Diese Forschung wurde aus den Grundlagenforschungsfonds für die Zentraluniversitäten, Fördernummer 2019GJZL04, gefördert. Wir danken Professor Weisheng Zeng von der Academy of Forest Inventory and Planning, National Forestry and Grassland Administration, China für den Zugang zu Daten.

1. Datenaufbereitung
<ol><li>Vorbereiten von Modellierungsdaten, die Einzelbauminformationen (Art und Durchmesser bei Brusthöhe bei 1,3 m) und Diagramminformationen (Neigung, Aspekt und Höhe) enthalten. In dieser Studie wurden die Daten aus dem 8. (2009) und 9. (2014) Chinesischen National Forest Inventory in der Provinz Xinjiang, Nordwestchina, gewonnen, das 21.898 Beobachtungen von 779 Stichprobenparzellen enthält. Diese Stichprobenparzellen sind quadratisch mit einer Größe von 1 Mu (chinesische Einheit mit einer Fläche von 0,067 ha) und systematisch über ein Raster von 4 km x 8 km angeordnet. ANMERKUNG: Die Daten für die Modellierung (Basalfläche) erfordern mindestens eine Wachstumsperiode (d. h. zwei Beobachtungen).</li>
	<li>Teilen Sie die Daten zufällig in zwei Datasets auf, wobei 80 % der Daten aus den Stichprobendiagrammen für die Modellanpassung verwendet werden (Modellentwicklungsdatensatz), der aus 17.145 Beobachtungen aus 623 Stichprobendiagrammen und 20 % für die Modellvalidierung (Modellvalidierungs-Dataset) besteht, die aus 4.753 Beobachtungen aus 156 Stichprobendiagrammen besteht. Beschreibende Statistiken für die verwendeten Schlüsselvariablen sind in Tabelle 1enthalten. HINWEIS: Dieser Schritt der Modellierungsprozedur kann weggelassen werden, und alle Daten werden für die Modellentwicklung verwendet.</li>
</ol><table border="1" fo:keep-together.within-page="1"><tbody>
<tr>
<td rowspan="2">Variablen</td>
			<td colspan="4">Anpassen von Daten</td>
			<td></td>
			<td colspan="4">Validierungsdaten</td>
		</tr>
<tr>
<td>Min</td>
			<td>Max</td>
			<td>Bedeuten</td>
			<td>Sd.</td>
			<td></td>
			<td>Min</td>
			<td>Max</td>
			<td>Bedeuten</td>
			<td>Sd.</td>
		</tr>
<tr>
<td>DBH1 (cm)</td>
			<td>5</td>
			<td>124.8</td>
			<td>19.9</td>
			<td>13.2</td>
			<td></td>
			<td>5</td>
			<td>101.5</td>
			<td>19.5</td>
			<td>13.4</td>
		</tr>
<tr>
<td>QMD (cm)</td>
			<td>6.7</td>
			<td>82.3</td>
			<td>22.5</td>
			<td>8.5</td>
			<td></td>
			<td>9.2</td>
			<td>73.3</td>
			<td>21.8</td>
			<td>9.2</td>
		</tr>
<tr>
<td>ID (cm)</td>
			<td>0.1</td>
			<td>14.4</td>
			<td>1.1</td>
			<td>1</td>
			<td></td>
			<td>0.1</td>
			<td>16.9</td>
			<td>1</td>
			<td>1.1</td>
		</tr>
<tr>
<td>BAL (m3)</td>
			<td>0</td>
			<td>5.2</td>
			<td>1.7</td>
			<td>0.9</td>
			<td></td>
			<td>0</td>
			<td>5.4</td>
			<td>1.7</td>
			<td>1</td>
		</tr>
<tr>
<td>NT (Bäume/ha)</td>
			<td>14.9</td>
			<td>3642</td>
			<td>1072</td>
			<td>673.7</td>
			<td></td>
			<td>14.9</td>
			<td>3418</td>
			<td>1205</td>
			<td>829.3</td>
		</tr>
<tr>
<td>BA (m2/ha)</td>
			<td>0.1</td>
			<td>77.5</td>
			<td>34.2</td>
			<td>13.9</td>
			<td></td>
			<td>0.1</td>
			<td>80.6</td>
			<td>34.5</td>
			<td>15.3</td>
		</tr>
<tr>
<td>EL (m)</td>
			<td>2</td>
			<td>3302</td>
			<td>2189</td>
			<td>340.3</td>
			<td></td>
			<td>1441</td>
			<td>3380</td>
			<td>2256</td>
			<td>308.3</td>
		</tr>
</tbody></table>Tabelle 1. Zusammenfassungsstatistiken für Anpassungs- und Validierungsdaten. DBH1: Anfangsdurchmesser bei Brusthöhe bei 1,3 m (DBH), DBH2: DBH gemessen nach 5 Jahren Wachstum, QMD: quadratischer Mittlerer Durchmesser, ID: Durchmesserzuwachs für 5 Jahre (DBH2 – DBH1), BAL: die Grundfläche von Bäumen größer als der Themenbaum (der Subjektbaum: der Baum, der die Wettbewerbsindizes berechnet wurde), NT: die Anzahl der Bäume pro Hektar, BA: Basalfläche pro Hektar, EL: Höhe, S.D.: Standardabweichung.
2. Grundlegende Modellentwicklung
<ol><li>Konsultieren Sie Verweise, um Variablen zu identifizieren, die sich auf die Einzelnenbaum-Basalflächeninkremente auswirken.</li>
	<li>Wählen Sie Variablen basierend auf den Daten aus und berechnen Sie diese. Im Allgemeinen wird das Einzelbaum-Basalflächeninkrement durch drei Variablengruppen beeinflusst: Baumgröße, Wettbewerb und Standortbedingung27,28,29,30.<ol>
<li>Betrachten Sie Baumgrößeneffekte wie DBH1, Quadrat von DBH1 ( <img alt="Equation 11" src="/files/ftp_upload/60827/60827eq11.jpg"> ), die inverse Transformation von DBH1 (1/DBH1), und den gemeinsamen Logarithmus von DBH1 (logDBH1) oder Kombinationen von ihnen.</li>
		<li>Betrachten Sie Wettbewerbseffekte wie ein- und zweiseitige Wettbewerbsindizes, um das Wettbewerbsniveau eines Baumes sowie seine soziale Position innerhalb des Standes umfassender zu quantifizieren. Zu den einseitigen Wettbewerben gehören BAL und der relative Dichteindex (RD=DBH1/QMD); Zu den beidseitigen Wettbewerben gehören NT und BA. HINWEIS: Die entfernungsabhängigen Wettbewerbsindizes sollten berücksichtigt werden, wenn Daten verfügbar sind.</li>
		<li>Betrachten Sie Standorteffekte wie Aspekt (ASP), Neigung (SL) und EL. SL und ASP sollten mithilfe der Stage-Transformation31einbezogen werden.</li>
	</ol>
</li>
	<li>Wählen Sie log( <img alt="Equation 12" src="/files/ftp_upload/60827/60827eq12.jpg"> - <img alt="Equation 11" src="/files/ftp_upload/60827/60827eq11.jpg"> +1) ( <img alt="Equation 12" src="/files/ftp_upload/60827/60827eq12.jpg"> bezeichnet Quadrat von DBH2) als abhängige Variable.</li>
	<li>Entwickeln Sie das Basismodell mit der schrittweisen Regressionsmethode. Stellen Sie sicher, dass das Modell biologisch vertretbar ist und erhebliche Unterschiede zwischen unabhängigen Variablen aufweist. Verwenden Sie den Varianzinflationsfaktor (VIF), um die Multikollinearität zu überprüfen.</li>
	<li>Lassen Sie die unabhängigen Variablen mit p < 0.05 und VIF < 5 im Basismodell.</li>
	<li>Geben Sie die Basismodellergebnisse und das Restdiagramm aus. Das hier produzierte Basismodell dient als Grundlage für die Weiterentwicklung eines Mixed-Effekt-Modells.</li>
</ol>3. Lineare Mixed-Effekt-Modellentwicklung mit dem Paket "nlme" in R-Software
<ol><li>Lesen Sie das Modellentwicklungs-Dataset, und laden Sie das Paket "nlme". >model.development.dataset=read.csv("E:/DATA/JoVE/modelingdata.csv", header=TRUE) >Bibliothek(nlme)</li>
	<li>Wählen Sie Stichprobendiagramme als Zufallseffekte aus, um das Modell für gemischte Effekte zu entwickeln.</li>
	<li>Passen Sie alle möglichen Kombinationen von Zufallseffekten mit der Methode der maximalen Wahrscheinlichkeit (ML) an und geben Sie die Ergebnisse aus. >Modell<-lme(Y1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML", zufällig = 1| PLOT) >Zusammenfassung(Modell) 
	<ol>
<li>Set zufällig = 1 ist der Abfang zu zufälligen Parametern. Ändern Sie die zufälligen Anweisungen, bis alle Kombinationen angepasst sind. Um beispielsweise 1/DBH1 und BAL als zufällige Parameter festzulegen, lautet der Code wie folgt: zufällig = 1/DBH1+BAL-1. Darüber hinaus können die Codes bei der Anpassung Fehler melden, die auf die Nichtkonvergenz des angepassten Modells zurückzuführen sind.</li>
	</ol></li>
	<li>Wählen Sie das beste Modell nach dem Informationskriterium (AIC) von Akaike, dem Bayesschen Informationskriterium (BIC), der Logarithmuswahrscheinlichkeit (Loglik) und dem Wahrscheinlichkeitsverhältnistest (LRT). >anova(Modell.1, Modell.6) >anova(Modell.6, Modell.23) >anova(Modell.23, Modell.30)</li>
	<li>Bestimmen Sie die Struktur von Ri. Adressieren Sie die Heteroskedastizität und Autokorrelation von Ri32. Das Ri ist wie folgt geschrieben: <img alt="Equation 1" src="/files/ftp_upload/60827/60827eq1.jpg">(1) wobei σ2 ein unbekannter Skalierungsfaktor ist, der der Modell-Restvarianz entspricht, ist Gi eine diagonale Matrix, die Heteroskedastizität beschreibt, und Γi ist eine Matrix, die die Autokorrelation beschreibt.<ol>
<li>Beobachten Sie, ob die Residuen heteroskedasitizität aus dem Restdiagramm haben. Wenn heteroskedastistizistiz ist (die Residuen haben ein klares Muster oder einen klaren Trend), führen Sie drei häufig verwendete Varianzfunktionen ein – die Konstante-Plus-Leistungsfunktion, die Leistungsfunktion und die exponentielle Funktion –, um die Fehlervarianzstruktur zu modellieren. >Modell.30.1<-lme(Y'1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML",random='1/DBH1+BAL+NT| Plot weights=varConstPower(form='angepasst(.))) >Zusammenfassung(Modell.30.1) >Modell.30.2<-lme(Y'1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML",random='1/DBH1+BAL+NT| Plot gewichte=varPower(form='angepasst(.))) >Zusammenfassung(Modell.30.2) >Modell.30.3<-lme(Y'1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML",random='1/DBH1+BAL+NT| Plot weights=varExp(form='angepasst(.))) >Zusammenfassung(Modell.30.3)
</li>
		<li>Bestimmen Sie die beste Varianzfunktion für das Modell gemäß AIC, BIC, Loglik und LRT. >anova(Modell.30, Modell.30.1) >anova (Modell.30, Modell.30.2) >anova(Modell.30, Modell.30.3)
</li>
		<li>Führen Sie drei häufig verwendete Autokorrelationsstrukturen ein – die zusammengesetzte Symmetriestruktur (CS), die autoregressive Struktur erster Ordnung [AR(1)] und eine Kombination aus autoregressiven und gleitenden Durchschnittsstrukturen erster Ordnung [ARMA(1,1)]–, um die Autokorrelation zu berücksichtigen. >Modell.30.3.1<-lme(Y'1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML", zufällig = 1 /DBH1+BAL+NT| PLOT, weights=varExp(form='fitted(.)), corr= corCompSymm()) >Zusammenfassung(Modell.30.3.1) >Modell.30.3.2<-lme(Y'1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML", zufällig = 1 /DBH1+BAL+NT| PLOT,weights=varExp(form='angepasst(.)), corr=corAR1()) >Zusammenfassung(Modell.30.3.2) >Modell.30.3.3<-lme(Y1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML", zufällig = 1 /DBH1+BAL+NT| PLOT,weights=varExp(form='angepasst(.)), corr=corARMA(q=1,p=1)) >Zusammenfassung(Modell.30.3.3)
</li>
		<li>Bestimmen Sie die beste Autokorrelationsstruktur gemäß AIC, BIC, Loglik und LRT. >anova(Modell.30.3, Modell.30.3.2) ANMERKUNG: Die Gi und i können nicht definiert werden, wenn es keine Heteroskedastizität und Autokorrelation gibt.</li>
		<li>Geben Sie die Endergebnisse des Mixed-Effekt-Modells mit der REML-Methode (Restricted Maximum Likelihood) aus. >Mixed.model<-lme(Y'1/DBH1+BAL+NT+EL,data=model.development.dataset, method="REML",random='1/DBH1+BAL+NT| Plot weights=varExp(form=' fitted(.)), corr=corAR1())) >Zusammenfassung(Mixed.model)
</li>
	</ol></li>
</ol>4. Bias-Korrektur
<ol><li>Transformieren Sie die vorhergesagten Werte des Basalflächeninkrements mithilfe des endgültigen Modells auf einer logarithmischen Skala in den ursprünglichen Maßstab. Eine solche lineare Rücktransformation des vorhergesagten Wertes aus einem logtransformierten Modell erzeugt jedoch eine zugeordnete Log-Transformationsverzerrung. Um die Log-Bias zu behandeln, wurde ein Korrekturfaktor abgeleitet und in die Vorhersagegleichung integriert, die das tatsächliche vorhergesagte Grundflächeninkrement für einen bestimmten Baum schätzt [Gleichung (2)]: <img alt="Equation 2" src="/files/ftp_upload/60827/60827eq2.jpg">(2) wobei der <img alt="Equation 13" src="/files/ftp_upload/60827/60827eq13.jpg"> prognostizierte logarithmische Wert des Basalflächeninkrements aus dem Modell vorhergesagt wird, während <img alt="Equation 14" src="/files/ftp_upload/60827/60827eq14.jpg"> der vorhergesagte rücktransformierte Wert des Basalflächeninkrements für 5 Jahre nach Korrektur für Log-Transformations-Bias ist. <img alt="Equation 15" src="/files/ftp_upload/60827/60827eq15.jpg"> ist die Varianz von Zufälligeffekten im Diagramm und σ2 ist Restvarianz.</li>
	<li>Konvertieren Sie das Grundflächeninkrement ( <img alt="Equation 14" src="/files/ftp_upload/60827/60827eq14.jpg"> ) in das Durchmesserinkrement.</li>
</ol>5. Modellvorhersage und -auswertung
<ol><li>Bereiten Sie das in Abschnitt 1.2 erstellte Modellvalidierungs-Dataset für die Vorhersage vor.</li>
	<li>Verwenden Sie das lineare Modell für gemischte Effekte, um das Inkrement der einzelnen Baumbasalflächen vorherzusagen. Die zufälligen Komponenten wurden mit dem folgenden besten linearen unvoreingenommenen Prädiktor berechnet: <img alt="Equation 3" src="/files/ftp_upload/60827/60827eq3.jpg">(3) wobei <img alt="Equation 16" src="/files/ftp_upload/60827/60827eq16.jpg"> ein Vektor für die zufälligen Komponenten ist; ist die <img alt="Equation 17" src="/files/ftp_upload/60827/60827eq17.jpg"> Varianz-Kovarianz-Matrix für die Variabilität zwischen den Plots; <img alt="Equation 18" src="/files/ftp_upload/60827/60827eq18.jpg"> ist die Konstruktionsmatrix für die zufälligen Komponenten, die auf die komplementären Beobachtungen wirken; <img alt="Equation 19" src="/files/ftp_upload/60827/60827eq19.jpg"> ist der Restvektor, dessen Komponenten durch die Differenz zwischen den Basalflächenschritten und den vorhergesagten Inkrementen mit dem Modell der festen Effekte angegeben werden.</li>
	<li>Bewerten und vergleichen Sie die Vorhersagefähigkeit des Basismodells und des linearen Mixed-Effekt-Modells anhand der folgenden drei statistischen Indikatoren23,33. <img alt="Equation 4" src="/files/ftp_upload/60827/60827eq4.jpg">(4) <img alt="Equation 5" src="/files/ftp_upload/60827/60827eq5.jpg">(5) <img alt="Equation 6" src="/files/ftp_upload/60827/60827eq6.jpg">(6) wobei obji die Basalflächeninkremente ist, esti die vorhergesagten Basalflächenschritte, <img alt="Equation 20" src="/files/ftp_upload/60827/60827eq20.jpg"> das Mittel der Beobachtungen und N die Anzahl der Beobachtungen.</li>
</ol>

<ol>
	<li>Meng, J., Lu, Y., Ji, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transformation+of+a+Degraded+Pinus+massoniana+Plantation+into+a+Mixed-Species+Irregular+Forest:+Impacts+on+Stand+Structure+and+Growth+in+Southern+China.">Transformation of a Degraded Pinus massoniana Plantation into a Mixed-Species Irregular Forest: Impacts on Stand Structure and Growth in Southern China.</a> Forests. 5 (12), 3199-3221 (2014).</li><li>Sharma, A., Bohn, K., Jose, S., Cropper, W. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Converting+even-aged+plantations+to+uneven-aged+stand+conditions:+A+simulation+analysis+of+silvicultural+regimes+with+slash+pine+(Pinus+elliottii+Engelm).">Converting even-aged plantations to uneven-aged stand conditions: A simulation analysis of silvicultural regimes with slash pine (Pinus elliottii Engelm).</a> Forest Science. 60 (5), 893-906 (2014).</li><li>Zhu, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Feasibility+of+implementing+thinning+in+even-aged+Larix+olgensis+plantations+to+develop+uneven-aged+larch&#8211;broadleaved+mixed+forests.">Feasibility of implementing thinning in even-aged Larix olgensis plantations to develop uneven-aged larch&#8211;broadleaved mixed forests.</a> Journal of Forest Research. 15 (1), 71-80 (2010).</li><li>Leites, L. P., Robinson, A. P., Crookston, N. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accuracy+and+equivalence+testing+of+crown+ratio+models+and+assessment+of+their+impact+on+diameter+growth+and+basal+area+increment+predictions+of+two+variants+of+the+Forest+Vegetation+Simulator.">Accuracy and equivalence testing of crown ratio models and assessment of their impact on diameter growth and basal area increment predictions of two variants of the Forest Vegetation Simulator.</a> Canadian Journal of Forest Research. 39 (3), 655-665 (2009).</li><li>Pretzsch, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Forest Dynamics, Growth and Yield. , (2009).</li><li>Weiskittel, A. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Forest+growth+and+yield+modeling.">Forest growth and yield modeling.</a> Forest Growth &amp; Yield Modeling. 7 (2), 223-233 (2002).</li><li>Burkhart, H. E., Tom&#233;, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Modeling Forest Trees and Stands. , (2012).</li><li>Zhang, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chinese+Academy+Of+Forestry.+A+linkage+among+whole-stand+model,+individual-tree+model+and+diameter-distribution+model.">Chinese Academy Of Forestry. A linkage among whole-stand model, individual-tree model and diameter-distribution model.</a> Journal of Forest Science. 56 (56), 600-608 (2010).</li><li>Peng, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Growth+and+yield+models+for+uneven-aged+stands:+past,+present+and+future.">Growth and yield models for uneven-aged stands: past, present and future.</a> Forest Ecology &amp; Management. 132 (2), 259-279 (2000).</li><li>Lhotka, J. M., Loewenstein, E. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+individual-tree+diameter+growth+model+for+managed+uneven-aged+oak-shortleaf+pine+stands+in+the+Ozark+Highlands+of+Missouri,+USA.">An individual-tree diameter growth model for managed uneven-aged oak-shortleaf pine stands in the Ozark Highlands of Missouri, USA.</a> Forest Ecology &amp; Management. 261 (3), 770-778 (2011).</li><li>Port&#233;, A., Bartelink, H. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modelling+mixed+forest+growth:+a+review+of+models+for+forest+management.">Modelling mixed forest growth: a review of models for forest management.</a> Ecological Modelling. 150 (1), 141-188 (2002).</li><li>Moses, L. E., Gale, L. C., Altmann, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+for+analysis+of+unbalanced,+longitudinal,+growth+data.">Methods for analysis of unbalanced, longitudinal, growth data.</a> American Journal of Primatology. 28 (1), 49-59 (2010).</li><li>Biging, G. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+Estimates+of+Site+Index+Curves+Using+a+Varying-Parameter+Model.">Improved Estimates of Site Index Curves Using a Varying-Parameter Model.</a> Forest Science. 31 (31), 248-259 (1985).</li><li>Kowalchuk, R. K., Keselman, H. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mixed-model+pairwise+multiple+comparisons+of+repeated+measures+means.">Mixed-model pairwise multiple comparisons of repeated measures means.</a> Psychological Methods. 6 (3), 282-296 (2001).</li><li>Hayes, A. F., Cai, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+heteroskedasticity-consistent+standard+error+estimators+in+OLS+regression:+An+introduction+and+software+implementation.">Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation.</a> Behavior Research Methods. 39 (4), 709-722 (2007).</li><li>Gutzwiller, K. J., Riffell, S. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Using Statistical Models to Study Temporal Dynamics of Animal-Landscape Relations. , (2007).</li><li>Calama, R., Montero, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Multilevel linear mixed model for tree diameter increment in stone pine (Pinus pinea): a calibrating approach. 39, (2005).</li><li>Vonesh, E. F., Chinchilli, V. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Linear+and+nonlinear+models+for+the+analysis+of+repeated+measurements.">Linear and nonlinear models for the analysis of repeated measurements.</a> Journal of Biopharmaceutical Statistics. 18 (4), 595-610 (1996).</li><li>Zobel, J. M., Ek, A. R., Burk, T. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+Forest+Inventory+and+Analysis+surveys,+basal+area+models,+and+fitting+methods+for+the+aspen+forest+type+in+Minnesota.">Comparison of Forest Inventory and Analysis surveys, basal area models, and fitting methods for the aspen forest type in Minnesota.</a> Forest Ecology &amp; Management. 262 (2), 188-194 (2011).</li><li>Sharma, M., Parton, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Height-diameter+equations+for+boreal+tree+species+in+Ontario+using+a+mixed-effects+modeling+approach.">Height-diameter equations for boreal tree species in Ontario using a mixed-effects modeling approach.</a> Forest Ecology &amp; Management. 249 (3), 187-198 (2007).</li><li>Crecente-Campo, F., Tom&#233;, M., Soares, P., Di&#233;guez-Aranda, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+generalized+nonlinear+mixed-effects+height&#8211;diameter+model+for+Eucalyptus+globulus+L.+in+northwestern+Spain.">A generalized nonlinear mixed-effects height&#8211;diameter model for Eucalyptus globulus L. in northwestern Spain.</a> Forest Ecology &amp; Management. 259 (5), 943-952 (2010).</li><li>Fu, L., Sharma, R. P., Hao, K., Tang, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+generalized+interregional+nonlinear+mixed-effects+crown+width+model+for+Prince+Rupprecht+larch+in+northern+China.">A generalized interregional nonlinear mixed-effects crown width model for Prince Rupprecht larch in northern China.</a> Forest Ecology &amp; Management. 389 (2017), 364-373 (2017).</li><li>Hao, X., Yujun, S., Xinjie, W., Jin, W., Yao, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Linear+mixed-effects+models+to+describe+individual+tree+crown+width+for+China-fir+in+Fujian+Province,+southeast+China.">Linear mixed-effects models to describe individual tree crown width for China-fir in Fujian Province, southeast China.</a> Plos One. 10 (4), 0122257 (2015).</li><li>Vanderschaaf, C. L., Burkhart, H. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparing+methods+to+estimate+Reineke's+Maximum+Size-Density+Relationship+species+boundary+line+slope.">Comparing methods to estimate Reineke's Maximum Size-Density Relationship species boundary line slope.</a> Forest Science. 53 (3), 435-442 (2007).</li><li>Zhang, L., Bi, H., Gove, J. H., Heath, L. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+alternative+methods+for+estimating+the+self-thinning+boundary+line.">A comparison of alternative methods for estimating the self-thinning boundary line.</a> Canadian Journal of Forest Research. 35 (6), 1507-1514 (2005).</li><li>Hart, D. R., Chute, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimating+von+Bertalanffy+growth+parameters+from+growth+increment+data+using+a+linear+mixed-effects+model,+with+an+application+to+the+sea+scallop+Placopecten+magellanicus.">Estimating von Bertalanffy growth parameters from growth increment data using a linear mixed-effects model, with an application to the sea scallop Placopecten magellanicus.</a> Ices Journal of Marine Science. 66 (9), 2165-2175 (2009).</li><li>Uzoh, F. C. C., Oliver, W. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Individual+tree+diameter+increment+model+for+managed+even-aged+stands+of+ponderosa+pine+throughout+the+western+United+States+using+a+multilevel+linear+mixed+effects+model.">Individual tree diameter increment model for managed even-aged stands of ponderosa pine throughout the western United States using a multilevel linear mixed effects model.</a> Forest Ecology &amp; Management. 256 (3), 438-445 (2008).</li><li>Cond&#233;s, S., Sterba, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparing+an+individual+tree+growth+model+for+Pinus+halepensis+Mill.+in+the+Spanish+region+of+Murcia+with+yield+tables+gained+from+the+same+area.">Comparing an individual tree growth model for Pinus halepensis Mill. in the Spanish region of Murcia with yield tables gained from the same area.</a> European Journal of Forest Research. 127 (3), 253-261 (2008).</li><li>Pokharel, B., Dech, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mixed-effects+basal+area+increment+models+for+tree+species+in+the+boreal+forest+of+Ontario,+Canada+using+an+ecological+land+classification+approach+to+incorporate+site+effects.">Mixed-effects basal area increment models for tree species in the boreal forest of Ontario, Canada using an ecological land classification approach to incorporate site effects.</a> Forestry. 85 (2), 255-270 (2012).</li><li>Wykoff, W. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+basal+area+increment+model+for+individual+conifers+in+the+northern+Rocky+Mountains.">A basal area increment model for individual conifers in the northern Rocky Mountains.</a> Forest Science. 36 (4), 1077-1104 (1990).</li><li>Stage, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Notes:+An+Expression+for+the+Effect+of+Aspect,+Slope,+and+Habitat+Type+on+Tree+Growth.">Notes: An Expression for the Effect of Aspect, Slope, and Habitat Type on Tree Growth.</a> Forest Science. 22 (4), 457-460 (1976).</li><li>Gregorie, T. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Generalized+Error+Structure+for+Forestry+Yield+Models.">Generalized Error Structure for Forestry Yield Models.</a> Forest Science. 33 (2), 423-444 (1987).</li><li>Zhao, L., Li, C., Tang, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Individual-tree+diameter+growth+model+for+fir+plantations+based+on+multi-level+linear+mixed+effects+models+across+southeast+China.">Individual-tree diameter growth model for fir plantations based on multi-level linear mixed effects models across southeast China.</a> Journal of Forest Research. 18 (4), 305-315 (2013).</li><li>Hall, D. B., Bailey, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+and+Prediction+of+Forest+Growth+Variables+Based+on+Multilevel+Nonlinear+Mixed+Models.">Modeling and Prediction of Forest Growth Variables Based on Multilevel Nonlinear Mixed Models.</a> Forest Science. 47 (3), 311-321 (2001).</li><li>Yang, Y., Huang, S., Meng, S. X., Trincado, G., Vanderschaaf, C. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+multilevel+individual+tree+basal+area+increment+model+for+aspen+in+boreal+mixedwood+stands+:+Journal+canadien+de+la+recherche+forestie&#768;re.">A multilevel individual tree basal area increment model for aspen in boreal mixedwood stands : Journal canadien de la recherche forestie&#768;re.</a> Revue Canadienne De Recherche Foresti&#232;re. 39 (39), 2203-2214 (2009).</li><li>Pinheiro, J. C., Bates, D. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mixed-effects+models+in+S+and+S-Plus.">Mixed-effects models in S and S-Plus.</a> Publications of the American Statistical Association. 96 (455), 1135-1136 (2000).</li></ol>

Die Autoren haben nichts zu verraten.

Eine entscheidende Frage für die Entwicklung von Modellen mit gemischten Effekten ist die Bestimmung, welche Parameter als Zufallseffekte behandelt werden können und welche als feste Effekte34,35betrachtet werden sollten. Es wurden zwei Methoden vorgeschlagen. Der häufigste Ansatz besteht darin, alle Parameter als Zufallseffekte zu behandeln und dann das beste Modell von AIC, BIC, Loglik und LRT auswählen zu lassen. Dies war die Methode unserer Studie<sup cla...

Im Vergleich zur monogetagten Monokultur hat die walduneinheitliche Waldbewirtschaftung mit mehreren Zielen in jüngster Zeit erhöhte Aufmerksamkeit erhalten1,2,3. Die Vorhersage verschiedener Bewirtschaftungsalternativen ist für die Formulierung robuster Waldbewirtschaftungsstrategienerforderlich, insbesondere für komplexe, ungleich gealterte Mischarten4 . Waldwachstums- und Ertragsmodelle wurden ausgiebig zur Vorhersage der Baum- oder Standentwicklung und -ernte im Rahmen verschiedener Bewirtschaftungsschemata5,6,7verwendet. Waldwachstums- und Ertragsmodelle werden in Einzelbaummodelle, Größenklassenmodelle und Vollwertmodell6,7,8klassifiziert. Leider eignen sich Modelle der Größenklasse und Ganzstandsmodelle nicht für ungleich gealterte Mischartenwälder, die eine detailliertere Beschreibung erfordern, um den Entscheidungsprozess der Waldbewirtschaftung zu unterstützen. Aus diesem Grund haben individuelle Baumwachstum und Ertragsmodelle in den letzten Jahrzehnten erhöhte Aufmerksamkeit erhalten, weil sie in der Lage sind, Vorhersagen für Waldbestände mit einer Vielzahl von Artenzusammensetzungen, Strukturen und Managementstrategien9,10,11zu machen.
Die Regression der gewöhnlichen kleinsten Quadrate (OLS) ist die am häufigsten verwendete Methode für die Entwicklung von Einzelbaumwachstumsmodellen12,13,14,15. Die Datensätze für einzelne Baumwachstumsmodelle, die wiederholt über einen festen Zeitraum auf derselben Stichprobeneinheit (d. h. Stichprobendiagramm oder Baum) gesammelt wurden, weisen eine hierarchische stochastische Struktur auf, mit einem Mangel an Unabhängigkeit und hoher räumlicher und zeitlicher Korrelation zwischen den Beobachtungen10,16. Die hierarchische stochastische Struktur verstößt gegen die grundannahmen der OLS-Regression: nämlich unabhängige Residuen und normal verteilte Daten mit gleichen Varianzen. Daher führt die Verwendung der OLS-Regression unweigerlich zu verzerrten Schätzungen des Standardfehlers von Parameterschätzungen für diese Daten13,14.
Modelle mit gemischten Effekten bieten ein leistungsstarkes Werkzeug für die Analyse von Daten mit komplexen Strukturen, z. B. Daten mit wiederholten Messeinheiten, Längsschnittdaten und mehrstufigen Daten. Modelle mit gemischten Effekten bestehen aus festen Komponenten, die der gesamten Grundgesamtheit gemeinsam sind, und zufälligen Komponenten, die für jede Stichprobenstufe spezifisch sind. Darüber hinaus berücksichtigen Modelle mit gemischten Effekten Heteroskedastizität und Autokorrelation in Raum und Zeit, indem nicht diagonale Varianz-Kovarianzstrukturmatrizen17,18,19definiert werden. Aus diesem Grund wurden Mixed-Effekt-Modelle in der Forstwirtschaft ausgiebig eingesetzt, wie z.B. in den Durchmesser-Höhen-Modellen20,21, Kronenmodelle22,23, selbstverdünnende Modelle24,25und Wachstumsmodelle26,27.
Dabei ging es vor allem darum, ein individuelles Grundflächen-Inkrementmodell mit einem linearen Mixed-Effekt-Ansatz zu entwickeln. Wir hoffen, dass der Ansatz der gemischten Auswirkungen umfassend angewandt werden kann.

<table border="0" cellpadding="0" cellspacing="0" style="width:666px;" width="665">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Computer</td>
			<td style="width:121px;">acer</td>
			<td style="width:161px;"></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Microsoft Office 2013</td>
			<td style="width:121px;"></td>
			<td style="width:161px;"></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">R x64 3.5.1</td>
			<td style="width:121px;"></td>
			<td style="width:161px;"></td>
			<td></td>
		</tr>
	</tbody>
</table>

development of an individual-tree basal area increment model using a linear mixed-effects approach

Hier haben wir ein individuelles Baummodell von 5-Jahres-Basalflächenschritten entwickelt, das auf einem Datensatz basiert, einschließlich 21898 Picea Asperata-Bäumen aus 779 Musterparzellen in der Provinz Xinjiang, Nordwestchina. Um hohe Korrelationen zwischen Beobachtungen derselben Stichprobeneinheit zu verhindern, haben wir das Modell mit einem linearen Mixed-Effekt-Ansatz mit zufälligem Ploteffekt entwickelt, um die stochastische Variabilität zu berücksichtigen. Verschiedene Variablen auf Baum- und Standebene, wie Z. B. Indizes für Baumgröße, Wettbewerb und Standortbedingung, wurden als feste Effekte zur Erklärung der Restvariabilität einbezogen. Darüber hinaus wurden Heteroskedastizität und Autokorrelation durch die Einführung von Varianzfunktionen und Autokorrelationsstrukturen beschrieben. Das optimale modellte lineare Mixed-Effekte wurde durch mehrere Anpassungsstatistiken bestimmt: das Informationskriterium von Akaike, das Bayessche Informationskriterium, die Logarithmuswahrscheinlichkeit und ein Wahrscheinlichkeitsverhältnistest. Die Ergebnisse zeigten, dass signifikante Variablen der Einzelbaum-Basalflächenzunahme die inverse Transformation des Durchmessers in Brusthöhe, die Basalfläche von Bäumen größer als der Themenbaum, die Anzahl der Bäume pro Hektar und die Höhe waren. Darüber hinaus wurden Fehler in der Varianzstruktur am erfolgreichsten durch die exponentielle Funktion modelliert, und die Autokorrelation wurde durch die autoregressive Struktur erster Ordnung (AR(1)) signifikant korrigiert. Die Leistung des linearen Mixed-Effekt-Modells wurde im Vergleich zum Modell mit der Regression der gewöhnlichen kleinsten Quadrate deutlich verbessert.

Das grundlegende Grundflächeninkrementmodell für P. asperata wurde als Gleichung (7) ausgedrückt. Die Parameterschätzungen, die entsprechenden Standardfehler und die Nichtanpassungsstatistiken sind in Tabelle 2dargestellt. Das Restdiagramm ist in Abbildung 1dargestellt. Es wurde eine ausgeprägte Heteroskedastizität der Residuen beobachtet. <img alt="Equation 7" src="/files/ftp_upload/60827/60827eq7.jpg">(7)
<table border="1" fo:keep-together.within-page="1"><tbody>
...

Watch this Scientific Journal Video about Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach at JoVE.com

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Modelle mit gemischten Effekten sind flexible und nützliche Werkzeuge zur Analyse von Daten mit einer hierarchischen stochastischen Struktur in der Forstwirtschaft und könnten auch verwendet werden, um die Leistung von Waldwachstumsmodellen deutlich zu verbessern. Hier wird ein Protokoll vorgestellt, das Informationen zu linearen Mixed-Effekt-Modellen synthetisiert.

Entwicklung eines Individual-Tree Basal Area Increment Modells mit einem linearen Mixed-Effects-Ansatz

Here, we developed an individual-tree model of 5-year basal area increments based on a dataset including 21898 Picea asperata trees from 779 sample plots ...

development-an-individual-tree-basal-area-increment-model-using

Research

JoVE Journal

Biology

3.2K Aufrufe.  Beijing Forestry University. Modelle mit gemischten Effekten sind flexible und nützliche Werkzeuge zur Analyse von Daten mit einer hierarchischen stochastischen Struktur in der Forstwirtschaft und könnten auch verwendet werden, um die Leistung von Waldwachstumsmodellen deutlich zu verbessern. Hier wird ein Protokoll vorgestellt, das Informationen zu linearen Mixed-Effekt-Modellen synthetisiert.

Serie: Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Microfabricated Post-Array-Detectors (mPADs): an Approach to Isolate Mechanical Forces

In this video, we will present our approach to measure cellular traction forces using a microfabricated array of posts.  Traction forces are generated through myosin-actin interactions and play an important role in our physiology.  During development, they enable cells to move from one location to the next in order to form the early structures of tissue.  Traction forces help in the healing processes.  They are necessary for the proper closure of wounds or the migration and crawling of leukocytes through our body.  These same forces can be detrimental to our health in the case of cancer metastasis or vascular growth towards a tumor.  The most common method by which to study cells in vitro has been to use a glass or polystyrene dish.  However, the rigidity of the substrates makes it impossible to physically measure cell traction forces, and there are relatively few methods to study traction forces.  Our lab has developed a technique to overcome these limitations.  The method is based on a vertical array of flexible cantilevers, the stiffness and size scale of which are such that individual cells spread across many cantilevers and deflect them in the process.  The pillars we use are 3 &mu;m in diameter, 10 &mu;m tall, and are configured in a regular array with 9 &mu;m center-to-center spacing.  But these physical dimensions can be readily varied to accommodate a variety of studies.  We start with a silicon master, but the final posts are made out of silicone rubber called poly (dimethyl siloxane), or PDMS.  We can measure the deflections under a microscope and calculate the magnitude and direction of traction forces required to produce the observed deflections.  We call these substrates microfabricated post-array-detectors, or mPADs.  Here, we will show you how we fabricate and use the mPADs to assess modulations of cellular contractility.

Microfabricated Post-Array-Detectors mPADs: an Approach to Isolate Mechanical Forces

In this video, we demonstrate how to fabricate and utilize microfabricated post array detectors (mPADs) to assess modulations of cellular contractility.

Mikrofabrizierten Post-Array-Detektoren (mPADs): Ein Ansatz zur Mechanical Forces Isolate

In this video, we will present our approach to measure cellular traction forces using a microfabricated array of posts.  Traction forces are generated ...

Forschung

Biologie

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,5,12,20) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization.
Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods3,4,9,10,13-15,17-19,22,23,25. In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model7 with a multiobjective evolutionary algorithm SPEA226, and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.

This work demonstrates an integration of a water quality model with an optimization component utilizing evolutionary algorithms to solve for optimal (lowest-cost) placement of agricultural conservation practices for a specified set of water quality improvement objectives. The solutions are generated using a multi-objective approach, allowing for explicit quantification of tradeoffs.

Spatial Mehrzieloptimierung of Agricultural Conservation Practices mit einem SWAT-Modell und einem evolutionären Algorithmus

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals ...

A Semi-quantitative Approach to Assess Biofilm Formation Using Wrinkled Colony Development

Biofilms, or surface-attached communities of cells encapsulated in an extracellular matrix, represent a common lifestyle for many bacteria. Within a biofilm, bacterial cells often exhibit altered physiology, including enhanced resistance to antibiotics and other environmental stresses 1. Additionally, biofilms can play important roles in host-microbe interactions. Biofilms develop when bacteria transition from individual, planktonic cells to form complex, multi-cellular communities 2. In the laboratory, biofilms are studied by assessing the development of specific biofilm phenotypes. A common biofilm phenotype involves the formation of wrinkled or rugose bacterial colonies on solid agar media 3. Wrinkled colony formation provides a particularly simple and useful means to identify and characterize bacterial strains exhibiting altered biofilm phenotypes, and to investigate environmental conditions that impact biofilm formation. Wrinkled colony formation serves as an indicator of biofilm formation in a variety of bacteria, including both Gram-positive bacteria, such as Bacillus subtilis 4, and Gram-negative bacteria, such as Vibrio cholerae 5, Vibrio parahaemolyticus 6, Pseudomonas aeruginosa 7, and Vibrio fischeri 8.
The marine bacterium V. fischeri has become a model for biofilm formation due to the critical role of biofilms during host colonization: biofilms produced by V. fischeri promote its colonization of the Hawaiian bobtail squid Euprymna scolopes 8-10. Importantly, biofilm phenotypes observed in vitro correlate with the ability of V. fischeri cells to effectively colonize host animals: strains impaired for biofilm formation in vitro possess a colonization defect 9,11, while strains exhibiting increased biofilm phenotypes are enhanced for colonization 8,12. V. fischeri therefore provides a simple model system to assess the mechanisms by which bacteria regulate biofilm formation and how biofilms impact host colonization.
In this report, we describe a semi-quantitative method to assess biofilm formation using V. fischeri as a model system. This method involves the careful spotting of bacterial cultures at defined concentrations and volumes onto solid agar media; a spotted culture is synonymous to a single bacterial colony. This 'spotted culture' technique can be utilized to compare gross biofilm phenotypes at single, specified time-points (end-point assays), or to identify and characterize subtle biofilm phenotypes through time-course assays of biofilm development and measurements of the colony diameter, which is influenced by biofilm formation. Thus, this technique provides a semi-quantitative analysis of biofilm formation, permitting evaluation of the timing and patterning of wrinkled colony development and the relative size of the developing structure, characteristics that extend beyond the simple overall morphology.

We provide a simple, semi-quantitative method to investigate biofilm formation in vitro. This method takes advantage of the Zeiss stemi 2000-C Dissecting Microscope (with camera attachment) to monitor both the timing and pattern of biofilm formation, as assessed by the development of wrinkled colonies.

Ein semi-quantitativer Ansatz, um die Biofilmbildung Mit Wrinkled Colony Entwicklung zu bewerten

Biofilms, or surface-attached communities of cells encapsulated in an extracellular matrix, represent a common lifestyle for many bacteria. Within a ...

Conversion of a Capture ELISA to a Luminex xMAP Assay using a Multiplex Antibody Screening Method

The enzyme-linked immunosorbent assay (ELISA) has long been the primary tool for detection of analytes of interest in biological samples for both life science research and clinical diagnostics. However, ELISA has limitations. It is typically performed in a 96-well microplate, and the wells are coated with capture antibody, requiring a relatively large amount of sample to capture an antigen of interest . The large surface area of the wells and the hydrophobic binding of capture antibody can also lead to non-specific binding and increased background. Additionally, most ELISAs rely upon enzyme-mediated amplification of signal in order to achieve reasonable sensitivity. Such amplification is not always linear and can thus skew results.
In the past 15 years, a new technology has emerged that offers the benefits of the ELISA, but also enables higher throughput, increased flexibility, reduced sample volume, and lower cost, with a similar workflow 1, 2. Luminex xMAP Technology is a microsphere (bead) array platform enabling both monoplex and multiplex assays that can be applied to both protein and nucleic acid applications 3-5. The beads have the capture antibody covalently immobilized on a smaller surface area, requiring less capture antibody and smaller sample volumes, compared to ELISA, and non-specific binding is significantly reduced. Smaller sample volumes are important when working with limiting samples such as cerebrospinal fluid, synovial fluid, etc. 6. Multiplexing the assay further reduces sample volume requirements, enabling multiple results from a single sample.
Recent improvements by Luminex include: the new MAGPIX system, a smaller, less expensive, easier-to-use analyzer; Low-Concentration Magnetic MagPlex Microspheres which eliminate the need for expensive filter plates and come in a working concentration better suited for assay development and low-throughput applications; and the xMAP Antibody Coupling (AbC) Kit, which includes a protocol, reagents, and consumables necessary for coupling beads to the capture antibody of interest. (See Materials section for a detailed list of kit contents.)
In this experiment, we convert a pre-optimized ELISA assay for TNF-alpha cytokine to the xMAP platform and compare the performance of the two methods 7-11. TNF-alpha is a biomarker used in the measurement of inflammatory responses in patients with autoimmune disorders.
We begin by coupling four candidate capture antibodies to four different microsphere sets or regions. When mixed together, these four sets allow for the simultaneous testing of all four candidates with four separate detection antibodies to determine the best antibody pair, saving reagents, sample and time. Two xMAP assays are then constructed with the two most optimal antibody pairs and their performance is compared to that of the original ELISA assay in regards to signal strength, dynamic range, and sensitivity.

An ELISA can be easily converted to a Luminex xMAP assay and, through the benefits of multiplexing, several antibodies can be screened simultaneously to identify an optimum antibody pair, resulting in increased sensitivity and dynamic range, while reducing assay cost.

Umbau eines Capture-ELISA zu einem Luminex xMAP Assay unter Verwendung eines Multiplex-Antikörper-Screening-Methode

The enzyme-linked immunosorbent assay (ELISA) has long been the primary tool for detection of analytes of interest in biological samples for both life ...

Cell Tracking Using Photoconvertible Proteins During Zebrafish Development

Embryogenesis is a dynamic process that is best studied by using techniques that allow the documentation of developmental changes in vivo. The use of genetically-encoded fluorescent proteins has proven a valuable strategy for elucidating dynamic morphogenetic processes as they occur in the intact organism. During the past decade, the development of photoactivatable and photoconvertible fluorescent proteins has opened the possibility to investigate the fate of discrete subpopulations of tagged proteins1. Unlike photoactivatable proteins, photoconvertible fluorescent proteins (PCFPs) are readily tracked and imaged in their native emission state prior to photoconversion, making it easier to identify and select regions by optical inspection. PCFPs, such as Kaede2, KikGR3, Dendra4 and EosFP5, can be shifted from green to red upon exposure to UV or blue light due to a His-Tyr-Gly tripeptide sequence which forms a green chromophore that can be photoconverted to a red one by a light-catalyzed &beta;-elimination and subsequent extension of a &pi;-conjugated system3. PCFPs and their monomeric variants are useful tools for tracking cells6-10 and studying protein dynamics11-14, respectively. During recent years, PCFPs have been expressed in different animal model, such as zebrafish6, chicken7,8 and mouse9,10 for cell fate tracking. Here we report a protocol for cell-specific photoconversion of PCFPs in the living zebrafish embryo and further tracking of photoconverted proteins at later developmental stages. This methodology allows studying, in a tissue-specific manner, cell biological events underlying morphogenesis in the zebrafish animal model.

Here, we present a method for the photoactivated switch of photoconvertible fluorescent proteins (PCFPs) in the living zebrafish embryo and further tracking of photoconverted protein at specific time points during development. This methodology allows monitoring of cell biological events underlying different developmental processes in a live vertebrate organism.

Handy-Tracking Mit Photoconvertible Proteine ​​während der Zebrafisch-Entwicklung

Embryogenesis is a dynamic process that is best studied by using techniques that allow the documentation of developmental changes in vivo. The use of ...

Assessing Species-specific Contributions To Craniofacial Development Using Quail-duck Chimeras

The generation of chimeric embryos is a widespread and powerful approach to study cell fates, tissue interactions, and species-specific contributions to the histological and morphological development of vertebrate embryos. In particular, the use of chimeric embryos has established the importance of neural crest in directing the species-specific morphology of the craniofacial complex. The method described herein utilizes two avian species, duck and quail, with remarkably different craniofacial morphology. This method greatly facilitates the investigation of molecular and cellular regulation of species-specific pattern in the craniofacial complex.&#160;Experiments in quail and duck chimeric embryos have already revealed neural crest-mediated tissue interactions and cell-autonomous behaviors that regulate species-specific pattern in the craniofacial skeleton, musculature, and integument. The great diversity of neural crest derivatives suggests significant potential for future applications of the quail-duck chimeric system to understanding vertebrate development, disease, and evolution.

This article describes a method to generate chimeric embryos that are&#160;designed to test the species-specific contributions of neural crest and/or other tissues to craniofacial development.

Die Beurteilung artspezifische Um Beiträge Craniofacial Entwicklung mit Wachtel-Ente Chimären

The generation of chimeric embryos is a widespread and powerful approach to study cell fates, tissue interactions, and species-specific contributions to ...

Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control

It was recently demonstrated that recombinase polymerase amplification (RPA), an isothermal amplification platform for pathogen detection, may be used to quantify DNA sample concentration using a standard curve. In this manuscript, a detailed protocol for developing and implementing a real-time quantitative recombinase polymerase amplification assay (qRPA assay) is provided. Using HIV-1 DNA quantification as an example, the assembly of real-time RPA reactions, the design of an internal positive control (IPC) sequence, and co-amplification of the IPC and target of interest are all described. Instructions and data processing scripts for the construction of a standard curve using data from multiple experiments are provided, which may be used to predict the concentration of unknown samples or assess the performance of the assay. Finally, an alternative method for collecting real-time fluorescence data with a microscope and a stage heater as a step towards developing a point-of-care qRPA assay is described. The protocol and scripts provided may be used for the development of a qRPA assay for any DNA target of interest.

Provided is a protocol for developing a real-time recombinase polymerase amplification assay to quantify initial concentration of DNA samples using either a thermal cycler or a microscope and stage heater. Also described is the development of an internal positive control. Scripts are provided for processing raw real-time fluorescence data.

Entwicklung eines Quantitative Rekombinase Polymerase Amplification Assay mit einem internen positiven Kontrolle

It was recently demonstrated that recombinase polymerase amplification (RPA), an isothermal amplification platform for pathogen detection, may be used to ...

Using a Whole-mount Immunohistochemical Method to Study the Innervation of the Biliary Tract in Suncus murinus

This work describes the whole-mount immunohistochemistry staining method in detail, using neurofilament protein antibody to label the innervation of the biliary tract in Suncus murinus (S. murinus&#160;). First, the specimen was dissected from S. murinus and fixed in 4% paraformaldehyde (PFA). Second, an enzymatic treatment and potential endogenous peroxidase inactivation were performed. The specimen was then exposed to the primary antibody, anti-neurofilament protein antibody, for 3-6 days. It was then incubated with the secondary antibody conjugated with horseradish peroxidase. The color reaction was revealed by reacting the specimen with a 3,3&#39;-diaminobenzidine (DAB) substrate. This method can be applied to analyze the innervation of all visceral organs. Furthermore, this protocol can also be adapted to test other neuronal antibodies, but optimization of the antibodies should be done first. This method was originally introduced by Kuratani and Tanaka1,2,3.

Using a Whole-mount Immunohistochemical Method to Study the Innervation of the Biliary Tract in Suncus murinus

A whole-mount immunohistochemical approach, to visualize neurofilament protein expression in the extrahepatic biliary tract in Suncus murinus. is presented here. This protocol can be used to analyze the innervation of all visceral organs in S. murinus or other species.

Mit einer vollständigen immunhistochemischen Methode, um die Innervation der Gallenwege zu studieren<em&gt; Suncus murinus</em

This work describes the whole-mount immunohistochemistry staining method in detail, using neurofilament protein antibody to label the innervation of the ...

Mass Spectrometry Analysis to Identify Ubiquitylation of EYFP-tagged CENP-A (EYFP-CENP-A)

Studying the structure and the dynamics of kinetochores and centromeres is important in understanding chromosomal instability (CIN) and cancer progression. How the chromosomal location and function of a centromere (i.e., centromere identity) are determined and participate in accurate chromosome segregation is a fundamental question. CENP-A is proposed to be the non-DNA indicator (epigenetic mark) of centromere identity, and CENP-A ubiquitylation is required for CENP-A deposition at the centromere, inherited through dimerization between cell division, and indispensable to cell viability.
Here we describe mass spectrometry analysis to identify ubiquitylation of EYFP-CENP-A K124R mutant suggesting that ubiquitylation at a different lysine is induced because of the EYFP tagging in the CENP-A K124R mutant protein. Lysine 306 (K306) ubiquitylation in EYFP-CENP-A K124R was successfully identified, which corresponds to lysine 56 (K56) in CENP-A through mass spectrometry analysis. A caveat is discussed in the use of GFP/EYFP or the tagging of high molecular weight protein as a tool to analyze the function of a protein. Current technical limit is also discussed for the detection of ubiquitylated bands, identification of site-specific ubiquitylation(s), and visualization of ubiquitylation in living cells or a specific single cell during the whole cell cycle.
The method of mass spectrometry analysis presented here can be applied to human CENP-A protein with different tags and other centromere-kinetochore proteins. These combinatory methods consisting of several assays/analyses could be recommended for researchers who are interested in identifying functional roles of ubiquitylation.

Mass Spectrometry Analysis to Identify Ubiquitylation of EYFP-tagged CENP-A EYFP-CENP-A

CENP-A ubiquitylation is an important requirement for CENP-A deposition at the centromere, inherited through dimerization between cell division, and indispensable to cell viability. Here we describe mass spectrometry analysis to identify ubiquitylation of EYFP-tagged CENP-A (EYFP-CENP-A) protein.

Massenspektrometrieanalyse zur Identifizierung der Ubiquitylierung von EIFP-A mit EYFP-Tags (EYFP-CENP-A)

Studying the structure and the dynamics of kinetochores and centromeres is important in understanding chromosomal instability (CIN) and cancer ...

Quantification of Cell-Substrate Adhesion Area and Cell Shape Distributions in MCF7 Cell Monolayers

The methods presented here quantify some parameters of confluent adherent cell monolayers from multiple appropriately stained confocal images: adhesion to the substrate as a function of the number and size of focal adhesions, and cell shape, characterized by the cell shape index and other shape descriptors. Focal adhesions were visualized by paxillin staining and cell-cell borders were marked by junction plakoglobin and actin. The methods for cell culture and staining were standard; images represent single focal planes; image analysis was performed using publicly available image processing software. The presented protocols are used to quantify the number and size of focal adhesions and the differences in cell shape distribution in the monolayers, but they can be repurposed for the quantification of the size and shape of any other distinct cellular structure that can be stained (e.g., mitochondria or nuclei). Assessing these parameters is important in the characterization of the dynamic forces in adherent cell layer, including cell adhesion and actomyosin contractility that affects cell shape.

The article describes quantification of 1) the size and number of focal adhesions and 2) cell shape index and its distribution from confocal images of the confluent monolayers of MCF7 cells.

Quantifizierung von Zell-Substrat-Adhäsionsflächen und Zellformverteilungen in MCF7-Zell-Monolayern

The methods presented here quantify some parameters of confluent adherent cell monolayers from multiple appropriately stained confocal images: adhesion to ...