWING TIME AS A SECOND CROP IN THE BRAZILIAN CERRADO

ABSTRACT Increasing attention has recently been given to sweet sorghum as a renewable raw material for ethanol production, mainly because its cultivation can be fully mechanized. However, the intensive use of agricultural machinery causes soil structural degradation, especially when performed under inadequate conditions of soil moisture. The aims of this study were to evaluate the physical quality of aLatossolo Vermelho Distroférrico (Oxisol) under compaction and its components on sweet sorghum yield forsecond cropsowing in the Brazilian Cerrado (Brazilian tropical savanna). The experiment was conducted in a randomized block design, in a split plot arrangement, with four replications. Five levels of soil compaction were tested from the passing of a tractor at the following traffic intensities: 0 (absence of additional compaction), 1, 2, 7, and 15 passes over the same spot. The subplots consisted of three different sowing times of sweet sorghum during the off-season of 2013 (20/01, 17/02, and 16/03). Soil physical quality was measured through the least limiting water range (LLWR) and soil water limitation; crop yield and technological parameters were also measured. Monitoring of soil water contents indicated a reduction in the frequency of water content in the soil within the limits of the LLWR (Fwithin) as agricultural traffic increased (T0 = T1 = T2>T7>T15), and crop yield is directly associated with soil water content. The crop sown in January had higher industrial quality; however, there was stalk yield reduction when bulk density was greater than 1.26 Mg m-3, with a maximum yield of 50 Mg ha-1 in this sowing time. Cultivation of sweet sorghum as a second crop is a promising alternative, but care should be taken in cultivation under conditions of pronounced climatic risks, due to low stalk yield.

Low E2F1 transcript levels are a strong determinant of favorable breast cancer outcome.

INTRODUCTION: We investigated whether mRNA levels of E2F1, a key transcription factor involved in proliferation, differentiation and apoptosis, could be used as a surrogate marker for the determination of breast cancer outcome. METHODS: E2F1 and other proliferation markers were measured by quantitative RT-PCR in 317 primary breast cancer patients from the Stiftung Tumorbank Basel. Correlations to one another as well as to the estrogen receptor and ERBB2 status and clinical outcome were investigated. Results were validated and further compared with expression-based prognostic profiles using The Netherlands Cancer Institute microarray data set reported by Fan and colleagues. RESULTS: E2F1 mRNA expression levels correlated strongly with the expression of other proliferation markers, and low values were mainly found in estrogen receptor-positive and ERBB2-negative phenotypes. Patients with low E2F1-expressing tumors were associated with favorable outcome (hazard ratio = 4.3 (95% confidence interval = 1.8-9.9), P = 0.001). These results were consistent in univariate and multivariate Cox analyses, and were successfully validated in The Netherlands Cancer Institute data set. Furthermore, E2F1 expression levels correlated well with the 70-gene signature displaying the ability of selecting a common subset of patients at good prognosis. Breast cancer patients' outcome was comparably predictable by E2F1 levels, by the 70-gene signature, by the intrinsic subtype gene classification, by the wound response signature and by the recurrence score. CONCLUSION: Assessment of E2F1 at the mRNA level in primary breast cancer is a strong determinant of breast cancer patient outcome. E2F1 expression identified patients at low risk of metastasis irrespective of the estrogen receptor and ERBB2 status, and demonstrated similar prognostic performance to different gene expression-based predictors.

Differential effects of sodium oxybate and baclofen on EEG, sleep, neurobehavioral performance, and memory.

STUDY OBJECTIVES: Sodium oxybate (SO) is a GABA(B) agonist used to treat the sleep disorder narcolepsy. SO was shown to increase slow wave sleep (SWS) and EEG delta power (0.75-4.5 Hz), both indexes of NREM sleep (NREMS) intensity and depth, suggesting that SO enhances recuperative function of NREM. We investigated whether SO induces physiological deep sleep. DESIGN: SO was administered before an afternoon nap or before the subsequent experimental night in 13 healthy volunteers. The effects of SO were compared to baclofen (BAC), another GABA(B) receptor agonist, to assess the role of GABA(B) receptors in the SO response. MEASUREMENTS AND RESULTS: As expected, a nap significantly decreased sleep need and intensity the subsequent night. Both drugs reversed this nap effect on the subsequent night by decreasing sleep latency and increasing total sleep time, SWS during the first NREMS episode, and EEG delta and theta (0.75-7.25 Hz) power during NREMS. The SO-induced increase in EEG delta and theta power was, however, not specific to NREMS and was also observed during REM sleep (REMS) and wakefulness. Moreover, the high levels of delta power during a nap following SO administration did not affect delta power the following night. SO and BAC taken before the nap did not improve subsequent psychomotor performance and subjective alertness, or memory consolidation. Finally, SO and BAC strongly promoted the appearance of sleep onset REM periods. CONCLUSIONS: The SO-induced EEG slow waves seem not to be functionally similar to physiological slow waves. Our findings also suggest a role for GABA(B) receptors in REMS generation. CITATION: Vienne J; Lecciso G; Constantinescu I; Schwartz S; Franken P; Heinzer R; Tafti M. Differential effects of sodium oxybate and baclofen on EEG, sleep, neurobehavioral performance, and memory. SLEEP 2012;35(8):1071-1084.

Single-step extraction of fluconazole from plasma by ultra-filtration for the measurement of its free concentration by high performance liquid chromatography.

High performance liquid chromatography (HPLC) is the reference method for measuring concentrations of antimicrobials in blood. This technique requires careful sample preparation. Protocols using organic solvents and/or solid extraction phases are time consuming and entail several manipulations, which can lead to partial loss of the determined compound and increased analytical variability. Moreover, to obtain sufficient material for analysis, at least 1 ml of plasma is required. This constraint makes it difficult to determine drug levels when blood sample volumes are limited. However, drugs with low plasma-protein binding can be reliably extracted from plasma by ultra-filtration with a minimal loss due to the protein-bound fraction. This study validated a single-step ultra-filtration method for extracting fluconazole (FLC), a first-line antifungal agent with a weak plasma-protein binding, from plasma to determine its concentration by HPLC. Spiked FLC standards and unknowns were prepared in human and rat plasma. Samples (240 microl) were transferred into disposable microtube filtration units containing cellulose or polysulfone filters with a 5 kDa cut-off. After centrifugation for 60 min at 15000g, FLC concentrations were measured by direct injection of the filtrate into the HPLC. Using cellulose filters, low molecular weight proteins were eluted early in the chromatogram and well separated from FLC that eluted at 8.40 min as a sharp single peak. In contrast, with polysulfone filters several additional peaks interfering with the FLC peak were observed. Moreover, the FLC recovery using cellulose filters compared to polysulfone filters was higher and had a better reproducibility. Cellulose filters were therefore used for the subsequent validation procedure. The quantification limit was 0.195 mgl(-1). Standard curves with a quadratic regression coefficient > or = 0.9999 were obtained in the concentration range of 0.195-100 mgl(-1). The inter and intra-run accuracies and precisions over the clinically relevant concentration range, 1.875-60 mgl(-1), fell well within the +/-15% variation recommended by the current guidelines for the validation of analytical methods. Furthermore, no analytical interference was observed with commonly used antibiotics, antifungals, antivirals and immunosuppressive agents. Ultra-filtration of plasma with cellulose filters permits the extraction of FLC from small volumes (240 microl). The determination of FLC concentrations by HPLC after this single-step procedure is selective, precise and accurate.

Firm's network capability and innovation performance: evidences from China hi-tech industry

ABSTRACT : A firm's competitive advantage can arise from internal resources as well as from an interfirm network. -This dissertation investigates the competitive advantage of a firm involved in an innovation network by integrating strategic management theory and social network theory. It develops theory and provides empirical evidence that illustrates how a networked firm enables the network value and appropriates this value in an optimal way according to its strategic purpose. The four inter-related essays in this dissertation provide a framework that sheds light on the extraction of value from an innovation network by managing and designing the network in a proactive manner. The first essay reviews research in social network theory and knowledge transfer management, and identifies the crucial factors of innovation network configuration for a firm's learning performance or innovation output. The findings suggest that network structure, network relationship, and network position all impact on a firm's performance. Although the previous literature indicates that there are disagreements about the impact of dense or spare structure, as well as strong or weak ties, case evidence from Chinese software companies reveals that dense and strong connections with partners are positively associated with firms' performance. The second essay is a theoretical essay that illustrates the limitations of social network theory for explaining the source of network value and offers a new theoretical model that applies resource-based view to network environments. It suggests that network configurations, such as network structure, network relationship and network position, can be considered important network resources. In addition, this essay introduces the concept of network capability, and suggests that four types of network capabilities play an important role in unlocking the potential value of network resources and determining the distribution of network rents between partners. This essay also highlights the contingent effects of network capability on a firm's innovation output, and explains how the different impacts of network capability depend on a firm's strategic choices. This new theoretical model has been pre-tested with a case study of China software industry, which enhances the internal validity of this theory. The third essay addresses the questions of what impact network capability has on firm innovation performance and what are the antecedent factors of network capability. This essay employs a structural equation modelling methodology that uses a sample of 211 Chinese Hi-tech firms. It develops a measurement of network capability and reveals that networked firms deal with cooperation between, and coordination with partners on different levels according to their levels of network capability. The empirical results also suggests that IT maturity, the openness of culture, management system involved, and experience with network activities are antecedents of network capabilities. Furthermore, the two-group analysis of the role of international partner(s) shows that when there is a culture and norm gap between foreign partners, a firm must mobilize more resources and effort to improve its performance with respect to its innovation network. The fourth essay addresses the way in which network capabilities influence firm innovation performance. By using hierarchical multiple regression with data from Chinese Hi-tech firms, the findings suggest that there is a significant partial mediating effect of knowledge transfer on the relationships between network capabilities and innovation performance. The findings also reveal that the impacts of network capabilities divert with the environment and strategic decision the firm has made: exploration or exploitation. Network constructing capability provides a greater positive impact on and yields more contributions to innovation performance than does network operating capability in an exploration network. Network operating capability is more important than network constructing capability for innovative firms in an exploitation network. Therefore, these findings highlight that the firm can shape the innovation network proactively for better benefits, but when it does so, it should adjust its focus and change its efforts in accordance with its innovation purposes or strategic orientation.

Factors favoring a degradation or an improvement in activities of daily living (ADL) performance among nursing home (NH) residents: A survival analysis.

Different factors influence ADL performance among nursing home (NH) residents in long term care. The aim was to investigate which factors were associated with a significant change of ADL performance in NH residents, and whether or not these factors were gender-specific. The design was a survival analysis. The 10,199 participants resided in ninety Swiss NHs. Their ADL performance had been assessed by the Resident Assessment Instrument Minimum Data Set (RAI-MDS) in the period from 1997 to 2007. Relevant change in ADL performance was defined as 2 levels of change on the ADL scale between two successive assessments. The occurrence of either an improvement or a degradation of the ADL status) was analyzed using the Cox proportional hazard model. The analysis included a total of 10,199 NH residents. Each resident received between 2 and 23 assessments. Poor balance, incontinence, impaired cognition, a low BMI, impaired vision, no daily contact with proxies, impaired hearing and the presence of depression were, by hierarchical order, significant risk factors for NH residents to experience a degradation of ADL performance. Residents, who were incontinent, cognitively impaired or had a high BMI were significantly less likely to improve their ADL abilities. Male residents with cancer were prone to see their ADL improve. The year of NH entry was significantly associated with either degradation or improvement of ADL performance. Measures aiming at improving balance and continence, promoting physical activity, providing appropriate nourishment and cognitive enhancement are important for ADL performance in NH residents.

Sensitive determination of D-lactic acid and L-lactic acid in urine by high-performance liquid chromatography-tandem mass spectrometry.

D-lactic acid in urine originates mainly from bacterial production in the intestinal tract. Increased D-lactate excretion as observed in patients affected by short bowel syndrome or necrotizing enterocolitis reflects D-lactic overproduction. Therefore, there is a need for a reliable and sensitive method able to detect D-lactic acid even at subclinical elevation levels. A new and highly sensitive method for the simultaneous determination of L- and D-lactic acid by a two-step procedure has been developed. This method is based on the concentration of lactic acid enantiomers from urine by supported liquid extraction followed by high-performance liquid chromatography-tandem mass spectrometry. The separation was achieved by the use of an Astec Chirobiotic? R chiral column under isocratic conditions. The calibration curves were linear over the ranges of 2-400 and 0.5-100 µmol/L respectively for L- and D-lactic acid. The limit of detection of D-lactic acid was 0.125 µmol/L and its limit of quantification was 0.5 µmol/L. The overall accuracy and precision were well within 10% of the nominal values. The developed method is suitable for production of reference values in children and could be applied for accurate routine analysis.

Improvement of therapeutic drug monitoring of imatinib by Bayesian prediction of trough levels. Personalized drug dosage

Aims: Plasma concentrations of imatinib differ largely between patients despite same dosage, owing to large inter-individual variability in pharmacokinetic (PK) parameters. As the drug concentration at the end of the dosage interval (Cmin) correlates with treatment response and tolerability, monitoring of Cmin is suggested for therapeutic drug monitoring (TDM) of imatinib. Due to logistic difficulties, random sampling during the dosage interval is however often performed in clinical practice, thus rendering the respective results not informative regarding Cmin values.Objectives: (I) To extrapolate randomly measured imatinib concentrations to more informative Cmin using classical Bayesian forecasting. (II) To extend the classical Bayesian method to account for correlation between PK parameters. (III) To evaluate the predictive performance of both methods.Methods: 31 paired blood samples (random and trough levels) were obtained from 19 cancer patients under imatinib. Two Bayesian maximum a posteriori (MAP) methods were implemented: (A) a classical method ignoring correlation between PK parameters, and (B) an extended one accounting for correlation. Both methods were applied to estimate individual PK parameters, conditional on random observations and covariate-adjusted priors from a population PK model. The PK parameter estimates were used to calculate trough levels. Relative prediction errors (PE) were analyzed to evaluate accuracy (one-sample t-test) and to compare precision between the methods (F-test to compare variances).Results: Both Bayesian MAP methods allowed non-biased predictions of individual Cmin compared to observations: (A) - 7% mean PE (CI95% - 18 to 4 %, p = 0.15) and (B) - 4% mean PE (CI95% - 18 to 10 %, p = 0.69). Relative standard deviations of actual observations from predictions were 22% (A) and 30% (B), i.e. comparable to the intraindividual variability reported. Precision was not improved by taking into account correlation between PK parameters (p = 0.22).Conclusion: Clinical interpretation of randomly measured imatinib concentrations can be assisted by Bayesian extrapolation to maximum likelihood Cmin. Classical Bayesian estimation can be applied for TDM without the need to include correlation between PK parameters. Both methods could be adapted in the future to evaluate other individual pharmacokinetic measures correlated to clinical outcomes, such as area under the curve(AUC).