Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Postsecondary STEM Paths of High-Achieving Students in Math and Science: A Longitudinal Multilevel Investigation of Their Selection and Persistence

This study used a quantitative approach to investigate high-school students’ talent-development pathways in STEM from 10th through 12th grades and for 8 years thereafter. The purpose of this study was to longitudinally investigate three important choices and accomplishments on the STEM talent development trajectory: a) selecting a STEM major in college; b) persisting with the STEM major until graduation; and c) selecting a career in STEM after college graduation. Given that students with gifts and talents are more likely to persist and succeed in STEM fields than average achievers, and understanding their unique needs may be the first important task to promote their talent and career development, this study concentrated on college bound high school students who achieve at high levels in math and science. I operationally defined students identified as high-achieving in math and science as those who scored in the 95th percentile or above in math or science in college entrance exams. Through an investigation, I used the longitudinal data of the Education Longitudinal Study of 2002 (ELS:2002) of a nationally-representative cohort of U.S. students. Two inferential analytic methods were used to estimate the probabilities associated with each binary outcome variable: multilevel logistic regression model and discrete-time hazard model. Students identified as high-achieving by the criteria of this study were more likely than students who did not meet the criteria to enter postsecondary STEM education and to persist in STEM after college graduation. However, there were severe disproportions in the numbers of students identified as college bound high-achievers. Female, Black, Hispanic, Native American, and other-race students, students from families of lower-quartile SES, and students who attended schools with higher levels of academic pressure were less likely to be identified as high-achievers than students in the corresponding reference groups. Mathematics self-efficacy and advanced courses in math and science, as moderators, increased the probabilities of STEM entrance, regardless of the identification as high-achieving. In terms of STEM persistence and graduation, fewer Black, Hispanic, Native American, and other race students graduated from college with a STEM major compared to White and Asian students. The disparities in the probabilities of further persistence also existed by student-and school-level covariates. Unlike prior studies in STEM education, I controlled for the effects of high achievement in college entrance exams, thus, the results revealed the effects of some covariates were unique for students identified as high-achieving. Based on the baseline estimates of probabilities provided by this study, more research needs to be conducted to investigate reasons for the significant effects promoting or preventing desirable outcomes on STEM pathways

Data_Sheet_1_Mediating roles of college teaching self-efficacy in job stress and job satisfaction among Chinese university teachers.pdf

Colleges and universities have been experiencing high rates of faculty turnover across countries, and hiring and retaining influential faculty members is a constant challenge that higher education institutions have encountered. Job stress and job satisfaction are stable predictors that psychologically determine teachers’ persistence in their institutions. The present study aimed to extend understanding of a mediating effect of college teaching self-efficacy (CTSE) on the relationship between faculty job stress and job satisfaction. Data collected from 455 Chinese university teachers were analyzed using structural equation moderated mediation models. CTSE was an effective mediator in alleviating the negative relationship between job stress and job satisfaction. Our finding from a moderated mediation model suggests that the mediation effect of CTSE did not differ by teaching experience, ranks, gender, and workload. However, the significant covariate effect of teaching experience incorporated in the mediation effect implies that teachers with more teaching experiences may have greater teaching self-efficacy, which may positively change the perceptions of job stress and job satisfaction. By way of discussion, we provided evidence regarding current trends and underlying psychological reasons for university teachers’ dissatisfaction which might be useful for educators, university administrators, and policymakers framing policy and institutional decisions. Some impractical implications are further discussed.</p