The positive effect of perceived leader humor on negative workplace gossip

The purpose of this study was to examine the positive relationship between employee perceived leader humor and employee negative workplace gossip about a leader (NWGL). Using a multiple time point investigation of leaders and followers (N = 168), I found that perceived leader humor was positively related to NWGL and that employee perceived team inclusion mediated this relationship. Further, employee job security moderated the relationship between perceived leader humor and employee perceived team inclusion, and also moderated the mediating effect of employee perceived team inclusion in the link between perceived leader humor and employee NWGL. These findings suggest that the beneficial effects of leader humor are not universal, and that the fostering of leader humor might have unintended negative consequences, that is, increased NWGL through employee perception of team inclusion. Implications for theory and research are discussed.

Multiple time-point assessment of lncRNA MEG3 shows potential to monitor treatment efficacy in rheumatoid arthritis patients

Background: This study explored the clinical role of lncRNA MEG3 in rheumatoid arthritis (RA) management. Materials & methods: Totally, 191 active RA patients were enrolled, and their lncRNA MEG3 expressions in peripheral blood monoclonal cells were detected. Results: LncRNA MEG3 expression was downregulated, and it negatively correlated with lesion joints, inflammation and disease activity in RA patients. Moreover, lncRNA MEG3 expression was increased during treatment; meanwhile its increment correlated with treatment response and remission. Conclusion: LncRNA MEG3 may serve as a potential biomarker for monitoring treatment efficacy in RA management.

Characterization of Pythium Species Collected from a Multiple Time-Point Sampling of Cucurbits in South Carolina

Species of Pythium cause root and stem rot in cucurbits, but no formal surveys have been conducted in the United States to identify which species are responsible. The cucurbit hosts bottle gourd, cucumber, Hubbard squash, and watermelon were transplanted in May, July, September, and November into sentinel plots in four and five different fields in 2017 and 2018, respectively, in South Carolina. Eight of the nine fields were replanted in March 2019. Isolates (600) were collected and identified by sequencing DNA of the mitochondrial cytochrome oxidase I region. The four most common species were P. spinosum (45.6% of all isolates), P. myriotylum (20.0%), P. irregulare (15.3%), and P. aphanidermatum (12.8%). P. myriotylum and P. aphanidermatum were predominantly isolated in May, July, and September, whereas P. spinosum and P. irregulare were predominantly isolated in November and March. Isolates of P. ultimum, P. irregulare, and P. spinosum were more virulent than isolates of P. myriotylum and P. aphanidermatum at 25°C. Representative isolates were screened in vitro for sensitivity to three fungicides: mefenoxam, propamocarb, and oxathiapiprolin. All isolates were sensitive to mefenoxam and propamocarb, but these same isolates were insensitive to oxathiapiprolin, except those classified taxonomically in Pythium clade I.

Decision making in surveillance of high-grade gliomas using perfusion MRI as adjunct to conventional MRI and artificial intelligence.

2054 Background: Surveillance of High-Grade Gliomas (HGGs) remains a major challenge in clinical neurooncology. Histopathological validation is not an option during the course of disease and imaging surveillance suffers from ambiguous features of both disease progression and treatment related changes. This study aimed to differentiate between Pseudoprogression (PsP) and Progressive Disease (PD) using an artificial intelligence (support vector machine - SVM) classification algorithm. Methods: Two groups of patients with histologically proven HGGs were analysed, a group with a single time point DSC perfusion MRI (45 patients) and a group with multiple time point DSC perfusion MRI (19 patients). Both groups included conventional MRI studies prior and after each perfusion MRI. This study design aimed to replicate decision making in clinical practice including multiple previous studies for each patient. SVM training was performed with all available MRI studies for each group and classification was based on different feature datasets from a single or multiple (subtracted features) time points. Classification accuracy comparisons were performed by calculating prediction error rates for different feature datasets and different time point analyses. Results: Our results indicate that the addition of multiple time point perfusion MRI combined with structural (conventional with gadolinium-enhanced sequences) MRI features results in optimal classification performance (median error rate: 0.016, lowest value dispersion). Subtracted feature datasets improved classification performance, more prominently when the final and first perfusion studies were included in the analysis. On the contrary, in the single time point group analysis, structural feature-based classification performed best (median error rate: 0.012). Conclusions: Validation of our results with a larger patient cohort may have significant clinical importance in optimising imaging surveillance and clinical decision making for patients with HGG.

Well-being, Smartphone Sensors, and Data from Open-access Databases: A Mobile Experience Sampling Study

We investigated fluctuations of well-being by using a smartphone-based mobile experience sampling method (real-time and multiple time point measurements in the field using smartphones). Moreover, temperature, longitude, latitude, altitude, wind speed, rainfall, and further environment-based indicators were included as predictors either from smartphone sensors or from open-access Internet databases. Overall, a total of 213 participants reported on their well-being (over 14 days; three measurements per day; 8,000+ well-being judgments). We were able to replicate and refine past research about the dynamics of well-being fluctuations during the day (low in the morning, high in the evening) and over the course of a week (low just before the beginning of the week, highest near the end of the week). We also show what kind of benefits empirical researchers can gain for their research using smartphones and their built-in sensors by combining these measures with data from open-access databases.