Index of papers in April 2015 that mention

**standard deviation**

Data collection and preprocessing | Background signal level was derived from the values for that feature among placebos, as the placebo mean plus one standard deviation . |

Data collection and preprocessing | Finally, the vaccinee values for the feature were scaled and centered to a mean of 0 and a standard deviation of 1, with values truncated to 60. |

Supervised learning: Classification | Nonetheless, even with a rigorous 200-replicate fivefold cross-validation, a mean AUC of 0.83 ( standard deviation of 0.10) was observed, indicating that antibody features are highly and robustly predictive of high vs. low ADCP activity. |

Supervised learning: Classification | Despite the reduction in data considered, Fig 3C and 3D shows that the resulting performance with the filtered feature set is comparable to that with the complete feature set, with a mean AUC of 0.84 ( standard deviation 0.10). |

Supervised learning: Classification | We found that on average an AUC of 0.79 was obtained, with a range from 0.67 to 0.87 and a standard deviation of 0.04 (recall that the PCC-based approach obtained 0.84). |

Supervised learning: Regression | The models are clearly predictive of ADCP, obtaining a mean Pearson correlation coefficient PCC = 0.64 ( standard deviation 0.15) over the 200-replicate fivefold. |

Supervised learning: Regression | Models learned from the filtered features from Fig 2B maintain about the same accuracy (mean PCC = 0.61 with standard deviation 0.15 for the 200-replicate fivefold (Fig 4D); an example LOOCV scatterplot is illustrated in Fig 4C). |

Supervised learning: Regression | Similarly, PCA-based models attain mean PCC of 0.61 With standard deviation 0.15 (Fig 4E and 4F), based largely on PC2 (1gG2/4 vs. 1/3) and someWhat on PCS (IgG4 vs. others), as can be seen in Fig 41. |

standard deviation is mentioned in 11 sentences in this paper.

Topics mentioned in this paper:

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

D Perfect | To understand the limits of the COOP parameter, we constructed a series of cases With different organizations by truncation of Gaussian distributions With specified standard deViations (Fig. |

Discussion | For example, the standard deviation is not an appropriate metric for quantifying orientation distribution of Z-lines as they are not distributed normally. |

Experimental Data | To calculate the average angle between the constructs ((60)) and the standard deviation of those angles (060) across multiple conver-slips, it is essential to keep in mind that the angle period is 71. |

Experimental Data | The (90) and 090 are the average and standard deviation of 90,,- for all cover-silps. |

Introduction | For example, some assume that the parameter can be described as the standard deviation of a truncated Gaussian, or normal, distribution [12]. |

OOPPCOOPC. | Assuming OOPp and OOPQ are normally distributed with the standard deviation UOOPP and O'OOPQ, respectively, and OOPP and OOPQ are independent, then the variance: and the standard deviation: |

OOPPCOOPC. | formulated the estimate for the the standard deviation of COOPC: |

Supporting Information | For (AD) schematic of the construct is on the left, and the orientation distribution With the GOP and standard deviation is on the right. |

Synthetic Data | 3) contained 108 random numbers (MATLAB function normrnd) that were normally distributed With the specified mean and standard deviation . |

standard deviation is mentioned in 9 sentences in this paper.

Topics mentioned in this paper:

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

Intervention target validation | For our test cases, we find that using 10,000 time steps for each evolution stage (with and then without prescribed node states) is enough to preserve the first three digits of the estimated probabilities p Am of reaching the attractor of interest, consistent with what is expected from the standard deviation of the estimated probability p Am. |

Intervention target validation | For our test cases, we find that 100,000 initial conditions are enough to estimate the probabilities p Am of reaching the attractor of interest with an error ( standard deviation of the estimated probability p Am) of 3' 10'3 [p Attr(1—p Attr)] 1/ 2. |

Intervention target validation | The number of time steps we use is enough to show no changes in p Am beyond what is expected from the standard deviation of the estimated probability p Am, and is also found to be enough for the initial conditions to reach the attractors when no interventions are applied. |

Supporting Information | The percentages are significant in the digits shown and have an estimated absolute error ( standard deviation of the mean) of 3'10_3[% |

Supporting Information | The percentages are significant in the digits shown and have an estimated absolute error ( standard deviation of the mean) of 3-10—3[%pAttr(1OO%—%pAttr)] “2 %, Where %pAttr is the percentage shown (e.g. |

Supporting Information | The percentages are significant in the digits shown and have an estimated absolute error ( standard deviation of the mean) of 6- 10_3[%pAttr(100%—% pAttr)]1/2 %, Where %pAm is the percentage shown (e.g. |

standard deviation is mentioned in 9 sentences in this paper.

Topics mentioned in this paper:

- T cell (26)
- network model (20)
- initial conditions (18)

4. Transcriptional Dynamics Reveal Critical Roles for Non-coding RNAs in the Immediate-Early Response

Definition of kinetic signatures | Bayesian evidence values and model parameter estimates (and their standard deviations ) are computed using nested sampling for all signatures for each time series. |

Definition of kinetic signatures | CAGE clusters are assigned to one of the exponential kinetic signatures if log Z for that signature is greater than 10 times log Z for the linear model and log Z minus its standard deviation (sd) is greater than log Z plus the estimated sd for any other eXponential signature (nested sampling computes log Z for parameters mapped to O..1 and we used the resulting log Z for the unit cube for model comparison). |

Results | A cluster was assigned to a ‘no decision’ category if the values of log Z (and the associated standard deviations ) computed for each model did not permit a clear assignment. |

Supporting Information | CAGE TPM values are plotted as circles (median value is filled), predictions of the kinetic signature models using parameter means are shown in blue and the vertical green lines indicate the mean t5 (or th in the case of the decay signature) and one standard deviation above and below. |

Supporting Information | CAGE TPM values are plotted as circles (median value is filled), predictions of the kinetic signature models using parameter means are shown in blue and the vertical green lines indicate the mean ts and one standard deviation above and below. |

peak category. | CAGE TPM values are plotted as circles (median value is filled), predictions of the kinetic signature models using parameter means are shown in blue and the vertical green lines indicate the mean ts and one standard deviation above and below. |

peak category. | CAGE TPM values are plotted as circles (median value is filled), predictions of the kinetic signature models using parameter means are shown in blue and the vertical green lines indicate the mean ts and one standard deviation above and below. |

peak category. | Expression values are plotted as circles (median value is filled), predictions of the model using parameter means are shown in blue and the vertical green lines indicate the mean th (or ts) and one standard deviation above and below. |

standard deviation is mentioned in 8 sentences in this paper.

Topics mentioned in this paper:

- time course (23)
- time series (19)
- transcription factors (15)

Coupled Stuart-Landau/Kuramoto model parameters | For both models, the natural frequencies of the oscillators in our simulation are given as a Gaussian distribution to simulate alpha with mean at 10 Hz and standard deviation 1, making wj around 10211 rad/ s. Time delay is (a) given an identical value between 2ms and 50 ms for all edges (for model networks as well as Gong et al.’s and Hagmann et al.’s human brain networks), or (b) given proportional to the physical distances for each edges with propagation speed of between 5 to 10m/ s (for Gong et al.’s human brain network) [60, 61]. |

Coupled Stuart-Landau/Kuramoto model parameters | For all simulations, we also added a Gaussian white noise @(t) of vanishing mean and standard deviation of 2 to each oscillator's equation to test the robustness of our results against random fluctuations. |

Identification of mathematical relationships among node degree, amplitude of local oscillations and directionality of interactions | The natural frequency of each node was randomly drawn from a Gaussian distribution with the mean at 10 Hz and standard deviation of 1 Hz, simulating the alpha bandwidth (8-13HZ) of human EEG, and we systematically varied the coupling strength 8 from 0 to 50. |

Identification of mathematical relationships among node degree, amplitude of local oscillations and directionality of interactions | Fig 2A shows the mean phase coherence (measure of how synchronized the oscillators are; see Materials and Methods for details) [42] for two groups of nodes in the network: 1) hub nodes, here defined as nodes with a degree above the group standard deviation (green triangles, 8 out of 78 nodes); and 2) peripheral nodes, here defined as nodes with a degree of 1 (yellow circles, 33 out of 78 nodes). |

Supporting Information | Each plot shows distinct local dynamics for hub nodes (darker green diamond: defined as nodes With degree above the group standard deviation), and peripheral nodes (lighter green square: defined as nodes With degree 1 for scale-free network, and as nodes With degree below the group standard deviation for other networks) as coupling strength S is varied. |

Supporting Information | Nodes are indexed in decreasing order of their degree; the degree distribution graph is red if the node degree is less than the average degree of the network, green if it is more than the average, and blue if it is higher than one standard deviation from the average. |

standard deviation is mentioned in 6 sentences in this paper.

Topics mentioned in this paper:

Robustness of DTT | When the standard deviation is increased upto 50% of the mean value of the ACTX, both pre-DTT and post-DTT areas decreased only by 20% (Supplementary Sl Fig). |

Supporting Information | The instantaneous input rate was choosen from a low pass filtered noise (time constant 2 5 msec) and standard deviation of :05 around the mean. |

Supporting Information | The mean of the input rate was varied every 500 msec while the standard deviation remained fixed. |

Supporting Information | D1 MSNs received additional input with a mean of 1 HZ and standard deviation 0 — A — ctxl. |

standard deviation is mentioned in 5 sentences in this paper.

Topics mentioned in this paper:

- firing rates (89)
- basal ganglia (21)
- feedforward (15)

Parameter estimation | We recalculated the standard deviation (0) from the cvar and the mean, such that a = y-cvar/ 100. |

Results | The thick dark blue line indicates the average cell size in the population and the light-blue area the range of one standard deviation (SD). |

Supporting Information | The thick dark blue line indicates the average cell size in the population and the light-blue area the range of one standard deviation (SD). |

standard deviation is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- growth rate (46)
- cell cycle (35)
- budding yeast (16)

Statistics | Numbers and error bars indicate average i standard deviation . |

Supporting Information | The figures give the squared coefficient of variation ( standard deviation normalized to the mean) for each conductance/flux parameter during the optimization process, averaged for the 10 GA runs. |

The combined stochastic current and multi-step voltage clamp protocol improves parameter estimation | Running the optimization with the combined objective does indeed lead to improved accuracy of the parameter estimation, with all nine current parameters being recovered to within one standard deviation (orange symbols, Fig 4B). |

standard deviation is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- action potential (38)
- experimental data (21)
- parameter estimation (19)