Questa ricerca è stata finanziata dai Fondi di ricerca fondamentale per le università centrali, numero di sovvenzione 2019GJZL04. Ringraziamo il professor Weisheng Zeng presso l'Academy of Forest Inventory and Planning, National Forestry and Grassland Administration, Cina per aver fornito l'accesso ai dati.

1. Preparazione dei dati
<ol><li>Preparare i dati di modellazione, che includono informazioni sui singoli alberi (specie e diametro all'altezza del seno a 1,3 m) e informazioni sulla trama (pendenza, aspetto e elevazione). In questo studio, i dati sono stati ottenuti dall'8 ° (2009) e dal 9 ° (2014) inventario forestale nazionale cinese nella provincia dello Xinjiang, nel nord-ovest della Cina, che include 21.898 osservazioni di 779 appezzamenti campione. Questi appezzamenti campione sono a forma quadrata con una dimensione di 1 Mu (unità di area cinese equivalente a 0,067 ha) e sono sistematicamente disposti su una griglia di 4 km x 8 km. NOTA: I dati per la modellazione (area basale) richiedono almeno un periodo di crescita (cioè due osservazioni).</li>
	<li>Dividi casualmente i dati in due set di dati, con l'80% dei dati dei grafici campione utilizzati per il raccordo del modello (set di dati di sviluppo del modello), che consiste in 17.145 osservazioni da 623 grafici campione e il 20% per la convalida del modello (set di dati di convalida del modello) che consiste in 4.753 osservazioni da 156 grafici campione. Le statistiche descrittive delle variabili chiave utilizzate sono fornite nella tabella 1. NOTA: questo passaggio della procedura di modellazione può essere omesso e tutti i dati vengono utilizzati per lo sviluppo del modello.</li>
</ol><table border="1" fo:keep-together.within-page="1"><tbody>
<tr>
<td rowspan="2">Variabili</td>
			<td colspan="4">Dati di montaggio</td>
			<td></td>
			<td colspan="4">Dati di convalida</td>
		</tr>
<tr>
<td>Minimo</td>
			<td>Massimo</td>
			<td>Significare</td>
			<td>S.d.</td>
			<td></td>
			<td>Minimo</td>
			<td>Massimo</td>
			<td>Significare</td>
			<td>S.d.</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 (alberi/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>La tabella 1. Statistiche di riepilogo per l'adattamento e la convalida dei dati. DBH1: diametro iniziale all'altezza del seno a 1,3 m (DBH), DBH2: DBH misurato dopo 5 anni di crescita, QMD: diametro medio quadratico, ID: incremento del diametro per 5 anni (DBH2 – DBH1), BAL: l'area basale degli alberi più grande dell'albero soggetto (l'albero soggetto: l'albero che è stato calcolato gli indici di competizione), NT: il numero di alberi per ettaro, BA: area basale per ettaro, EL: elevazione, S.D.: deviazione standard.
2. Sviluppo di modelli di base
<ol><li>Consultare i riferimenti per identificare le variabili che influiscono sugli incrementi dell'area basale dei singoli alberi.</li>
	<li>Selezionare e calcolare le variabili in base ai dati. In genere, l'incremento dell'area basale del singolo albero è influenzato da tre gruppi di variabili: dimensione dell'albero, competizione e condizionedel sito 27,28,29,30.<ol>
<li>Si consideri gli effetti delle dimensioni degli alberi quali DBH1, il quadrato di DBH1 ( <img alt="Equation 11" src="/files/ftp_upload/60827/60827eq11.jpg"> ), latrasformazione inversa di DBH 1 (1/DBH1) e il logaritmo comune di DBH1 (logDBH1) o le loro combinazioni.</li>
		<li>Si consideri gli effetti competitivi, come gli indici di concorrenza a una o due lati, per quantificare in modo più completo il livello di concorrenza vissuto da un albero, nonché la sua posizione sociale all'interno dello stand. La competizione unilaterale include BAL e l'indice di densità relativa (RD=DBH1/QMD); la competizione su due lati include NT e BA. NOTA: gli indici di concorrenza dipendenti dalla distanza dovrebbero essere presi in considerazione se i dati sono disponibili.</li>
		<li>Considerare che gli effetti del sito, ad esempio aspect (ASP), slope (SL) e EL. SL e ASP, devono essere inclusi utilizzando latrasformazione 31 diStage.</li>
	</ol>
</li>
	<li>Selezionare 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"> indica il quadrato di DBH2) come variabile dipendente.</li>
	<li>Sviluppare il modello di base utilizzando il metodo di regressione graduale. Assicurarsi che il modello sia biologicamente ragionevole e mostri differenze significative tra variabili indipendenti. Utilizzare il fattore di inflazione di varianza (VIF) per verificare la multicollinearità.</li>
	<li>Lasciare le variabili indipendenti con p < 0.05 e VIF < 5 nel modello di base.</li>
	<li>Produrre i risultati del modello di base e il plottaggio residuo. Il modello di base qui prodotto funge da base per l'ulteriore sviluppo di un modello ad effetti misti.</li>
</ol>3. Sviluppo lineare di modelli ad effetti misti con il pacchetto "nlme" nel software R
<ol><li>Leggere il set di dati di sviluppo del modello e caricare il pacchetto "nlme". >model.development.dataset=read.csv("E:/DATA/JoVE/modelingdata.csv", header=TRUE) >libreria(nlme)</li>
	<li>Selezionate i grafici campione come effetti casuali per sviluppare il modello degli effetti misti.</li>
	<li>Adatta tutte le possibili combinazioni di effetti casuali con il metodo di massima verosimiglianza (ML) e restituisci i risultati. >Modello<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML", casuale =~1| TRAMA) >riepilogo(Modello) 
	<ol>
<li>Set random =~1 è l'intercetta su parametri casuali. Modificare le istruzioni casuali fino a quando non vengono montate tutte le combinazioni. Ad esempio, per impostare 1/DBH1 e BAL come parametri casuali, il codice è il seguente: casuale =~1/DBH1+BAL-1. Inoltre, nel processo di montaggio, i codici possono segnalare errori dovuti alla non converenza del modello montato.</li>
	</ol></li>
	<li>Selezionare il modello migliore in base al criterio informativo (AIC) di Akaike, al criterio dell'informazione bayesiana (BIC), alla probabilità logaritmo (Loglik) e al test del rapporto di verosimiglianza (LRT). >anova(Modello.1, Modello.6) >anova(Modello.6, Modello.23) >anova(Modello.23, Modello.30)</li>
	<li>Determinare la struttura di Ri. Affrontare l'eterogeneità e l'autocorrelazione di Ri32. La Ri è scritta come segue: <img alt="Equation 1" src="/files/ftp_upload/60827/60827eq1.jpg">(1) dove σ2 è un fattore di scala sconosciuto che è uguale alla varianza residua del modello, G iè una matrice diagonale che descrive l'eterogeneità, e Γ i èuna matrice che descrive l'autocorrelazione.<ol>
<li>Osservare se i residui hanno eteroscedasticità dalla trama residua. Se c'è eteroscedasticità (i residui hanno un modello o una tendenza chiara), introdurre tre funzioni di varianza usate di frequente - la costante più la funzione di potenza, la funzione di potenza e la funzione esponenziale - per modellare la struttura di varianza degli errori. > La commissione per l'Model.30.1<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML",random=~1/DBH1+BAL+NT| Trama weights=varConstPower(form=~ montato(.))) > La commissione per l'riepilogo(Modello.30.1) > La commissione per l'Model.30.2<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML",random=~1/DBH1+BAL+NT| Trama weights=varPower(form=~ montato(.))) > La commissione per l'riepilogo(Modello.30.2) > La commissione per l'Model.30.3<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML",random=~1/DBH1+BAL+NT| Trama weights=varExp(form=~ montato(.))) >riepilogo(Modello.30.3)
</li>
		<li>Determinare la migliore funzione di varianza per il modello in base a AIC, BIC, Loglik e LRT. > La commissione per l'anova(Modello.30, Modello.30.1) > La commissione per l'anova(Modello.30, Modello.30.2) >anova(Modello.30, Modello.30.3)
</li>
		<li>Introdurre tre strutture di autocorrelazione comunemente utilizzate - la struttura di simmetria composta (CS), la struttura autoregressiva del primo ordine [AR(1)] e una combinazione di strutture autoregressive e medie mobili di primo ordine [ARMA(1,1)], per tenere conto dell'autocorrelazione. >Modello.30.3.1<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML", random=~1/DBH1+BAL+NT| PLOT, weights=varExp(form=~fitted(.)), corr= corCompSymm()) >riepilogo(Modello.30.3.1) >Modello.30.3.2<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML", random=~1/DBH1+BAL+NT| PLOT,weights=varExp(form=~ fitted(.)), corr=corAR1()) >riepilogo(Modello.30.3.2) >Modello.30.3.3<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML", random=~1/DBH1+BAL+NT| PLOT,weights=varExp(form=~ fitted(.)), corr=corARMA(q=1,p=1)) >riepilogo(Modello.30.3.3)
</li>
		<li>Determinare la migliore struttura di autocorrelazione in base a AIC, BIC, Loglik e LRT. >anova(Modello.30.3, Modello.30.3.2) NOTA: I Gi e Γi non possono essere definiti se non vi è eteroscedasticità e autocorrelazione.</li>
		<li>Produrre i risultati finali del modello ad effetti misti utilizzando il metodo REML (Restricted Maximum Likelihood). >Mixed.model<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="REML",random=~1/DBH1+BAL+NT| Trama weights=varExp(form=~ fitted(.)), corr=corAR1()) >riepilogo(Misto.modello)
</li>
	</ol></li>
</ol>4. Correzione dei pregiudizi
<ol><li>Trasformate i valori previsti dell'incremento dell'area basale utilizzando il modello finale su una scala logaritmica nella scala originale. Tuttavia, una tale trasformazione posteriore lineare del valore previsto da un modello trasformato da log produce una distorsione di trasformazione del log associata. Per gestire la distorsione del log, è stato derivato e integrato un fattore di correzione nell'equazione di previsione, che stima l'incremento effettivo previsto dell'area basale per un dato albero [Equazione (2)]: <img alt="Equation 2" src="/files/ftp_upload/60827/60827eq2.jpg">(2) dove <img alt="Equation 13" src="/files/ftp_upload/60827/60827eq13.jpg"> è previsto il valore logaritmico dell'incremento dell'area basale dal modello, <img alt="Equation 14" src="/files/ftp_upload/60827/60827eq14.jpg"> mentre è il valore di trasformazione posteriore previsto dell'incremento dell'area basale per 5 anni dopo la correzione per la distorsione della trasformazione del log. <img alt="Equation 15" src="/files/ftp_upload/60827/60827eq15.jpg"> è la varianza dagli effetti casuali nel grafico e σ2 è la varianza residua.</li>
	<li>Convertire l'incremento dell'area basale ( <img alt="Equation 14" src="/files/ftp_upload/60827/60827eq14.jpg"> ) nell'incremento del diametro.</li>
</ol>5. Previsione e valutazione del modello
<ol><li>Preparare il set di dati di convalida del modello prodotto nella sezione 1.2 per la stima.</li>
	<li>Utilizzate il modello lineare degli effetti misti per prevedere l'incremento dell'area basale a singolo albero. I componenti casuali sono stati calcolati utilizzando il seguente miglior predittore lineare imparziale: <img alt="Equation 3" src="/files/ftp_upload/60827/60827eq3.jpg">(3) dove <img alt="Equation 16" src="/files/ftp_upload/60827/60827eq16.jpg"> è un vettore per i componenti casuali; è la matrice varianza-covarianza per la variabilità tra grafici; è la matrice di progettazione per i componenti casuali che agiscono alle osservazioni complementari; è il vettore residuo le cui componenti sono date dalla differenza tra gli <img alt="Equation 17" src="/files/ftp_upload/60827/60827eq17.jpg"> <img alt="Equation 18" src="/files/ftp_upload/60827/60827eq18.jpg"> <img alt="Equation 19" src="/files/ftp_upload/60827/60827eq19.jpg"> incrementi dell'area basale e gli incrementi previsti utilizzando il modello ad effetti fissi.</li>
	<li>Valutare e confrontare la capacità predittiva del modello di base e del modello lineare degli effetti misti utilizzando i seguentitre indicatori statistici 23,33. <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) dove obji è l'area basale incrementa, est i è l'area basale prevista incrementi, <img alt="Equation 20" src="/files/ftp_upload/60827/60827eq20.jpg"> è la media delle osservazioni, e N è il numero di osservazioni.</li>
</ol>

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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>

Gli autori non hanno nulla da rivelare.

Una questione cruciale per lo sviluppo di modelli ad effetti misti è determinare quali parametri possono essere trattati come effetti casuali e quali dovrebbero essere considerati effettifissi 34,35. Sono stati proposti due metodi. L'approccio più comune è quello di trattare tutti i parametri come effetti casuali e quindi avere il miglior modello selezionato da AIC, BIC, Loglik e LRT. Questo è stato il metodo utilizzato dal nostro studio35</...

Rispetto alla monocoltura invecchiata uniforme, la gestione forestale di specie miste di età irregolare con molteplici obiettivi ha ricevuto recentementeuna maggiore attenzione 1,2,3. La previsione di diverse alternative di gestione è necessaria per formulare solide strategie di gestione forestale, in particolare per la complessa foresta di specie miste di età irregolare4. I modelli di crescita e resa delle foreste sono stati ampiamente utilizzati per prevedere lo sviluppo e il raccolto di alberi o standnell'ambito di vari schemi di gestione 5,6,7. I modelli di crescita e resa delle foreste sono classificati in modelli a albero singolo, modelli di classe di dimensioni e modelli di crescita interi6,7,8. Sfortunatamente, i modelli di classe di dimensioni e i modelli di stand interi non sono appropriati per le foreste di specie miste di età non uniforme, che richiedono una descrizione più dettagliata per sostenere il processo decisionale di gestione forestale. Per questo motivo, i modelli di crescita e resa dei singoli alberi hanno ricevuto una maggiore attenzione negli ultimi decenni a causa della loro capacità di fare previsioni per gli stand forestali con una varietà di composizioni, strutture e strategie digestione delle specie 9,10,11.
La regressione OLS (Ordinary Least Squares) è il metodo più comunemente utilizzato per lo sviluppo di modelli di crescita di singoli alberi12,13,14,15. I set di dati per i singoli modelli di crescita degli alberi raccolti ripetutamente per un periodo di tempo fisso sulla stessa unità di campionamento (ad esempio, grafico campione o albero) hanno una struttura stocastica gerarchica, con una mancanza di indipendenza e un'elevata correlazione spaziale e temporale tra leosservazioni 10,16. La struttura stocastica gerarchica viola le ipotesi fondamentali della regressione OLS: vale a dire residui indipendenti e dati normalmente distribuiti con varianze uguali. Pertanto, l'utilizzo della regressione OLS produce inevitabilmente stime distorte dell'errore standard delle stime dei parametriper questi dati 13,14.
I modelli ad effetti misti forniscono un potente strumento per analizzare i dati con strutture complesse, ad esempio dati di misure ripetute, dati longitudinali e dati a più livelli. I modelli ad effetti misti sono costituiti sia da componenti fissi, comuni alla popolazione completa, sia da componenti casuali, specifici di ogni livello di campionamento. Inoltre, i modelli ad effetti misti tengono conto dell'eterogeneità e dell'autocorrelazione nello spazio e nel tempo definendo matrici di struttura varianza-covarianza non diagonali17,18,19. Per questo motivo, i modelli ad effetto misto sono stati ampiamente utilizzati nella silvicoltura, come nei modelli di diametro-altezza20,21,modelli acorona 22,23,modelli auto-assottiglianti24,25e modelli dicrescita 26,27.
Qui, l'obiettivo principale era quello di sviluppare un modello di incremento dell'area basale a singolo albero utilizzando un approccio lineare ad effetti misti. Speriamo che l'approccio basato sugli effetti misti possa essere ampiamente applicato.

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

Qui, abbiamo sviluppato un modello a albero individuale di incrementi di area basale di 5 anni basati su un set di dati che include 21898 alberi picea asperata da 779 appezzamenti campione situati nella provincia dello Xinjiang, nel nord-ovest della Cina. Per evitare alte correlazioni tra le osservazioni della stessa unità di campionamento, abbiamo sviluppato il modello utilizzando un approccio lineare ad effetti misti con effetto grafico casuale per tenere conto della variabilità stocastica. Varie variabili ad albero e stand-level, come gli indici per le dimensioni degli alberi, la concorrenza e le condizioni del sito, sono state incluse come effetti fissi per spiegare la variabilità residua. Inoltre, l'eteroscedasticità e l'autocorrelazione sono state descritte introducendo funzioni di varianza e strutture di autocorrelazione. Il modello ottimale degli effetti misti lineari è stato determinato da diverse statistiche di adattamento: il criterio informativo di Akaike, il criterio dell'informazione bayesiana, la probabilità logaritmo e un test del rapporto di verosimiglianza. I risultati hanno indicato che variabili significative dell'incremento dell'area basale del singolo albero erano la trasformazione inversa del diametro all'altezza del seno, l'area basale degli alberi più grande dell'albero soggetto, il numero di alberi per ettaro e l'elevazione. Inoltre, gli errori nella struttura di varianza sono stati modellati con maggior successo dalla funzione esponenziale, e l'autocorrelazione è stata significativamente corretta dalla struttura autoregressiva di primo ordine (AR(1)). Le prestazioni del modello lineare degli effetti misti sono state significativamente migliorate rispetto al modello utilizzando la regressione ordinaria dei meno quadrati.

Il modello base di incremento dell'area basale per P. asperata è stato espresso come Equazione (7). Le stime dei parametri, i relativi errori standard e le statistiche sulla mancanza di adattamento sono riportati nella tabella 2. Il grafico residuo è illustrato nella figura 1. È stata osservata una pronunciata eterogeneità dei residui. <img alt="Equation 7" src="/files/ftp_upload/60827/60827eq7.jpg">(7)
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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

I modelli ad effetti misti sono strumenti flessibili e utili per analizzare i dati con una struttura stocastica gerarchica nella silvicoltura e potrebbero anche essere utilizzati per migliorare significativamente le prestazioni dei modelli di crescita forestale. Qui viene presentato un protocollo che sintetizza le informazioni relative ai modelli lineari ad effetti misti.

Sviluppo di un modello di incremento dell'area basale a albero individuale mediante un approccio lineare ad effetti misti

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 Views.  Beijing Forestry University. I modelli ad effetti misti sono strumenti flessibili e utili per analizzare i dati con una struttura stocastica gerarchica nella silvicoltura e potrebbero anche essere utilizzati per migliorare significativamente le prestazioni dei modelli di crescita forestale. Qui viene presentato un protocollo che sintetizza le informazioni relative ai modelli lineari ad effetti misti.

Video: 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.

Microfabbricazione post-Array-Detector (mPADs): un approccio per isolare le forze meccaniche

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

Ricerca

Biologia

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.

Spaziale Ottimizzazione multiobiettivo di pratiche di conservazione agricole utilizzando un modello SWAT e un algoritmo evolutivo

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.

Un approccio semi-quantitativo per valutare la formazione di biofilm mediante lo sviluppo spiegazzato Colony

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.

Conversione di un ELISA Capture ad un saggio Luminex xMAP utilizzando un anticorpo Multiplex metodo di screening

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.

Cella di rilevamento utilizzando proteine ​​Photoconvertible durante lo sviluppo Zebrafish

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.

Valutare Contributi specie-specifiche per lo sviluppo craniofacciale Uso Chimere Quaglia-anatra

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.

Sviluppo di un Quantitative ricombinasi Polymerase Amplificazione Assay con un controllo positivo interno

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.

Utilizzando un metodo immunohistochemico integrale per studiare l&#39;innervazione del tratto bipolare in<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.

Analisi della spettrometria di massa per identificare l'ubiquilazione del CENP-A con tag EYFP (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.

Quantificazione dell'area di adesione del substrato cellulare e della distribuzione delle forme cellulari nei monostrati cellulari MCF7

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