Index of papers in PLOS Comp. Biol. that mention

**statistically significant**

Comparison of model-based predictions and real neuronal responses | We measured the minimum latency of acoustic pulse train responses (see Methods), and found a statistically significant difference between synchronized and non-synchronized neurons, within both our simulated and real neuronal populations |

Comparison of model-based predictions and real neuronal responses | We observed a statistically significant difference between synchronized and non-synchronized responses in both simulated and real neurons (Fig. |

Comparison of model-based predictions and real neuronal responses | This difference was not due to a slightly longer latency response in non-synchronizing neurons, as we also observed a statistically significant difference between synchronized and non-synchronized neurons when the time window used to calculate the onset discharge rate was lengthened to 100 ms |

Data analysis | A synchronized neuron was required to have statistically significant vector strength at the longest IPI tested (Rayleigh statistic> 13.8, P<0.001, at IPI = 75ms). |

Data analysis | Values of the Rayleigh statistic greater than 13.8 were considered statistically significant (P < 0.001) [25]. |

Methods). | While non-synchronized neurons had a slightly higher pure tone evoked response to pure tones than synchronized neurons, this difference was not statistically significant (Wil-coxon rank sum test, P = 0.58, uncorrected). |

Methods). | Although we only observed a statistically significant difference between synchronized and mixed response neurons for the stimulus synchronization limit and not maximum vector strength, this may be due to the limited number of mixed response neurons that we were able to record from |

Methods). | While for simulated neurons with the same excitatory input strength, the stimulus synchronization limit of synchronized neurons decreased as the I/E ratio increased (P<0.001, Spearman correlation coefficient), we did not observe a statistically significant trend between the stimulus synchronization limit and I/ E ratio in mixed response neurons (P>0.05, Spearman correlation coefficient). |

Model parameters underlying rate and temporal representations | 4b) between the excitatory input strength and the Rayleigh statistic, the criterion we used to measure the statistical significance of stimulus-synchronization. |

Model parameters underlying rate and temporal representations | We observed a statistically significant correlation (r = 0.87, P< 1.5 X 10'”, Spearman Correlation, Fig. |

Responses to pulse trains in real and simulated cortical neurons | A synchronized neuron was required to have statistically significant vector strength at the longest IPI tested (Rayleigh statistic> 13.8, P<0.001, at IPI = 75ms) [25]. |

statistically significant is mentioned in 11 sentences in this paper.

Topics mentioned in this paper:

- simulated neurons (29)
- computational model (20)
- excitatory input (16)

2. Mining Host-Pathogen Protein Interactions to Characterize Burkholderia mallei Infectivity Mechanisms

Characteristics of host proteins interacting with known B. mal/ei virulence factors | Table 2 shows that these virulence factors interacted with a statistically significant number of human proteins that were associated with 1) protein ubiquitination and ubiquitin ligase activity, 2) vesicle organization, and 3) protein complexes located in the cytoskeleton, in lysosomes, and in the nuclear lumen. |

Characteristics of host proteins interacting with known B. mal/ei virulence factors | The other 11 connected components consisted of five or fewer proteins, an observation that was not statistically significant from a random selection of proteins (data not shown). |

Gene set functional enrichment analyses | We retained only annotations that were enriched at an FDR control level of 0.05, i.e., there is a less than 5% chance that the obtained p-values are not statistically significant . |

Gene set functional enrichment analyses | We evaluated whether the observed IMt interaction modules were statistically significant as follows. |

Human-B. mal/ei interactions and their effect on the crosstalk between different biological processes | The largest statistically significant interaction module, represented by red stars, contained proteins previously identified as bacterial targets vital for host actin cytoskeleton rearrangement, e.g., membrane-associated small GTPases (CDC42 and RALA), Filamin-A (FILA), and Rho GDP-dissociation inhibitor (ARHGDIB) [2, 33]. |

Introduction | Using the HPIA algorithm, we identified a statistically significant number of functionally similar host-pathogen interactions between these three PPI datasets. |

Putative B. mallei virulence factors improve characterization of B. mallei targets | We identified 75 statistically significant interaction modules whose GO biological process annotations largely overlapped with the ones identified for interacting partners of known virulence factors only. |

Putative B. mallei virulence factors improve characterization of B. mallei targets | Although the number of statistically significant interaction modules was smaller than above (an increase in the number of host proteins dilutes the enrichment), the addition of new host proteins increased the size (in terms of proteins and interactions) of previously identified interaction modules (S4 Table). |

Putative B. mallei virulence factors improve characterization of B. mallei targets | We identified two statistically significantly enriched host pathways: bacterial invasion of epithelial cells and focal adhesion. |

Summary | pestis and human-S. enterica and identified a statistically significant number of aligned interactions. |

Topological properties of human proteins interacting with B. mal/ei in the human PPI network | We evaluated whether the observed values for each of the five properties were statistically significant as follows. |

statistically significant is mentioned in 20 sentences in this paper.

Topics mentioned in this paper:

- statistically significant (20)
- cytoskeleton (19)
- PPI network (11)

Flexibility peaks are localized at tandem repeats inside 3’UTR regions | This is not coherent with 1:2:1 ratio distribution of the yeast genome, making the difference statistically significant for the converging regions (Fisher test: |

Flexibility peaks map on polyadenylation signals | We identified, as expected, a TATATATATATATATATGTATAT motif (MEME statistical significance E-value = 4.6 X 10—585) in 145 peaks and a ATTATTAT-TATTATTATTATTATTATT motif (MEME statistical significance E-value = 3.7 x 10—119) in 32 of them. |

Flexibility peaks map on polyadenylation signals | peak regions, comprehensive of additional 100m upstream and downstream), we found that in 183 sites the novel A/T-rich motif CTTCTTTTCTTC (MEME statistical significance E-function since it again occurs in all interORF peak regions. |

Statistical analysis | The statistical significance of properties and classifications has been assessed by means of Fish-er’s exact test and t-test. |

Statistical analysis | Differently, When external classifications have been used, statistical significance has been imported With the results. |

Statistical analysis | As stated by the authors in [26] , MEME usually finds the most statistically significant (low E-Value) motifs first. |

statistically significant is mentioned in 6 sentences in this paper.

Topics mentioned in this paper:

- mRNA (16)
- gene expression (9)
- transcriptional (8)

Abstract | However for the rest of mutations outside of the active site we observed a weak yet statistically significant positive correlation between thermal stability and catalytic activity indicating the lack of a stability-activity tradeoff for DHFR. |

Comparison with other methods | PopMusic shows also strong performance with highly statistically significant 1’ = 0.55 between theory and experiment, however the limitation of this method is that it can consider only single point mutations. |

Discussion | A straightforward explanation for the weak yet statistically significant positive correlation between activity and stability observed in our case might be that more stable proteins have greater effective concentration of the folded (i.e. |

Discussion | It is also important to note that a weak yet statistically significant positive correlation between activity and stability for DHFR can be revealed only when stabilizing mutations are included in the analysis. |

Discussion | Our earlier study [48] analyzed a smaller set of primarily destabilizing mutants and did not reveal any statistically significant trend (positive or negative) in the stability-activity relation for DHFR. |

Experimental characterization of predicted mutants | Given that statistically most random mutations are destabiliz-ing with only a small fraction (less than 18%) stabilizing [8,12], this statistically significant result (p = 0.002 under the null hypothesis that mutations are random) indicates that MCPU is an effective method for selecting stability-enhancing mutations. |

statistically significant is mentioned in 6 sentences in this paper.

Topics mentioned in this paper:

- Monte Carlo (14)
- point mutations (14)
- destabilizing (11)

OOPPCOOPC. | However, if the COOP is ever found to be statistically significantly greater than COOPC, it will be essential to reevaluate the applicability of the parameter. |

OOPPCOOPC. | If the error in the system is so large that there is no statistically significant difference between the boundaries (COOPu and COOPC), it would not be possible to differentiate between the regions. |

OOPPCOOPC. | To estimate the error and sample size, we calculated the propagation of error in COOPu and COOPC, and used them in the student t-test to calculate statistical significance . |

Supporting Information | Statistical significance at p< 0.05, With OOP error of O'OOP = |

statistically significant is mentioned in 5 sentences in this paper.

Topics mentioned in this paper:

- sample size (12)
- standard deviation (9)
- cytoskeleton (7)

Acknowledgments | We would like to thank Matthew Stephens for advice about evaluating statistical significance and multiple hypothesis correction. |

Author Summary | In this paper, we improve on a method used to identify cycling time series by better estimating the statistical significance of periodic patterns and, in turn, by searching for a Wider range of patterns than traditionally investigated. |

Overview | In this case, each time point is a different group, and ANOVA is equivalent to testing for any statistically significant variation across the time points. |

Overview | Because eXpression measurements are averages over many cells and different time points come from different samples (as the measurement is destructive), only synchronized, consistent variation across all samples can generate a statistically significant trend. |

statistically significant is mentioned in 4 sentences in this paper.

Topics mentioned in this paper:

- time series (80)
- ANOVA (39)
- p-value (21)

Discussion | Similarly, our models predicted that IgG4 has a negative impact on functional level, and an analogous depletion experiment did exhibit this trend across 2 different vaccine regimens, although the increase in activity in the RV144 samples when IgG4 was depleted did not meet statistical significance [23]. |

Supervised learning: Classification | Thus we see, for example, that the two dominant and statistically significant (at an unadjusted 0.05 level) contributors to predicting ADCP class are IgG1.gp120 and IgG3.p24, capturing both key subclasses with two different antigen specificities. |

Supervised learning: Classification | While not achieving statistically significant confidence in the coefficient value, negative contributions from IgG2 were also observed, consistent with the unsupervised analysis and the reduced ability of this subclass to bind to FcyR on phagocytes presumably due to blocking (i.e., preferred binding of antibodies with better affinity). |

statistically significant is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- cytokine (16)
- principal components (16)
- machine learning (13)

ANG-2 and PIGF survival analysis on breast cancer patients | Interestingly, we observed that the same analysis repeated for patients with high and low levels of ANG-2 and CD14 or PIGF and CD14 (and not for the remaining gene) resulted on p-values not statistically significant (0.0587 and 0.521 respectively, Fig. |

Abstract | Finally, the re-lapse-free survival analysis showed a statistically significant difference between patients with tumors with high and low expression values for genes encoding transitioning proteins detected in silico and validated on patient TEM. |

Statistical analysis and data treatments | A p value < 0.05 was considered statistically significant . |

statistically significant is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- regulatory network (22)
- cytokine (20)
- breast cancer (16)

Discussion | The power of a statistical test generally depends on three factors: first, the sample size; second, statistical significance as measured by the threshold p-value used to assess significance; and third, the effect size, which quantifies departures from the null hypothesis. |

Power calculation for fixed non-neutral model parameters | For the LOGS metacommunity, and when the local dynamics are strictly neutral (7/ = 0 for model HL or c = 0 for model PC), the models are equivalent to the SNM, and the power is equal to the threshold p-value for statistical significance (0.05 in our study). |

Testing the neutral null model | The neutral model is rejected if the p-value is less than the chosen threshold for statistical significance , which we take to be 0.05. |

statistically significant is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- model parameters (16)
- null model (9)
- sample size (8)

Quantifying accuracy and detecting functional connections | We then consider the effect of the presynaptic input to be statistically significant when the ZZ-test gives p < 0.05. |

Quantifying accuracy and detecting functional connections | In evaluating statistical significance , we limit our analysis to the pairwise models and use the un-cross-validated likelihood-ratio test. |

input experiments. | Comparison of the distributions obtained using the observed vs shuffled spike trains allows us to test whether an input has a statistically significant effect on the firing of the postsynaptic neuron (see Methods). |

statistically significant is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- synaptic inputs (32)
- functional connectivity (26)
- spike trains (19)