Journal: Biotechnology for Biofuels

Article Title: How recombinant swollenin from Kluyveromyces lactis affects cellulosic substrates and accelerates their hydrolysis

doi: 10.1186/1754-6834-4-33

Figure Lengend Snippet: Influence of crystallinity and mean particle size on hydrolysis of cellulosic substrates . Data points for cellulosic substrates were obtained from Figure 5 (mean particle size), Figure 6 (crystallinity index) and Figure 7 (initial hydrolysis rate from 0 to 6 h). TableCurve 3D was used to determine an empirical surface fit ( R 2 = 0.93) based on a non-linear Gaussian cumulative function.

Article Snippet: TableCurve 3D 4.0 (Systat Software, San Jose, CA, USA) was used to empirically correlate CrI and mean particle size with initial hydrolysis rates via the non-linear Gaussian cumulative function: (3) z = G C U M X ( a , b , c ) + G C U M Y ( d , e , f ) + G C U M X ( g , b , c ) ⋅ G C U M Y ( 1 , e , f ) in which a, b, c, d, e, f and g denote the various fitting parameters of the non-linear Gaussian cumulative function (-).

Techniques:

Journal: Proceedings of the Royal Society B: Biological Sciences

Article Title: Do perceptual biases emerge early or late in visual processing? Decision-biases in motion perception

doi: 10.1098/rspb.2016.0263

Figure Lengend Snippet: Sensory re-weighting and late, decision-related bias models. (a) Outline of the sensory re-weighting model. The sensory representation of the moving stimulus is modelled as a Gaussian probability density function (𝒩) centred on the true direction of motion (μ) and variance σd, a free parameter for both models. The weighting function w is modelled as a gamma probability function (Γ) governed by two parameters: a shape parameter (A) and a scale parameter (B). Both s and w are derived in the same way for both the sensory re-weighting and late, decision-related bias models. The sensory representation of the motion stimulus is multiplied by the weighting profile, resulting in the weighted sensory representation (wsr). To fit these models to the data, we obtained the (Gaussian) maximum-likelihood estimates for σd, A, B (and δ, for the modified model). (b) Model predictions for sensory re-weighting model. For both situations in our Experiment 1, reference present or absent during the estimation task, the model predicts the same biased responses, as the re-weighting of sensory information is tied to discrimination boundary which is unchanged in both situations. For the same reason, the original model predicts the same responses for Experiment 2, where the position of the reference is systematically changed at the time of the estimation task. (c) Outline of the late, decision-related bias model. The early sensory representation s remains unchanged. The re-weighting of the sensory information by a weighting function w is dependent on the presence of an explicit reference during the estimation task. In addition, the re-weighting is relative to the position of one or more references at the time of estimation. The parameter δ can absorb differences between the position of the discrimination boundary (during stimulus presentation) and the reference during the manual estimation task and correctly predicts shifts in the responses (from r to r'). (d) Model predictions for the late, decision-related bias model. In the situations where a reference is present during the estimation task (and not shifted with respect to the discrimination boundary), the original and modified models make the same predictions (light grey lines, left panel). However, if no reference is present at the estimation stage, the model predicts veridical responses (dark grey line, left panel). Additionally, if the reference present during estimation is displaced relative to the decision boundary, the modified model predicts concomitant changes in the responses (dashed and solid light grey lines, right panel).

Article Snippet: We used nonlinear least-squares ( fminsearch in Matlab; Nelder–Mead algorithm [ 9 ]) to estimate the two parameters ( μ , σ ) of the best-fitting cumulative Gaussian distribution.

Techniques: Derivative Assay, Modification

Journal: BMC Veterinary Research

Article Title: Wild-type cutoff for Apramycin against Escherichia coli

doi: 10.1186/s12917-020-02522-0

Figure Lengend Snippet: Definitions of the terminology used in this study

Article Snippet: To confirm the presence of more than one MIC distribution, frequency distributions of MIC data were analyzed by nonlinear least squares regression analysis based on the following Cumulative Gaussian Counts equation: Z = ((X-Mean))/SD, Y=N*zdist(z) according to the previous study [ ], in which the Mean is the average of the original distribution, from which the frequency distribution was created; SD is the standard deviation of the original distribution (calculated by Graphpad prism 6.0 software, San Diego, CA).

Techniques: In Vitro