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chapters/1-introduction.tex View File

@ -154,9 +154,14 @@ Nella sezione \ref{sec:fluo} vengono introdotti i principali limiti
della microscopia di fluorescenza e le soluzioni proposte per il loro
superamento.
Nel capitolo \ref{cap:methods} vengono descritte nel dettaglio le
caratteristiche dell'apparato sperimentale realizzato e le procedure
di validazione, calibrazione e acquisizione dei dati.
Nel capitolo \ref{cap:setup} vengono descritte nel dettaglio le
caratteristiche e le proprietà specifiche dell'apparato sperimentale
realizzato.
Nel capitolo \ref{cap:methods} vengono descritti i metodi utilizzati
per validare il funzionamento dell'apparato sperimentale,
quantificarne i parametri di funzionamento e realizzare misure su
campioni biologici.
Nel capitolo \ref{cap:results} sono analizzati i dati prodotti durante
le operazioni di validazione dell'apparato sperimentale e delle
@ -247,8 +252,8 @@ ruolo.
Lo stato attuale delle conoscenze sulla rete di interazioni che
governa e regola il funzionamento delle giunzioni aderenti è riportato
schematicamente in Appendice, sotto forma di diagramma delle vie di
segnalazione.
schematicamente in appendice \ref{app:junctions}, sotto forma di
diagramma delle vie di segnalazione.
\begin{figure}[ht]
\centering


chapters/2-methods.tex → chapters/2-setup.tex View File


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chapters/3-methods.tex View File

@ -0,0 +1,20 @@
\chapter{Metodi}
\label{cap:methods}
\section{Stabilizzazione meccanica}
\label{sec:stabilization}
\section{Calibrazione parametri trappole}
\label{sec:calibration}
\section{Retroazione AOM e \textit{force-clamp}}
\label{sec:force-clamp}
\section{Saggio a tre sfere}
\label{sec:three-beads}
\section{Fluorescenza di singola molecole}
\label{sec:single_molecule_fluorescence}
\section{TIRF e illuminazione a modi di galleria}
\label{sec:gallery_mode}

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chapters/4-results.tex View File

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\chapter{Risultati e discussione}
\label{cap:results}

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chapters/A1-AJ_network.tex View File

@ -1,7 +1,18 @@
\chapter{Giunzioni cellulari}
fdsafasd
\label{app:junctions}
\begin{sidewaysfigure}
\centering
\includegraphics[width=1.0\textwidth]{images/map04520.png}
\includegraphics[width=\textwidth]{images/map04520.png}
\caption{\textbf{Giunzioni aderenti:} mappa delle interazioni molecolari attualmente note, fornita dal progetto
KEGG (Kyoto Encyclopedia of Genes and Genomes) \cite{10.1093/nar/28.1.27,10.1093/nar/gky962,doi:10.1002/pro.3715}}
\label{fig:network_aj}
\end{sidewaysfigure}
\begin{sidewaysfigure}
\centering
\includegraphics[width=0.7\textwidth]{images/map04530.png}
\caption{\textbf{Giunzioni occludenti:} mappa delle interazioni molecolari attualmente note, fornita dal progetto
KEGG (Kyoto Encyclopedia of Genes and Genomes) \cite{10.1093/nar/28.1.27,10.1093/nar/gky962,doi:10.1002/pro.3715}}
\label{fig:network_tj}
\end{sidewaysfigure}

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@ -0,0 +1,2 @@
\chapter{Elettronica di controllo}
\label{app:electronics}

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main.tex View File

@ -30,7 +30,7 @@
\let\Re\relax
\DeclareMathOperator{\Re}{Re}
\usepackage{mhchem}
\usepackage[version=4]{mhchem}
% ===== TABLES
\usepackage{booktabs}
@ -48,6 +48,8 @@
% ===== BIBLIOGRAPHY
\usepackage{cite}
\usepackage{hyperref}
\title{Master Thesis}
\author{Lorenzo Zolfanelli}
\date{April 2020}
@ -68,12 +70,15 @@ Abstract goes here
\tableofcontents
\input{chapters/1-introduction}
\input{chapters/2-methods}
\input{chapters/2-setup}
\input{chapters/3-methods}
\input{chapters/4-results}
%\input{setup-fig/fig1}
\appendix
\input{chapters/A1-AJ_network}
\input{chapters/A2-electronics}
\bibliography{references.bib}{}
\bibliographystyle{plain}
\bibliographystyle{plainurl}
\end{document}

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- 10
references.bib View File

@ -9,7 +9,7 @@
volume = {11},
month = {May},
year = {1986},
url = {http://ol.osa.org/abstract.cfm?URI=ol-11-5-288},
_url = {http://ol.osa.org/abstract.cfm?URI=ol-11-5-288},
doi = {10.1364/OL.11.000288},
abstract = {Optical trapping of dielectric particles by a single-beam gradient force trap was demonstrated for the first reported time. This confirms the concept of negative light pressure due to the gradient force. Trapping was observed over the entire range of particle size from 10 $\mu$m to ~25 nm in water. Use of the new trap extends the size range of macroscopic particles accessible to optical trapping and manipulation well into the Rayleigh size regime. Application of this trapping principle to atom trapping is considered.},
}
@ -23,8 +23,9 @@
pages = "529 - 541",
year = "1996",
issn = "0030-4018",
doi = "https://doi.org/10.1016/0030-4018(95)00753-9",
url = "http://www.sciencedirect.com/science/article/pii/0030401895007539",
doi = "10.1016/0030-4018(95)00753-9",
_doi = "https://doi.org/10.1016/0030-4018(95)00753-9",
_url = "http://www.sciencedirect.com/science/article/pii/0030401895007539",
author = "Yasuhiro Harada and Toshimitsu Asakura",
abstract = "Theoretical expressions of the radiation pressure force for a dielectric sphere in the Rayleigh regime of light scattering under illumination of a Gaussian laser beam with the fundamental mode are derived in explicit form as a function of measurable quantities of the beam parameter in MKS units. Correctness of the derived expressions and validity of the size range of the Rayleigh approximation for the radiation forces as a sum of the scattering force and the gradient force are investigated by a graphical comparison of the calculated forces in longitudinal and transverse components with those obtained from the generalized Lorenz-Mie theory. Fairly good agreement in both components is found within ordinary particle-size ranges of the Rayleigh scattering theory. Furthermore, the good agreement in the transverse component, where the gradient force is dominant, is found to be satisfactory beyond the existing criterion in particle size of the Rayleigh scattering theory until the particle size becomes comparable with the spot size of the illuminating laser beam."
}
@ -42,8 +43,8 @@
abstract = "{KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www.genome.ad.jp/kegg/ ).}",
issn = {0305-1048},
doi = {10.1093/nar/28.1.27},
url = {https://doi.org/10.1093/nar/28.1.27},
eprint = {https://academic.oup.com/nar/article-pdf/28/1/27/9895154/280027.pdf},
_url = {https://doi.org/10.1093/nar/28.1.27},
_eprint = {https://academic.oup.com/nar/article-pdf/28/1/27/9895154/280027.pdf},
}
@article{10.1093/nar/gky962,
@ -58,8 +59,8 @@
abstract = "{KEGG (Kyoto Encyclopedia of Genes and Genomes; https://www.kegg.jp/ or https://www.genome.jp/kegg/) is a reference knowledge base for biological interpretation of genome sequences and other high-throughput data. It is an integrated database consisting of three generic categories of systems information, genomic information and chemical information, and an additional human-specific category of health information. KEGG pathway maps, BRITE hierarchies and KEGG modules have been developed as generic molecular networks with KEGG Orthology nodes of functional orthologs so that KEGG pathway mapping and other procedures can be applied to any cellular organism. Unfortunately, however, this generic approach was inadequate for knowledge representation in the health information category, where variations of human genomes, especially disease-related variations, had to be considered. Thus, we have introduced a new approach where human gene variants are explicitly incorporated into what we call ‘network variants’ in the recently released KEGG NETWORK database. This allows accumulation of knowledge about disease-related perturbed molecular networks caused not only by gene variants, but also by viruses and other pathogens, environmental factors and drugs. We expect that KEGG NETWORK will become another reference knowledge base for the basic understanding of disease mechanisms and practical use in clinical sequencing and drug development.}",
issn = {0305-1048},
doi = {10.1093/nar/gky962},
url = {https://doi.org/10.1093/nar/gky962},
eprint = {https://academic.oup.com/nar/article-pdf/47/D1/D590/27436321/gky962.pdf},
_url = {https://doi.org/10.1093/nar/gky962},
_eprint = {https://academic.oup.com/nar/article-pdf/47/D1/D590/27436321/gky962.pdf},
}
@article{doi:10.1002/pro.3715,
@ -71,15 +72,15 @@
pages = {1947-1951},
keywords = {KEGG, KEGG MEDICUS, KEGG module, pathway mapping, reaction module},
doi = {10.1002/pro.3715},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.3715},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/pro.3715},
_url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.3715},
_eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/pro.3715},
abstract = {Abstract In this era of high-throughput biology, bioinformatics has become a major discipline for making sense out of large-scale datasets. Bioinformatics is usually considered as a practical field developing databases and software tools for supporting other fields, rather than a fundamental scientific discipline for uncovering principles of biology. The KEGG resource that we have been developing is a reference knowledge base for biological interpretation of genome sequences and other high-throughput data. It is now one of the most utilized biological databases because of its practical values. For me personally, KEGG is a step toward understanding the origin and evolution of cellular organisms.},
year = {2019}
}
@article{Capitanio2012,
doi = {10.1038/nmeth.2152},
url = {https://doi.org/10.1038/nmeth.2152},
_url = {https://doi.org/10.1038/nmeth.2152},
year = {2012},
month = sep,
publisher = {Springer Science and Business Media {LLC}},


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