In recent years, concern has grown about the inappropriate application and interpretation of P values, especially the use of P<0.05 to denote "statistical significance" and the practice of P-hacking ...to produce results below this threshold and selectively reporting these in publications. Such behavior is said to be a major contributor to the large number of false and non-reproducible discoveries found in academic journals. In response, it has been proposed that the threshold for statistical significance be changed from 0.05 to 0.005. The aim of the current study was to use an evolutionary agent-based model comprised of researchers who test hypotheses and strive to increase their publication rates in order to explore the impact of a 0.005 P value threshold on P-hacking and published false positive rates. Three scenarios were examined, one in which researchers tested a single hypothesis, one in which they tested multiple hypotheses using a P<0.05 threshold, and one in which they tested multiple hypotheses using a P<0.005 threshold. Effects sizes were varied across models and output assessed in terms of researcher effort, number of hypotheses tested and number of publications, and the published false positive rate. The results supported the view that a more stringent P value threshold can serve to reduce the rate of published false positive results. Researchers still engaged in P-hacking with the new threshold, but the effort they expended increased substantially and their overall productivity was reduced, resulting in a decline in the published false positive rate. Compared to other proposed interventions to improve the academic publishing system, changing the P value threshold has the advantage of being relatively easy to implement and could be monitored and enforced with minimal effort by journal editors and peer reviewers.
Much has been written about "rank and yank," a management technique centered on annually pruning a workforce of low performers to raise average levels of organizational performance over time. ...Companies using this approach identify and fire the lowest performers in their workforce and replace them with new hires. As a general workplace practice, rank and yank has been harshly criticized on practical and theoretical grounds. Although rank and yank might not be suitable for all workplace environments, this paper postulates that it could be a powerful performance-improvement approach in certain conducive workplaces and goes on to outline why call centers are one such environment. This study then examines both the performance improvement and financial returns of a 5-year simulation of rank and yank within a call-center environment. The simulation was run under an ideal condition (no voluntary turnover) and for a more typical call center (30% voluntary turnover). Annually yanking the bottom 10% resulted in significant and rapid performance and financial gains in both the baseline and the more realistic call-center-turnover scenario. The authors conclude with a discussion of the issues call center leaders and consultants should think through before proceeding with a rank and yank implementation.
Background
A number of college presidents have endorsed the Amethyst Initiative, a call to consider lowering the minimum legal drinking age (MLDA). Our objective is to forecast the effect of the ...Amethyst Initiative on college drinking.
Methods
A system model of college drinking simulates MLDA changes through (i) a decrease in heavy episodic drinking (HED) because of the lower likelihood of students drinking in unsupervised settings where they model irresponsible drinking (misperception), and (ii) an increase in overall drinking among currently underage students because of increased social availability of alcohol (wetness).
Results
For the proportion of HEDs on campus, effects of large decreases in misperception of responsible drinking behavior were more than offset by modest increases in wetness.
Conclusions
For the effect of lowering the MLDA, it appears that increases in social availability of alcohol have a stronger impact on drinking behavior than decreases in misperceptions.
GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory ...connections between them which govern the level of expression of mRNA and protein from genes. The original motivation came from our efforts to perform parameter estimation and forward simulation of the dynamics of a differential equations model of a small GRN with 21 nodes and 31 edges. We wanted a quick and easy way to visualize the weight parameters from the model which represent the direction and magnitude of the influence of a transcription factor on its target gene, so we created GRNsight. GRNsight automatically lays out either an unweighted or weighted network graph based on an Excel spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows, a Simple Interaction Format (SIF) text file, or a GraphML XML file. When a user uploads an input file specifying an unweighted network, GRNsight automatically lays out the graph using black lines and pointed arrowheads. For a weighted network, GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (positive for activation or negative for repression) and magnitude of the weight parameter. GRNsight is written in JavaScript, with diagrams facilitated by D3.js, a data visualization library. Node.js and the Express framework handle server-side functions. GRNsight’s diagrams are based on D3.js’s force graph layout algorithm, which was then extensively customized to support the specific needs of GRNs. Nodes are rectangular and support gene labels of up to 12 characters. The edges are arcs, which become straight lines when the nodes are close together. Self-regulatory edges are indicated by a loop. When a user mouses over an edge, the numerical value of the weight parameter is displayed. Visualizations can be modified by sliders that adjust the force graph layout parameters and through manual node dragging. GRNsight is best-suited for visualizing networks of fewer than 35 nodes and 70 edges, although it accepts networks of up to 75 nodes or 150 edges. GRNsight has general applicability for displaying any small, unweighted or weighted network with directed edges for systems biology or other application domains. GRNsight serves as an example of following and teaching best practices for scientific computing and complying with FAIR principles, using an open and test-driven development model with rigorous documentation of requirements and issues on GitHub. An exhaustive unit testing framework using Mocha and the Chai assertion library consists of around 160 automated unit tests that examine nearly 530 test files to ensure that the program is running as expected. The GRNsight application (
http://dondi.github.io/GRNsight/
) and code (
https://github.com/dondi/GRNsight
) are available under the open source BSD license.
We investigated the dynamics of a gene regulatory network controlling the cold shock response in budding yeast,
Saccharomyces cerevisiae
. The medium-scale network, derived from published genome-wide ...location data, consists of 21 transcription factors that regulate one another through 31 directed edges. The expression levels of the individual transcription factors were modeled using mass balance ordinary differential equations with a sigmoidal production function. Each equation includes a production rate, a degradation rate, weights that denote the magnitude and type of influence of the connected transcription factors (activation or repression), and a threshold of expression. The inverse problem of determining model parameters from observed data is our primary interest. We fit the differential equation model to published microarray data using a penalized nonlinear least squares approach. Model predictions fit the experimental data well, within the 95 % confidence interval. Tests of the model using randomized initial guesses and model-generated data also lend confidence to the fit. The results have revealed activation and repression relationships between the transcription factors. Sensitivity analysis indicates that the model is most sensitive to changes in the production rate parameters, weights, and thresholds of Yap1, Rox1, and Yap6, which form a densely connected core in the network. The modeling results newly suggest that Rap1, Fhl1, Msn4, Rph1, and Hsf1 play an important role in regulating the early response to cold shock in yeast. Our results demonstrate that estimation for a large number of parameters can be successfully performed for nonlinear dynamic gene regulatory networks using sparse, noisy microarray data.
The misuse and abuse of alcohol among college students remain persistent problems. Using a systems approach to understand the dynamics of student drinking behavior and thus forecasting the impact of ...campus policy to address the problem represents a novel approach. Toward this end, the successful development of a predictive mathematical model of college drinking would represent a significant advance for prevention efforts.
A deterministic, compartmental model of college drinking was developed, incorporating three processes: (1) individual factors, (2) social interactions, and (3) social norms. The model quantifies these processes in terms of the movement of students between drinking compartments characterized by five styles of college drinking: abstainers, light drinkers, moderate drinkers, problem drinkers, and heavy episodic drinkers. Predictions from the model were first compared with actual campus-level data and then used to predict the effects of several simulated interventions to address heavy episodic drinking.
First, the model provides a reasonable fit of actual drinking styles of students attending Social Norms Marketing Research Project campuses varying by "wetness" and by drinking styles of matriculating students. Second, the model predicts that a combination of simulated interventions targeting heavy episodic drinkers at a moderately "dry" campus would extinguish heavy episodic drinkers, replacing them with light and moderate drinkers. Instituting the same combination of simulated interventions at a moderately "wet" campus would result in only a moderate reduction in heavy episodic drinkers (i.e., 50% to 35%).
A simple, five-state compartmental model adequately predicted the actual drinking patterns of students from a variety of campuses surveyed in the Social Norms Marketing Research Project study. The model predicted the impact on drinking patterns of several simulated interventions to address heavy episodic drinking on various types of campuses.
Abstract only
A gene regulatory network (GRN) is a set of transcription factors which regulate the level of expression of genes encoding other transcription factors. The dynamics of a GRN show how ...gene expression in the network changes over time. Microarray data were obtained from the
Saccharomyces cerevisiae
wild type strain and five transcription factor deletion strains (
Δcin5
,
Δgln3
,
Δhap4
,
Δhmo1
,
Δzap1
) before cold shock at 13°C and 15, 30, and 60 minutes after cold shock (NCBI GEO Series GSE83656). Genes that showed a significant change in expression were submitted to the YEASTRACT database to determine which transcription factors regulated them and to generate a candidate GRN of 15 nodes (transcription factors) and 28 edges (regulatory relationships). The edges of this intact network were then systematically deleted one‐at‐a‐time to create a family of 28 additional networks to determine the importance of each edge in the network. We used the open source software, GRNmap (
http://kdahlquist.github.io/GRNmap/
), to model the dynamics of these GRNs. GRNmap models the change in expression for each gene as the production of mRNA minus its degradation using differential equations with a sigmoidal production function. Given published mRNA degradation rates (Neymotin et al. 2014; doi: 10.1261/rna.045104.114) and cold shock microarray data, GRNmap then estimated the production rates and expression thresholds for each gene, and regulatory weights for each edge, which denote the direction (activation or repression) and strength of the regulatory relationships. Edge weights were then visualized with the open source software GRNsight (Dahlquist et al. 2014; doi: 10.7717/peerj‐cs.85;
https://dondi.github.io/GRNsight/
). Figure
1
shows the intact GRN. Red edges indicate activation; blue edges indicate repression. Node stripes left to right indicate gene expression at 15, 30, and 60 minutes of cold shock, red is increased expression; blue is decreased. To evaluate the goodness of fit of the model, GRNmap reports the least squares error (LSE) between the model and the microarray data. The LSE was compared to the minimum theoretical least squares error (minLSE) achievable due to variation in the data, to determine the model performance between networks. In the 28 edge‐deletion networks, LSE:minLSE ratios indicated that five networks performed better than the intact network, ten networks performed about the same, and thirteen performed worse. The edge‐deletions involving the Hmo1, Msn2, and Cin5 transcription factors resulted in a poor fit of the model to data, indicating that those edges represent important regulatory relationships in the cold shock response. K‐means clustering was performed on the edge weight values from the intact network and edge‐deletion networks. An examination of the clusters showed that weight values deviated from those of the intact network for 6 of 9 edge‐deletions involving Msn2, 4 of 5 edge‐deletions involving Hmo1, and 3 of 6 edge‐deletions involving Cin5, further reinforcing their importance in the network controlling the cold shock response in yeast.
Support or Funding Information
Loyola Marymount University Rains Research Assistant Program (A.K.F.)
Figure 1
This article extends the compartmental model previously developed by Scribner et al. in the context of college drinking to a mathematical model of the consequences of lowering the legal drinking age.
...Using data available from 32 U.S. campuses, the analyses separate underage and legal age drinking groups into an eight-compartment model with different alcohol availability (wetness) for the underage and legal age groups. The model evaluates the likelihood that underage students will incorrectly perceive normative drinking levels to be higher than they actually are (i.e., misperception) and adjust their drinking accordingly by varying the interaction between underage students in social and heavy episodic drinking compartments.
The results evaluate the total heavy episodic drinker population and its dependence on the difference in misperception, as well as its dependence on underage wetness, legal age wetness, and drinking age.
Results suggest that an unrealistically extreme combination of high wetness and low enforcement would be needed for the policies related to lowering the drinking age to be effective.
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