From 15b1e0688812c2a29da63bb69dd8ec9ee631f828 Mon Sep 17 00:00:00 2001 From: "W. Trevor King" Date: Thu, 27 Jun 2013 15:54:39 -0400 Subject: [PATCH] Add prefixes to sort the nomenclature appropriately In order: * Operators and functions * Greek symbols * Roman symbols * Text I also added a bunch of trailing periods, and ensured that the \nomenclature{} calls were set off from paragraphs by a `%`. --- src/apparatus/cantilever-calib.tex | 17 ++--- src/apparatus/polymer-synthesis.tex | 66 ++++++++++--------- src/apparatus/procedure.tex | 6 +- src/apparatus/sample-preparation.tex | 5 +- src/blurb/abstract.tex | 6 +- src/calibcant/discussion.tex | 2 +- src/calibcant/overview.tex | 16 ++--- src/calibcant/theory.tex | 58 +++++++++-------- src/cantilever-calib/contour_integration.tex | 8 ++- src/cantilever-calib/integrals.tex | 5 +- src/future/software.tex | 4 +- src/hooke/history.tex | 9 +-- src/hooke/plugins.tex | 5 +- src/introduction/main.tex | 9 +-- src/pyafm/auxiliary.tex | 8 +-- src/pyafm/frameworks.tex | 17 ++--- src/pyafm/stack.tex | 6 +- src/salt/main.tex | 2 +- src/sawsim/discussion.tex | 68 +++++++++++--------- src/sawsim/methods.tex | 64 +++++++++--------- 20 files changed, 205 insertions(+), 176 deletions(-) diff --git a/src/apparatus/cantilever-calib.tex b/src/apparatus/cantilever-calib.tex index 4fb942e..35dad7b 100644 --- a/src/apparatus/cantilever-calib.tex +++ b/src/apparatus/cantilever-calib.tex @@ -11,9 +11,9 @@ deflection via Hooke's law,\index{Hooke's law} where $x$ is the perpendicular displacement of the cantilever tip ($x_c$ in \cref{fig:unfolding-schematic}). % -\nomenclature{$F$}{Force (newtons)} -\nomenclature{$\kappa$}{Spring constant (newtons per meter)} -\nomenclature{$x$}{Displacement (meters)} +\nomenclature[sr ]{$F$}{Force (newtons).} +\nomenclature[sg k ]{$\kappa$}{Spring constant (newtons per meter).} +\nomenclature[sr ]{$x$}{Displacement (meters).} The basic idea is to use the equipartition theorem, which gives the thermal energy per degree of freedom. For a simple harmonic @@ -26,14 +26,15 @@ where $k_B$ is Boltzmann's constant, $T$ is the absolute temperature, and $\avg{x^2}$ is the average value of $x^2$ measured over a long time interval. % -\nomenclature{$k_B$}{Boltzmann's constant, - $k_B = 1.380 65\E{-23}\U{J/K}$\cite{codata-boltzmann}} -\nomenclature{$T$}{Absolute temperature (Kelvin)} -\nomenclature{$\avg{s(t)}$}{Mean (expectation value) of a time-series $s(t)$ +\nomenclature[sr ]{$k_B$}{Boltzmann's constant, + $k_B = 1.380 65\E{-23}\U{J/K}$\citep{codata-boltzmann}.} +\nomenclature[sr ]{$T$}{Absolute temperature (Kelvin).} +\nomenclature[o ]{$\avg{s(t)}$}{Mean (expectation value) of a + time-series $s(t)$ \begin{equation} \avg{A} \equiv \iLimT{A} \;. \end{equation}} -\nomenclature{$\equiv$}{Defined as (\ie\ equivalent to)} +\nomenclature[o ]{$\equiv$}{Defined as (\ie\ equivalent to).} To calculate the spring constant $\kappa$ using \cref{eq:equipart}, we need to measure the buffer temperature $T$ and the thermal vibration diff --git a/src/apparatus/polymer-synthesis.tex b/src/apparatus/polymer-synthesis.tex index ecb511b..a2795c0 100644 --- a/src/apparatus/polymer-synthesis.tex +++ b/src/apparatus/polymer-synthesis.tex @@ -40,12 +40,13 @@ to the substrate, van der Waals forces and non-specific binding with the surface dominate the tip-surface interaction. In order to increase the tip-surface distance while preserving single molecule analysis, \citet{carrion-vazquez99b} synthesized a protein composed of -eight repeats of immunoglobulin-like domain 27 (I27), one of the -globular domains from native titin (\cref{fig:I27}). Octameric I27 -produced using their procedure is now available +eight repeats of immunoglobulin-like domain 27 (I27\index{I27}), one +of the globular domains from native titin (\cref{fig:I27}). Octameric +I27 produced using their procedure is now available commercially\citep{athenaes-i27o}. -\index{I27} -\nomenclature{I27}{Immunoglobulin-like domain 27 from human titin} +% +\nomenclature[text ]{I27}{Immunoglobulin-like domain 27 from human + titin.} \begin{figure} \includegraphics[width=2in]{figures/i27/1TIT} @@ -75,8 +76,8 @@ eventual plasmid vector has the eight I27s and a host-specific promoter that causes the bacteria to produce large quantities of I27. The exact structure of the generated octamer is\citep{carrion-vazquez99b} -\nomenclature{cDNA}{Complementary DNA} -\nomenclature{PCR}{Polymerase chain reaction} +\nomenclature[text ]{cDNA}{Complementary DNA.} +\nomenclature[text ]{PCR}{Polymerase chain reaction.} \begin{center} Met-Arg-Gly-Ser-(His)$_6$-Gly-Ser-(I27-Arg-Ser)$_7$-I27-\ldots-Cys-Cys @@ -98,9 +99,10 @@ The plasmid is then transformed into the host, usually a proprietary equivalent such as Agilent's SURE 2 Supercompetent Cells\citep{agilent-sure2,carrion-vazquez00}. The infected cells are cultured to express the protein. -\nomenclature{Bacterial transformation}{The process by which bacterial - cells take up exogenous DNA molecules} -\nomenclature{Exogenous DNA}{DNA that is outside of a cell} +% +\nomenclature[text ]{Bacterial transformation}{The process by which + bacterial cells take up exogenous DNA molecules.} +\nomenclature[text ]{Exogenous DNA}{DNA that is outside of a cell.} The octamer is then purified from the culture using immobilized metal ion affinity chromatography (IMAC), where the His-tagged end of the @@ -111,25 +113,27 @@ broth has been washed out of the chromatography column, the octamer is eluted via either another molecule which competes for the metal ions\citep{ma10} or by changing the pH so the octamer is less attracted to the metal ion. -\nomenclature{IMAC}{Immobilized metal ion affinity chromatography} -\nomenclature{Ni-NTA}{Nickle nitrilotriacetic acid} +% +\nomenclature[text ]{IMAC}{Immobilized metal ion affinity + chromatography.} +\nomenclature[text ]{Ni-NTA}{Nickle nitrilotriacetic acid.} -\nomenclature{Ala}{Alanine, an amino acid} -\nomenclature{Arg}{Arginine, an amino acid} -\nomenclature{Asn}{Asparagine, an amino acid} -\nomenclature{Asp}{Aspartic acid, an amino acid} -\nomenclature{Cys}{Cystine, an amino acid} -\nomenclature{Glu}{Glutamine, an amino acid} -\nomenclature{Gly}{Glycine, an amino acid} -\nomenclature{His}{Histidine, an amino acid} -\nomenclature{Ile}{Isoleucine, an amino acid} -\nomenclature{Leu}{Leucine, an amino acid} -\nomenclature{Lys}{Lysine, an amino acid} -\nomenclature{Met}{Methionine, an amino acid} -\nomenclature{Phe}{Phenylalanine, an amino acid} -\nomenclature{Pro}{Proline, an amino acid} -\nomenclature{Ser}{Serine, an amino acid} -\nomenclature{Thr}{Threonine, an amino acid} -\nomenclature{Trp}{Tryptophan, an amino acid} -\nomenclature{Tyr}{Tyrosine, an amino acid} -\nomenclature{Val}{Valine, an amino acid} +\nomenclature[text ]{Ala}{Alanine, an amino acid.} +\nomenclature[text ]{Arg}{Arginine, an amino acid.} +\nomenclature[text ]{Asn}{Asparagine, an amino acid.} +\nomenclature[text ]{Asp}{Aspartic acid, an amino acid.} +\nomenclature[text ]{Cys}{Cystine, an amino acid.} +\nomenclature[text ]{Glu}{Glutamine, an amino acid.} +\nomenclature[text ]{Gly}{Glycine, an amino acid.} +\nomenclature[text ]{His}{Histidine, an amino acid.} +\nomenclature[text ]{Ile}{Isoleucine, an amino acid.} +\nomenclature[text ]{Leu}{Leucine, an amino acid.} +\nomenclature[text ]{Lys}{Lysine, an amino acid.} +\nomenclature[text ]{Met}{Methionine, an amino acid.} +\nomenclature[text ]{Phe}{Phenylalanine, an amino acid.} +\nomenclature[text ]{Pro}{Proline, an amino acid.} +\nomenclature[text ]{Ser}{Serine, an amino acid.} +\nomenclature[text ]{Thr}{Threonine, an amino acid.} +\nomenclature[text ]{Trp}{Tryptophan, an amino acid.} +\nomenclature[text ]{Tyr}{Tyrosine, an amino acid.} +\nomenclature[text ]{Val}{Valine, an amino acid.} diff --git a/src/apparatus/procedure.tex b/src/apparatus/procedure.tex index 0b8b7b6..b73ffd0 100644 --- a/src/apparatus/procedure.tex +++ b/src/apparatus/procedure.tex @@ -31,9 +31,9 @@ individual unfolding events are separated from each other in space and time, allowing single molecule resolution despite the use of multi-domain test proteins. % -\nomenclature{force curve}{Or force--distance curve. Cantilever-force - versus piezo extension data aquired during a force spectroscopy - experiment (\cref{fig:expt-sawtooth}).} +\nomenclature[text ]{force curve}{Or force--distance curve. + Cantilever-force versus piezo extension data aquired during a force + spectroscopy experiment (\cref{fig:expt-sawtooth}).} \begin{figure} \begin{center} diff --git a/src/apparatus/sample-preparation.tex b/src/apparatus/sample-preparation.tex index 06c9412..4e4247f 100644 --- a/src/apparatus/sample-preparation.tex +++ b/src/apparatus/sample-preparation.tex @@ -27,8 +27,9 @@ similar PBS recipes in common use\citep{florin95,carrion-vazquez00,lo01,brockwell02}, but our PBS is diluted from 10x PBS stock composed of $1260\U{mM}$ NaCl, $72\U{mM}$ \diNaHPO, and $30\U{mM}$ \NadiHPO\citep{chyan04}. +% \index{Phosphate buffered saline (PBS)} -\nomenclature{PBS}{Phosphate buffered saline} +\nomenclature[text ]{PBS}{Phosphate buffered saline.} As an alternative to binding proteins to gold, others have used EGTA\citep{kellermayer03}, @@ -38,4 +39,4 @@ the cantilever tips by coating them with molecules designed to bind to the protein\citep{lee05}. Of these, a Ni-NTA coating is the most popular\citep{schmitt00}. % -\nomenclature{EGTA}{Ethylene glycol tetraacetic acid} +\nomenclature[text ]{EGTA}{Ethylene glycol tetraacetic acid} diff --git a/src/blurb/abstract.tex b/src/blurb/abstract.tex index 4fc21be..a5540b7 100644 --- a/src/blurb/abstract.tex +++ b/src/blurb/abstract.tex @@ -10,7 +10,8 @@ the lack of transparency makes it more difficult to share data and techniques between labs, which slows progress. In some cases it can also lead to ambiguity as to which of several similar approaches, correction factors, etc.\ were used in a particular paper. -\nomenclature{SMFS}{Single molecule force spectroscopy} +% +\nomenclature[text ]{SMFS}{Single molecule force spectroscopy.} In this thesis, I introduce an SMFS sofware suite for cantilever calibration (\calibcant), experiment control (\pyafm), analysis @@ -28,5 +29,6 @@ kernel\citep{comedi}. Users running other operating systems should be able to swap in analogous low level physical-interface packages if Linux is not an option. % -\nomenclature{OS}{Operating system} +\nomenclature[text ]{OS}{Operating system.} + \end{abstract} diff --git a/src/calibcant/discussion.tex b/src/calibcant/discussion.tex index 36330d5..02633a3 100644 --- a/src/calibcant/discussion.tex +++ b/src/calibcant/discussion.tex @@ -22,7 +22,7 @@ see that the peak frequency is shifted from $f_0$ depending on the damping term $\beta_f$. For overdamped cantilevers with large values of $\beta$, the peak frequency will not have a real solution.% % -\nomenclature{$f_\text{max}$}{The frequency of the peak power in +\nomenclature[sr ]{$f_\text{max}$}{The frequency of the peak power in $\PSD_f$ (\cref{eq:peak-frequency}).} \subsection{Propagation of errors} diff --git a/src/calibcant/overview.tex b/src/calibcant/overview.tex index 1931027..50ea64a 100644 --- a/src/calibcant/overview.tex +++ b/src/calibcant/overview.tex @@ -30,9 +30,9 @@ we can gauge the relative importance errors in each parameter and calculate the uncertainty in our estimated $\kappa$ (\cref{sec:calibcant:discussion:errors}). % -\nomenclature{$V_p$}{The vertical photodiode deflection voltage +\nomenclature[sr ]{$V_p$}{The vertical photodiode deflection voltage (\cref{fig:afm-schematic,eq:x-from-Vp}).} -\nomenclature{$\sigma_p$}{The linear photodiode sensitivity to +\nomenclature[sg s_p ]{$\sigma_p$}{The linear photodiode sensitivity to cantilever displacement (\cref{fig:afm-schematic,eq:x-from-Vp}).} In order to filter out noise in the measured value of $\avg{V_p^2}$ we @@ -51,15 +51,15 @@ value for $V_p^2$ is given by \label{eq:avg-Vp-Gone-f} \end{equation} % -\nomenclature[PSDf]{$\PSD_f$}{Power spectral density in +\nomenclature[o PSDf ]{$\PSD_f$}{Power spectral density in frequency space \begin{equation} - \PSD_f(g, f) \equiv \normLimT 2 \magSq{ \Fourf{g(t)}(f) } + \PSD_f(g, f) \equiv \normLimT 2 \magSq{ \Fourf{g(t)}(f) } \;. \end{equation}} -\nomenclature{$f$}{Frequency (hertz)} -\nomenclature{$f_0$}{Resonant frequency (hertz)} -\nomenclature{$\pi$}{Archmides' constant, $\pi=3.14159\ldots$. The - ratio of a circle's circumference to its diameter.} +\nomenclature[sr ]{$f$}{Frequency (hertz).} +\nomenclature[sr ]{$f_0$}{Resonant frequency (hertz).} +\nomenclature[sg p ]{$\pi$}{Archmides' constant, $\pi=3.14159\ldots$. + The ratio of a circle's circumference to its diameter.} Combining \cref{eq:equipart_k,eq:x-from-Vp,eq:avg-Vp-Gone-f}, we have diff --git a/src/calibcant/theory.tex b/src/calibcant/theory.tex index f9b1724..8f6d19e 100644 --- a/src/calibcant/theory.tex +++ b/src/calibcant/theory.tex @@ -20,14 +20,14 @@ where $x$ is the displacement from equilibrium\index{$x$}, During the non-contact phase of calibration, $F(t)$ comes from random thermal noise. % -\nomenclature{$m$}{Effective mass of a damped harmonic oscillator +\nomenclature[sr ]{$m$}{Effective mass of a damped harmonic oscillator (\cref{eq:DHO}).} -\nomenclature{$\gamma$}{Damped harmonic oscillator drag coefficient - $F_\text{drag} = \gamma\dt{x}$ (\cref{eq:DHO}).} -\nomenclature{$\dt{s}$}{First derivative of the time-series $s(t)$ - with respect to time. $\dt{s} = \deriv{t}{s}$} -\nomenclature{$\ddt{s}$}{Second derivative of the time-series $s(t)$ - with respect to time. $\ddt{s} = \nderiv{2}{t}{s}$} +\nomenclature[sg c ]{$\gamma$}{Damped harmonic oscillator drag + coefficient $F_\text{drag} = \gamma\dt{x}$ (\cref{eq:DHO}).} +\nomenclature[o d1 ]{$\dt{s}$}{First derivative of the time-series + $s(t)$ with respect to time. $\dt{s} = \deriv{t}{s}$.} +\nomenclature[o d2 ]{$\ddt{s}$}{Second derivative of the time-series + $s(t)$ with respect to time. $\ddt{s} = \nderiv{2}{t}{s}$.} In the following analysis, we use the unitary, angular frequency Fourier transform normalization @@ -37,13 +37,13 @@ Fourier transform normalization where $\omega$ is the angular frequency and $i\equiv\sqrt{-1}$ is the imaginary unit. % -\nomenclature{\Four{s(t)}}{Fourier transform of the time-series +\nomenclature[o F ]{\Four{s(t)}}{Fourier transform of the time-series $s(t)$. $s(f) = \Four{s(t)} \equiv \frac{1}{\sqrt{2\pi}} \iInfInf{t}{s(t) e^{-i \omega t}}$. }\index{Fourier transform} -\nomenclature{$i$}{Imaginary unit $i\equiv\sqrt{-1}$.} -\nomenclature{$\omega$}{Angular frequency (radians per second).} +\nomenclature[sr ]{$i$}{Imaginary unit $i\equiv\sqrt{-1}$.} +\nomenclature[sg z ]{$\omega$}{Angular frequency (radians per second).} We also use the following theorems (proved elsewhere): \begin{align} @@ -78,12 +78,12 @@ relates to the variance by \end{align} where $t_T$ is the total time over which data has been aquired. % -\nomenclature[PSDo]{$\PSD$}{Power spectral density in angular +\nomenclature[o PSDo ]{$\PSD$}{Power spectral density in angular frequency space \begin{equation} - \PSD(g, w) \equiv \normLimT 2 \magSq{ \Four{g(t)}(\omega) } + \PSD(g, w) \equiv \normLimT 2 \magSq{ \Four{g(t)}(\omega) } \;. \end{equation}} -\nomenclature{$\abs{z}$}{Absolute value (or magnitude) of $z$. For +\nomenclature[o ]{$\abs{z}$}{Absolute value (or magnitude) of $z$. For complex $z$, $\abs{z}\equiv\sqrt{z\conj{z}}$.} We also use the Wiener--Khinchin theorem, @@ -103,11 +103,11 @@ where $r_{xx}(t)$ is defined in terms of the expectation value \end{align} and $\conj{x}$ represents the complex conjugate of $x$. % -\nomenclature{$S_{xx}(\omega)$}{Two sided power spectral density in - angular frequency space (\cref{eq:wiener_khinchin}).} -\nomenclature{$r_{xx}(t)$}{Autocorrelation function +\nomenclature[o ]{$S_{xx}(\omega)$}{Two sided power spectral density + in angular frequency space (\cref{eq:wiener_khinchin}).} +\nomenclature[o ]{$r_{xx}(t)$}{Autocorrelation function (\cref{eq:autocorrelation}).} -\nomenclature{$\conj{z}$}{Complex conjugate of $z$} +\nomenclature[o ]{$\conj{z}$}{Complex conjugate of $z$.} \subsection{Highly damped case} \label{sec:calibcant:ODHO} @@ -146,7 +146,7 @@ per unit time as = \normLimT 2 \magSq{F(\omega)} \;. \label{eq:GOdef} % label O != zero \end{equation} % -\nomenclature{$G_0$}{The power spectrum of the thermal noise in +\nomenclature[sr ]{$G_0$}{The power spectrum of the thermal noise in angular frequency space (\cref{eq:GOdef}).} Plugging \cref{eq:GOdef} into \cref{eq:ODHO-psd-F} we have @@ -223,10 +223,11 @@ resonant angular frequency and $\beta \equiv \gamma / m$ is the drag-acceleration coefficient.\index{Damped harmonic oscillator}\index{$\gamma$}\index{$\kappa$}\index{$\beta$} % -\nomenclature{$\beta$}{Damped harmonic oscillator drag-acceleration - coefficient $\beta \equiv \gamma/m$ (\cref{eq:DHO-xmag}).} -\nomenclature{$\omega_0$}{Resonant angular frequency (radians per - second, \cref{eq:DHO-xmag}).} +\nomenclature[sg b ]{$\beta$}{Damped harmonic oscillator + drag-acceleration coefficient $\beta \equiv \gamma/m$ + (\cref{eq:DHO-xmag}).} +\nomenclature[sg z0 ]{$\omega_0$}{Resonant angular frequency (radians + per second, \cref{eq:DHO-xmag}).} We compute the \PSD\ by plugging \cref{eq:DHO-xmag} into \cref{eq:psd-def} @@ -360,8 +361,8 @@ This gives \label{eq:avg-Vp-Gone} \end{align} % -\nomenclature{$G_1$}{The scaled power spectrum of the thermal noise in - angular frequency space (\cref{eq:Gone-def}).} +\nomenclature[sr ]{$G_1$}{The scaled power spectrum of the thermal + noise in angular frequency space (\cref{eq:Gone-def}).} Plugging into the equipartition theorem (\cref{eq:equipart_k}) yields \begin{align} @@ -400,8 +401,10 @@ from which we can translate the \PSD &= 2\pi \PSD(x, \omega=2\pi f) \;. \end{split} \end{align} -\nomenclature{$t$}{Time (seconds)} +% +\nomenclature[sr ]{$t$}{Time (seconds).} \index{PSD@\PSD!in frequency space} +% The variance of the function $x(t)$ is then given by plugging into \cref{eq:parseval-var} (our corollary to Parseval's theorem) \begin{align} @@ -478,8 +481,9 @@ where $Q$ is the quality factor\citep{burnham03} \label{eq:Q} \end{equation} % -\nomenclature{$Q$}{Quality factor of a damped harmonic oscillator. - $Q\equiv \frac{\sqrt{\kappa m}}{\gamma}$ (\cref{eq:Q}).} +\nomenclature[sr ]{$Q$}{Quality factor of a damped harmonic + oscillator. $Q\equiv \frac{\sqrt{\kappa m}}{\gamma}$ + (\cref{eq:Q}).} % TODO: re-integrate the following diff --git a/src/cantilever-calib/contour_integration.tex b/src/cantilever-calib/contour_integration.tex index 8865abd..6b18c23 100644 --- a/src/cantilever-calib/contour_integration.tex +++ b/src/cantilever-calib/contour_integration.tex @@ -1,8 +1,10 @@ \section{Contour integration} -As a brief review, some definite integrals from $-\infty$ to $\infty$% -\nomenclature{$\infty$}{Infinity} can be evaluated by integrating -along the contour \C\ shown in \cref{fig:UHP-contour}. +As a brief review, some definite integrals from $-\infty$ to $\infty$ +can be evaluated by integrating along the contour \C\ shown in +\cref{fig:UHP-contour}. +% +\nomenclature[o ]{$\infty$}{Infinity} \begin{figure} \asyinclude{figures/contour/contour} diff --git a/src/cantilever-calib/integrals.tex b/src/cantilever-calib/integrals.tex index 25114c8..f223d85 100644 --- a/src/cantilever-calib/integrals.tex +++ b/src/cantilever-calib/integrals.tex @@ -28,11 +28,12 @@ This result is used in \cref{eq:ODHO-psd-int}. \subsection{General case integral} \label{sec:integrals:general} -We will show that, for any $(a,b > 0) \in \Reals$,% -\nomenclature[aR]{\Reals}{Real numbers} +We will show that, for any $(a,b > 0) \in \Reals$, \begin{equation} I = \iInfInf{z}{\frac{1}{(a^2-z^2)^2 + b^2 z^2}} = \frac{\pi}{b a^2} \;. \end{equation} +% +\nomenclature[so aR ]{\Reals}{Real numbers.} First we note that $\abs{f(z)} \rightarrow 0$ like $\abs{z^{-4}}$ for $\abs{z} \gg 1$, and that $f(z)$ is even, so diff --git a/src/future/software.tex b/src/future/software.tex index 68ee628..cdc1bbb 100644 --- a/src/future/software.tex +++ b/src/future/software.tex @@ -19,8 +19,8 @@ stack---including the existing libraries and systems layer dependencies---is open source, so other labs are free to use, improve, and republish it as they see fit. % -\nomenclature{tarball}{A single file containing a collection of files - and directories. Created by +\nomenclature[text ]{tarball}{A single file containing a collection of + files and directories. Created by \href{http://www.gnu.org/software/tar/}{Tar}, tarballs were originally used for tape archives (hence the name), but they are now often used for distributing project source code.} diff --git a/src/hooke/history.tex b/src/hooke/history.tex index bb72a49..6a68113 100644 --- a/src/hooke/history.tex +++ b/src/hooke/history.tex @@ -55,9 +55,10 @@ for the CLI) became truly interactive. The interactive portions of the GUI owe a large debt to earlier work by Rolf Schmidt et al.~from the Centre for NanoScience Research at Concordia University. % -\nomenclature{CLI}{Command line interface. A textual computing +\nomenclature[text ]{CLI}{Command line interface. A textual computing environtment, where the user controls execution by typing commands at a prompt (\cf~GUI).} -\nomenclature{GUI}{Graphical user interface. A graphical computing - environment, where the user controls execution through primarily - through mouse clicks and interactive menus and widgets (\cf~CLI).} +\nomenclature[text ]{GUI}{Graphical user interface. A graphical + computing environment, where the user controls execution through + primarily through mouse clicks and interactive menus and widgets + (\cf~CLI).} diff --git a/src/hooke/plugins.tex b/src/hooke/plugins.tex index fcea6c3..700c1d7 100644 --- a/src/hooke/plugins.tex +++ b/src/hooke/plugins.tex @@ -20,5 +20,6 @@ commands for fitting polymer models to the loading peaks \imint{python}|flatfilt| plugin or with any other peak-marking plugin. For other available plugins, see the \Hooke\ documentation. % -\nomenclature{playlist}{Playlists are containers in \Hooke\ that hold - lists of unfolding curves along with some additional metadata.} +\nomenclature[text ]{playlist}{Playlists are containers in + \Hooke\ that hold lists of unfolding curves along with some + additional metadata.} diff --git a/src/introduction/main.tex b/src/introduction/main.tex index a5f6020..7dba8f9 100644 --- a/src/introduction/main.tex +++ b/src/introduction/main.tex @@ -29,9 +29,7 @@ in understanding the molecular mechanisms behind structures and processes in cells. % What do genes do? Why is protein folding interesting? -An organism's genetic code is stored in DNA% -\nomenclature{DNA}{Deoxyribonucleic Acid} -in the cell nucleus. +An organism's genetic code is stored in DNA in the cell nucleus. DNA sequencing is a fairly well developed field, with fundamental work such as the Human Genome Project seeing major development in the early 2000s\citep{wolfsberg01,mcpherson01,collins03}. It is estimated that @@ -45,6 +43,8 @@ vitally important in executing its biological tasks conformations of a given amino acid sequence and the inverse problem of finding sequences that form a given conformation have proven remarkably difficult. +% +\nomenclature[text ]{DNA}{Deoxyribonucleic acid.} \begin{figure} \begin{center} @@ -166,7 +166,8 @@ microscopes\citep{halvorsen09}. These techniques cover a wide range of approaches, and even when the basic approach is the same (e.g.\ force microscopy), the different techniques span orders of magnitude in the range of their controllable parameters. -\nomenclature{AFM}{Atomic Force Microscope (or Microscopy)} +% +\nomenclature[text ]{AFM}{Atomic force microscope (or microscopy).} \section{Why \emph{unfolding?}} \label{sec:unfolding} diff --git a/src/pyafm/auxiliary.tex b/src/pyafm/auxiliary.tex index c956d40..6d2ba12 100644 --- a/src/pyafm/auxiliary.tex +++ b/src/pyafm/auxiliary.tex @@ -205,10 +205,10 @@ Ziegler--Nichols' step response\citep{ziegler42}, bang-bang response, and ultimate cycle response\citep{ziegler42} tuning rules, as well as Cohen--Coon's\citep{cohen53} and Wang--Juang--Chan's\citep{wang95} step response tuning rules\citep{astrom93}. - -\nomenclature{PID}{Proportional-integral-derivative feedback. For a - process value $p$, setpoint $p_0$, and manipulated variable $m$, the - standard PID algorithm is +% +\nomenclature[text ]{PID}{Proportional-integral-derivative feedback. + For a process value $p$, setpoint $p_0$, and manipulated variable + $m$, the standard PID algorithm is \begin{align} m(t) &= K_p e(t) + K_i \integral{0}{t}{\tau}{e(\tau)} + K_d \deriv{t}{e(t)} \\ diff --git a/src/pyafm/frameworks.tex b/src/pyafm/frameworks.tex index 2f0d5cf..3a9dc9c 100644 --- a/src/pyafm/frameworks.tex +++ b/src/pyafm/frameworks.tex @@ -27,9 +27,10 @@ lab, I'd been using LabVIEW for years, and had become familiar with its two major limitations: name based linking and a binary file format. % -\nomenclature{DAQ}{Data acquisition. Although the term only refers to - input, it is sometimes implicitly extended to include signal output - as well (for controlling experiments as well as measuring results).} +\nomenclature[text ]{DAQ}{Data acquisition. Although the term only + refers to input, it is sometimes implicitly extended to include + signal output as well (for controlling experiments as well as + measuring results).} Programming in a graphical language is quite similar to programming in a textual language. In both, you reduce complexity by encapsulating @@ -44,8 +45,8 @@ languages like C or Python, you can use functions and libraries to package the functional subroutines. In LabVIEW, you package the subroutines in \emph{virtual instruments} (VIs). -\nomenclature{VI}{Virtual instrument. LabVIEW's analog to functions - for encapsulating subroutines.} +\nomenclature[text ]{VI}{Virtual instrument. LabVIEW's analog to + functions for encapsulating subroutines.} The problem comes when you want to update one of your subroutines. LabVIEW VIs are linked dynamically by VI name\citep{ni-vi-management}, @@ -92,9 +93,9 @@ extremely well, but for binary file formats, performance decreases drastically. There are third-party merge tools\citep{ni-merge} for LabVIEW, but the tools are not officially supported. % -\nomenclature{VCS}{Version control system. A system for tracking - project development by recording versions of the project in a - repository.} +\nomenclature[text ]{VCS}{Version control system. A system for + tracking project development by recording versions of the project in + a repository.} While National Instruments seems to put a reasonable amount of effort into maintaining backwards compatibility, long term archival of binary diff --git a/src/pyafm/stack.tex b/src/pyafm/stack.tex index 93be6d8..cbd5e24 100644 --- a/src/pyafm/stack.tex +++ b/src/pyafm/stack.tex @@ -127,11 +127,11 @@ cantilever deflection detection for synchronized ramps, the basic channels. In practice, only the cantilever deflection is monitored, but if other \pypiezo\ users want to measure other analog inputs, the functionality is already built in. - -\nomenclature{DAC}{Digital to analog converter. A device that +% +\nomenclature[text ]{DAC}{Digital to analog converter. A device that converts a digital signal into an analog signal. The inverse of an ADC} -\nomenclature{ADC}{Analog to digital converter. A device that +\nomenclature[text ]{ADC}{Analog to digital converter. A device that digitizes an analog signal. The inverse of a DAC.} The surface detection logic is somewhat heuristic, although it has diff --git a/src/salt/main.tex b/src/salt/main.tex index 512aa44..b34ae62 100644 --- a/src/salt/main.tex +++ b/src/salt/main.tex @@ -75,7 +75,7 @@ which is what we expect due to destabilized hydrogen bonding. \end{center} \end{figure} % -\nomenclature{DTT}{Dithiothreitol +\nomenclature[text ]{DTT}{Dithiothreitol (C\textsubscript{4}H\textsubscript{10}O\textsubscript{2}S\textsubscript{2}), also known as Cleland's reagent\citep{cleland64}. It can be used to reduce disulfide bonding in proteins.} diff --git a/src/sawsim/discussion.tex b/src/sawsim/discussion.tex index 0f0456e..dade809 100644 --- a/src/sawsim/discussion.tex +++ b/src/sawsim/discussion.tex @@ -167,10 +167,10 @@ unfolding event becomes less significant, the change in unfolding probability becomes dominant, and the unfolding force increases upon each subsequent unfolding event\citep{zinober02}. % -\nomenclature{$N_f$}{The number of folded domains in a protein chain - (\cref{sec:sawsim:results:scaffold}).} -\nomenclature{$N_u$}{The number of unfolded domains in a protein chain - (\cref{sec:sawsim:results:scaffold}).} +\nomenclature[sr ]{$N_f$}{The number of folded domains in a protein + chain (\cref{sec:sawsim:results:scaffold}).} +\nomenclature[sr ]{$N_u$}{The number of unfolded domains in a protein + chain (\cref{sec:sawsim:results:scaffold}).} We validate this explanation by calculating the unfolding force probability distribution's dependence on the two competing factors. @@ -242,17 +242,19 @@ as far as I know, nobody has found an analytical form for the unfolding force histograms produced under such a variable loading rate. % -\nomenclature{$r_{uF}$}{Unfolding loading rate (newtons per second)} -\nomenclature{$\alpha$}{The mode unfolding force, +\nomenclature[sr ]{$r_{uF}$}{Unfolding loading rate (newtons per + second).} +\nomenclature[sg a ]{$\alpha$}{The mode unfolding force, $\alpha\equiv-\rho\ln(N_f k_{u0}\rho/\kappa v)$ (\cref{eq:sawsim:gumbel}).} -\nomenclature{$\rho$}{The characteristic unfolding force, +\nomenclature[sg r ]{$\rho$}{The characteristic unfolding force, $\rho\equiv k_BT/\Delta x_u$ (\cref{eq:sawsim:gumbel}).} -\nomenclature{$\gamma_e$}{Euler--Macheroni constant, $\gamma_e=0.577\ldots$} -\nomenclature{$\sigma$}{Standard deviation. For example, $\sigma$ is - used as the standard deviation of an unfolding force distribution in - \cref{eq:sawsim:gumbel}. Not to be confused with the photodiode - sensitivity $\sigma_p$.} +\nomenclature[sg ce ]{$\gamma_e$}{Euler--Macheroni constant, + $\gamma_e=0.577\ldots$.} +\nomenclature[sg s ]{$\sigma$}{Standard deviation. For example, + $\sigma$ is used as the standard deviation of an unfolding force + distribution in \cref{eq:sawsim:gumbel}. Not to be confused with + the photodiode sensitivity $\sigma_p$.} From \cref{fig:sawsim:order-dep}, we see that the proper way to process data from mechanical unfolding experiments is to group the @@ -435,12 +437,12 @@ the symmetrized probability distribution p_m(i) \equiv [p_e(i)+p_s(i)]/2 \;. \label{eq:sawsim:p_m} \end{equation} % -\nomenclature{$D_\text{JS}$}{The Jensen--Shannon divergence +\nomenclature[sr ]{$D_\text{JS}$}{The Jensen--Shannon divergence (\cref{eq:sawsim:D_JS}).} -\nomenclature{$D_\text{LK}$}{The Kullback--Leibler divergence +\nomenclature[sr ]{$D_\text{LK}$}{The Kullback--Leibler divergence (\cref{eq:sawsim:D_KL}).} -\nomenclature{$p_m(i)$}{The symmetrized probability distribution used - in calculating the Jensen--Shannon divergence +\nomenclature[sr ]{$p_m(i)$}{The symmetrized probability distribution + used in calculating the Jensen--Shannon divergence (\cref{eq:sawsim:D_JS,eq:sawsim:p_m}).} % DONE: Mention inter-histogram normalization? no. % For experiments carried out over a series of pulling velocities, we @@ -458,8 +460,9 @@ $\chi^2$ test\citep{NIST:chi-square}, \end{equation} is infinite if there is a bin for which $p_e(i)>0$ but $p_s(i)=0$. % -\nomenclature{$\chi^2$}{The chi-squared distribution} -\nomenclature{$D_{\chi^2}$}{Pearson's $\chi^2$ test (\cref{eq:sawsim:X2}).} +\nomenclature[sg x2 ]{$\chi^2$}{The chi-squared distribution.} +\nomenclature[sr ]{$D_{\chi^2}$}{Pearson's $\chi^2$ test + (\cref{eq:sawsim:X2}).} \Cref{fig:sawsim:fit-space} shows the Jensen--Shannon divergence calculated using \cref{eq:sawsim:D_JS} between an experimental data @@ -591,11 +594,12 @@ Moving the simulation to the departments' 16 core file server cuts that execution time down to 18 hours, which will easily complete over a quiet weekend. Using MPI on the departments' 15 box, dual core computer lab, the simulation would finish overnight. -\nomenclature{MPI}{Message passing interface, a parallel computing - infrastructure} -\nomenclature{PBS}{Portable batch system, a parallel computing +% +\nomenclature[text ]{MPI}{Message passing interface, a parallel + computing infrastructure.} +\nomenclature[text ]{PBS}{Portable batch system, a parallel computing infrastructure. You should be able to distinguish this from the - other PBS (phosphate buffered saline) based on the context} + other PBS (phosphate buffered saline) based on the context.} \subsection{Testing} \label{sec:sawsim:testing} @@ -648,7 +652,7 @@ $N_f$ in terms of $k_u$ as follows: \end{align} where $N_f(0) = N$ because all the domains are initially folded. % -\nomenclature{$W$}{Bin width of an unfolding force histogram +\nomenclature[sr ]{$W$}{Bin width of an unfolding force histogram (\cref{eq:unfold:hist}).} \subsubsection{Constant unfolding rate} @@ -722,8 +726,8 @@ prefactor due to the range\footnote{ difference will be negligible. }. % -\nomenclature{$\alpha'$}{The mode unfolding force for a single folded - domain, $\alpha'\equiv-\rho\ln(k_{u0}\rho/\kappa v)$ +\nomenclature[sg a' ]{$\alpha'$}{The mode unfolding force for a single + folded domain, $\alpha'\equiv-\rho\ln(k_{u0}\rho/\kappa v)$ (\cref{eq:unfold:bell_pdf}).} \subsubsection{Saddle-point Kramers' model} @@ -740,14 +744,14 @@ $l_b$ is the characteristic length of the bound state $l_b \equiv 1/\rho_b$, $\rho_b$ is the density of states in the bound state, and $l_{ts}$ is the characteristic length of the transition state. % -\nomenclature{$U_b(F)$}{The barrier energy as a function of force - (\cref{eq:kramers-saddle}).} -\nomenclature{$l_b$}{The characteristic length of the bound state $l_b - \equiv 1/\rho_b$ (\cref{eq:kramers-saddle}).} -\nomenclature{$\rho_b$}{The density of states in the bound state +\nomenclature[sr ]{$U_b(F)$}{The barrier energy as a function of force (\cref{eq:kramers-saddle}).} -\nomenclature{$l_{ts}$}{The characteristic length of the transition +\nomenclature[sr ]{$l_b$}{The characteristic length of the bound state + $l_b \equiv 1/\rho_b$ (\cref{eq:kramers-saddle}).} +\nomenclature[sg r_b ]{$\rho_b$}{The density of states in the bound state (\cref{eq:kramers-saddle}).} +\nomenclature[sr ]{$l_{ts}$}{The characteristic length of the + transition state (\cref{eq:kramers-saddle}).} \citet{evans97} solved this unfolding rate for both inverse power law potentials and cusp potentials. @@ -765,7 +769,7 @@ experiments\citep{rief97a}. However, none of these earlier implementations were open source, which made it difficult to reuse or validate their results. % -\nomenclature{MD}{Molecular dynamics simulation. Simulate the +\nomenclature[text ]{MD}{Molecular dynamics simulation. Simulate the physical motion of atoms and molecules by numerically solving Newton's equations.} diff --git a/src/sawsim/methods.tex b/src/sawsim/methods.tex index 29a2c92..a4f383b 100644 --- a/src/sawsim/methods.tex +++ b/src/sawsim/methods.tex @@ -121,11 +121,12 @@ polymer as an elastic rod of persistence length $p$ and contour length $L$ (\cref{fig:wlc}). The relationship between tension $F$ and extension (end-to-end distance) $x$ is given by Bustamante's interpolation formula\citep{marko95,bustamante94}. -\nomenclature{WLC}{Wormlike chain, an entropic spring model} -\nomenclature{$p$}{Persistence length of a wormlike chain +% +\nomenclature[text ]{WLC}{Wormlike chain, an entropic spring model.} +\nomenclature[sr ]{$p$}{Persistence length of a wormlike chain (\cref{eq:sawsim:wlc})).} -\nomenclature{$L$}{Contour length in a polymer tension model - (\cref{eq:sawsim:wlc,eq:sawsim:fjc})} +\nomenclature[sr ]{$L$}{Contour length in a polymer tension model + (\cref{eq:sawsim:wlc,eq:sawsim:fjc}).} \begin{equation} F_\text{WLC}(x,p,L) = \frac{k_B T}{p} \p[{ \frac{1}{4}\p({ \frac{1}{(1-x/L)^2} - 1 }) @@ -142,8 +143,9 @@ this characteristic force works out to be around $11\U{pN}$. Most proteins studied using force spectroscopy have unfolding forces in the hundreds of piconewtons, by which point the interpolation formula is in it's more accurate high-extension regime. -\nomenclature{\AA}{{\AA}ngstr{\"o}m, a unit of length. - $1\U{\AA}=1\E{-10}\U{m}$} +% +\nomenclature[so ]{\AA}{{\AA}ngstr{\"o}m, a unit of length. + $1\U{\AA}=1\E{-10}\U{m}$.} For chain with $N_u$ unfolded domains sharing a persistence length $p_u$ and per-domain contour lengths $L_{u1}$, the tension of the WLC @@ -236,16 +238,16 @@ where $L=Nl$ is the total length of the chain, and $\Langevin(\alpha)\equiv\coth{\alpha}-\frac{1}{\alpha}$ is the Langevin function\citep{hatfield99}. % -\nomenclature{FJC}{Freely-jointed chain, an entropic spring model - (\cref{eq:sawsim:fjc}).} -\nomenclature{$\Langevin$}{The Langevin function, +\nomenclature[text ]{FJC}{Freely-jointed chain, an entropic spring + model (\cref{eq:sawsim:fjc}).} +\nomenclature[o L ]{$\Langevin$}{The Langevin function, $\Langevin(\alpha)\equiv\coth{\alpha}-\frac{1}{\alpha}$} -\nomenclature{$\coth$}{Hyperbolic cotangent, +\nomenclature[o coth ]{$\coth$}{Hyperbolic cotangent, \begin{equation} \coth(x) = \frac{\exp{x} + \exp{-x}}{\exp{x} - \exp{-x}} \;. \end{equation} } -\nomenclature{$l$}{Kuhn length in the freely-jointed chain +\nomenclature[sr ]{$l$}{Kuhn length in the freely-jointed chain (\cref{fig:fjc-model,eq:sawsim:fjc}).} \begin{figure} @@ -272,9 +274,10 @@ elastic FJCs\citep{fisher99a,janshoff00}, and freely rotating chains\citep{puchner08} (FRCs) have also been used to model DNA and polysaccharides, but are rarely used to model the relatively short and inextensible synthetic proteins used in force spectroscopy. -\nomenclature{FRC}{Freely-rotating chain (like the FJC, except that - the bond angles are fixed. The torsional angles are not - restricted)} +% +\nomenclature[text ]{FRC}{Freely-rotating chain (like the FJC, except + that the bond angles are fixed. The torsional angles are not + restricted).} \subsubsection{Assumptions} @@ -343,7 +346,7 @@ one sawtooth in the force curve. As the pulling continues and more domains unfold, force curves with a series of sawteeth are generated (\cref{fig:sawsim:sim-sawtooth}). % -\nomenclature{$v$}{Cantilever retraction speed in velocity-clamp +\nomenclature[sr ]{$v$}{Cantilever retraction speed in velocity-clamp unfolding experiments.} \subsubsection{Equlibration time scales} @@ -382,7 +385,8 @@ This relatively large relaxation time constant makes the cantilever act as a low-pass filter and also causes a lag in the force measurement. % -\nomenclature{$\eta$}{Dynamic viscocity (\cref{eq:sawsim:tau-wlc}).} +\nomenclature[sg e ]{$\eta$}{Dynamic viscocity + (\cref{eq:sawsim:tau-wlc}).} \subsection{Unfolding protein molecules by force} \label{sec:sawsim:rate} @@ -417,13 +421,13 @@ where $k_{u0}$ is the unfolding rate in the absence of an external force, and $\Delta x_u$ is the distance between the native state and the transition state along the pulling direction. % -\nomenclature{$\exp{x}$}{Exponential function, +\nomenclature[sr $e^x$ ]{$\exp{x}$}{Exponential function, \begin{equation} \exp{x} = \sum_{n=0}^{\infty} \frac{x^n}{n"!} = 1 + x + \frac{x^2}{2"!} + \ldots \;. \end{equation} } -\nomenclature{$e$}{Euler's number, $e=2.718\ldots$.} +\nomenclature[sr ]{$e$}{Euler's number, $e=2.718\ldots$.} \begin{figure} \asyinclude{figures/schematic/landscape-bell} @@ -453,14 +457,14 @@ group of $N_f$ identical domains to unfold in a given time step is \end{equation} where the approximation is valid when $N_fP_1 \ll 1$. % -\nomenclature{$k$}{Rate constant for general state transitions - (inverse seconds)} -\nomenclature{$k_u$}{Unfolding rate constant} -\nomenclature{$k_{u0}$}{Unforced unfolding rate constant} -\nomenclature{$\Delta x_u$}{Distance between a domain's native state - and the transition state along the pulling direction.} -\nomenclature{$P$}{Probability for at least one domain unfolding in a - given time step (\cref{eq:sawsim:prob-n}).} +\nomenclature[sr ]{$k$}{Rate constant for general state transitions + (inverse seconds).} +\nomenclature[sr ]{$k_u$}{Unfolding rate constant.} +\nomenclature[sr ]{$k_{u0}$}{Unforced unfolding rate constant.} +\nomenclature[sg D ]{$\Delta x_u$}{Distance between a domain's native + state and the transition state along the pulling direction.} +\nomenclature[sr ]{$P$}{Probability for at least one domain unfolding + in a given time step (\cref{eq:sawsim:prob-n}).} \begin{figure} \asyinclude{figures/schematic/monte-carlo} @@ -518,9 +522,11 @@ proteins with broad free energy barriers. \end{equation} where $D$ is the diffusion coefficient and $U_F(x)$ is the free energy along the unfolding cordinate $x$ (\cref{fig:landscape:kramers}). -\nomenclature{$D$}{Diffusion coefficient (square meters per second)} -\nomenclature{$U_F(x)$}{Protein free energy along the unfolding - coordinate $x$ (joules)} +% +\nomenclature[sr ]{$D$}{Diffusion coefficient (square meters per + second).} +\nomenclature[sr ]{$U_F(x)$}{Protein free energy along the unfolding + coordinate $x$ (joules).} \begin{figure} \begin{center} -- 2.26.2