MULTI-GENERATIONAL WORKFORCE STRATEGIES FOR 21ST CENTURY MANAGERS

At any given time, managers can employ up to five generations of individuals in the workplace. Each generational cohort enhances the workplace with their own belief system, habits, attitude, and work expectations. The manager\u27s responsibility to both the organization and the workforce is to bring all the employees together to foster shared values and work towards the organization’s common goal. The aim of this qualitative collective case study was to investigate the strategies managers use to direct a multigenerational workforce in today’s marketplace. Data were collected using semi-structured interviews from managers in the banking, educational, grocery, medical, non-profit, restaurant, and retail industries. Participants shared their experiences and skills used in maintaining a multi-generational workforce. The data was analyzed and conclusions were drawn based on the participants’ responses. The results of this study demonstrated that open communication and constant employee feedback were not only the managers’ main objectives when interacting with their workforce but also their greatest area of opportunity for improvement

A comparison of chemistry and dust cloud formation in ultracool dwarf model atmospheres

The atmospheres of substellar objects contain clouds of oxides, iron, silicates, and other refractory condensates. Water clouds are expected in the coolest objects. The opacity of these `dust' clouds strongly affects both the atmospheric temperature-pressure profile and the emergent flux. Thus any attempt to model the spectra of these atmospheres must incorporate a cloud model. However the diversity of cloud models in atmospheric simulations is large and it is not always clear how the underlying physics of the various models compare. Likewise the observational consequences of different modeling approaches can be masked by other model differences, making objective comparisons challenging. In order to clarify the current state of the modeling approaches, this paper compares five different cloud models in two sets of tests. Test case 1 tests the dust cloud models for a prescribed L, L--T, and T-dwarf atmospheric (temperature T, pressure p, convective velocity vconv)-structures. Test case 2 compares complete model atmosphere results for given (effective temperature Teff, surface gravity log g). All models agree on the global cloud structure but differ in opacity-relevant details like grain size, amount of dust, dust and gas-phase composition. Comparisons of synthetic photometric fluxes translate into an modelling uncertainty in apparent magnitudes for our L-dwarf (T-dwarf) test case of 0.25 < \Delta m < 0.875 (0.1 < \Delta m M 1.375) taking into account the 2MASS, the UKIRT WFCAM, the Spitzer IRAC, and VLT VISIR filters with UKIRT WFCAM being the most challenging for the models. (abr.)Comment: 22 pages, 17 figures, MNRAS 2008, accepted, (minor grammar/typo corrections

Genetic-Background Modulation of Core and Variable Autistic-Like Symptoms in Fmr1 Knock-Out Mice

International audienc

Risk Prediction for Epithelial Ovarian Cancer in 11 United States–Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted

Immune plasma for the treatment of severe influenza: an open-label, multicentre, phase 2 randomised study

BackgroundInfluenza causes substantial morbidity and mortality despite available treatments. Anecdotal reports suggest that plasma with high antibody titres to influenza might be of benefit in the treatment of severe influenza.MethodsIn this randomised, open-label, multicentre, phase 2 trial, 29 academic medical centres in the USA assessed the safety and efficacy of anti-influenza plasma with haemagglutination inhibition antibody titres of 1:80 or more to the infecting strain. Hospitalised children and adults (including pregnant women) with severe influenza A or B (defined as the presence of hypoxia or tachypnoea) were randomly assigned to receive either two units (or paediatric equivalent) of anti-influenza plasma plus standard care, versus standard care alone, and were followed up for 28 days. The primary endpoint was time to normalisation of patients' respiratory status (respiratory rate of ≤20 breaths per min for adults or age-defined thresholds of 20-38 breaths per min for children) and a room air oxygen saturation of 93% or more. This study is registered with ClinicalTrials.gov, number NCT01052480.FindingsBetween Jan 13, 2011, and March 2, 2015, 113 participants were screened for eligibility and 98 were randomly assigned from 20 out of 29 participating sites. Of the participants with confirmed influenza (by PCR), 28 (67%) of 42 in the plasma plus standard care group normalised their respiratory status by day 28 compared with 24 (53%) of 45 participants on standard care alone (p=0·069). The hazard ratio (HR) comparing plasma plus standard care with standard care alone was 1·71 (95% CI 0·96-3·06). Six participants died, one (2%) from the plasma plus standard care group and five (10%) from the standard care group (HR 0·19 [95% CI 0·02-1·65], p=0·093). Participants in the plasma plus standard care group had non-significant reductions in days in hospital (median 6 days [IQR 4-16] vs 11 days [5-25], p=0·13) and days on mechanical ventilation (median 0 days [IQR 0-6] vs 3 days [0-14], p=0·14). Fewer plasma plus standard care participants had serious adverse events compared with standard care alone recipients (nine [20%] of 46 vs 20 [38%] of 52, p=0·041), the most frequent of which were acute respiratory distress syndrome (one [2%] vs two [4%] patients) and stroke (one [2%] vs two [4%] patients).InterpretationAlthough there was no significant effect of plasma treatment on the primary endpoint, the treatment seemed safe and well tolerated. A phase 3 randomised trial is now underway to further assess this intervention.FundingNational Institute of Allergy and Infectious Diseases, US National Institutes of Health