Bu araştırma, 2019GJZL04 hibe numarası olan Merkez Üniversiteler için Temel Araştırma Fonları tarafından finanse edildi. Orman Envanteri ve Planlaması Akademisi, Ulusal Ormancılık ve Çayır İdaresi, Çin'deki Profesör Weisheng Zeng'e verilere erişim sağladığı için teşekkür ederiz.

1. Veri hazırlama
<ol><li>Bireysel ağaç bilgilerini (1,3 m'de meme yüksekliğinde tür ve çap) ve çizim bilgilerini (eğim, yön ve yükseklik) içeren modelleme verilerini hazırlayın. Bu çalışmada, Çin'in kuzeybatısındaki Sincan Eyaleti'ndeki 8'inci (2009) ve 9'uncu (2014) Çin Ulusal Orman Envanteri'nden elde edilen veriler, 779 örneklem arsasının 21.898 gözlemini içermektedir. Bu örnek parseller 1 Mu (0.067 ha'ya eşdeğer Çince alan birimi) boyutuyla kare şeklindedir ve sistematik olarak 4 km x 8 km'lik bir ızgara üzerinde düzenlenmiştir. NOT: Modelleme (bazal alan) artışı için veriler en az bir büyüme dönemi (yani iki gözlem) gerektirir.</li>
	<li>Verileri, 623 örnek çizimden 17.145 gözlemden oluşan model sığdırma (model geliştirme veri kümesi) için kullanılan örnek çizimlerden elde edilen verilerin %80'i ve 156 örnek çizimden 4.753 gözlemden oluşan model doğrulama (model doğrulama veri kümesi) için %20'si olmak üzere iki veri kümesine rastgele bölün. Kullanılan anahtar değişkenler için açıklayıcı istatistikler Tablo 1'de verilmiştir. NOT: Modelleme yordamının bu adımı atlanabilir ve tüm veriler model geliştirme için kullanılır.</li>
</ol><table border="1" fo:keep-together.within-page="1"><tbody>
<tr>
<td rowspan="2">Değişken</td>
			<td colspan="4">Verileri sığdırma</td>
			<td></td>
			<td colspan="4">Doğrulama verileri</td>
		</tr>
<tr>
<td>Dk</td>
			<td>Max</td>
			<td>Demek</td>
			<td>S.d.</td>
			<td></td>
			<td>Dk</td>
			<td>Max</td>
			<td>Demek</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>Kimlik (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 (ağaçlar/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>Tablo 1. Verileri sığdırma ve doğrulama için özet istatistikler. DBH1: meme yüksekliğinde başlangıç çapı 1.3 m (DBH), DBH2: DBH 5 yıllık büyümeden sonra ölçüldü, QMD: ikinci dereceden ortalama çap, kimlik: 5 yıl boyunca çap artışı (DBH2 –DBH 1),BAL: konu ağacından daha büyük ağaçların bazal alanı (konu ağacı: rekabet endeksleri hesaplanan ağaç), NT: hektar başına ağaç sayısı, BA: hektar başına bazal alan, EL: yükseklik, S.D.: standart sapma.
2. Temel model geliştirme
<ol><li>Tek tek ağaç bazal alan artışlarını etkileyen değişkenleri tanımlamak için başvurulara başvurun.</li>
	<li>Verileri temel alan değişkenleri seçin ve hesaplayın. Genellikle, tek tek ağaç bazal alan artışı üç değişken grubundan etkilenir: ağaç boyutu, rekabet ve site durumu27,28,29,30.<ol>
<li>DBH 1 ,DBH 1karesi ( ),DBH 1'in <img alt="Equation 11" src="/files/ftp_upload/60827/60827eq11.jpg"> ters dönüşümü (1/DBH1) veDBH 1'inortak logaritması (logDBH1) veyabunlarınkombinasyonları gibi ağaç boyutu etkilerini göz önünde bulundurun.</li>
		<li>Bir ağacın yaşadığı rekabet seviyesini ve stand içindeki sosyal konumunu daha kapsamlı bir şekilde ölçmek için rekabetin hem tek hem de iki taraflı endeksleri gibi rekabetçi etkileri göz önünde bulundurun. Tek taraflı rekabet BAL ve göreli yoğunluk indeksini içerir (RD=DBH1/QMD); iki taraflı rekabet NT ve BA'yı içerir. NOT: Veriler mevcutsa mesafeye bağlı rekabet endeksleri dikkate alınmalıdır.</li>
		<li>Sahne Alanı'nın dönüşümü31kullanılarak aspect (ASP), eğim (SL) ve EL. SL ve ASP gibi site efektlerinin dahil edilmesi gerektiğini göz önünde bulundurun.</li>
	</ol>
</li>
	<li>Bağımlı değişken olarak 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"> (DBH2'ninkaresini gösterir) seçeneğini belirleyin.</li>
	<li>Adım adım regresyon yöntemini kullanarak temel modeli geliştirin. Modelin biyolojik olarak makul olduğundan ve bağımsız değişkenler arasında önemli farklılıklar sergilediğini sağlayın. Çok renkli olup olmadığını kontrol etmek için varyans enflasyon faktörünü (VIF) kullanın.</li>
	<li>Bağımsız değişkenleri temel modelde p < 0.05 ve VIF < 5 ile bırakın.</li>
	<li>Temel model sonuçlarını ve artık çizimi çıktısını elde edin. Burada üretilen temel model, karma efektli bir modelin daha da geliştirilmesi için bir temel görevi görür.</li>
</ol>3. R yazılımında "nlme" paketi ile doğrusal karışık efekt modeli geliştirme
<ol><li>Model geliştirme veri kümesini okuyun ve "nlme" paketini yükleyin. >model.development.dataset=read.csv("E:/DATA/JoVE/modelingdata.csv", üstbilgi=DOĞRU) >kitaplık(nlme)</li>
	<li>Karışık efekt modelini geliştirmek için örnek çizimleri rastgele efektler olarak seçin.</li>
	<li>Rastgele efektlerin olası tüm kombinasyonlarını maksimum olasılık (ML) yöntemiyle sığdırın ve sonuçları çıktısını çıkarın. >Model<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML", rastgele =~1| ARSA) >özet(Model) 
	<ol>
<li>Random =~1'i ayarlamak, rastgele parametrelere kesişme noktasıdır. Tüm birleşimler takılana kadar rasgele deyimleri değiştirin. Örneğin, 1/DBH1 ve BAL'ı rasgele parametreler olarak ayarlamak için kod aşağıdaki gibidir: random =~1/DBH1+BAL-1. Ek olarak, montaj sürecinde, kodlar takılan modelin nonconvergence nedeniyle hataları bildirebilir.</li>
	</ol></li>
	<li>Akaike'nin bilgi kriteri (AIC), Bayes bilgi kriteri (BIC), logaritma olasılığı (Loglik) ve olasılık oranı testi (LRT) ile en iyi modeli seçin. >anova(Model.1, Model.6) >anova(Model.6, Model.23) >anova(Model.23, Model.30)</li>
	<li>Ri'ninyapısını belirleyin. Ri32'ninheterosklastisitesini ve otomatik düzeltilmesini ele alın. Ri aşağıdaki gibi yazılmıştır: <img alt="Equation 1" src="/files/ftp_upload/60827/60827eq1.jpg">(1) burada σ2, model kalıntısı varyansına eşit bilinmeyen bir ölçekleme faktörüdür, Gi heteroskedastisiteyi tanımlayan çapraz bir matristir ve Γi otomatik ilişkiyi tanımlayan bir matristir.<ol>
<li>Artıkların artık arsadan heteroskedasiteye sahip olup olmadığını gözlemleyin. Heterosklastisite varsa (artıkların açık bir deseni veya eğilimi vardır), hataların varyans yapısını modellemek için sık kullanılan üç varyans işlevini (sabit artı güç işlevi, güç işlevi ve üstel işlev) tanıtın. >Model.30.1<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML",random=~1/DBH1+BAL+NT| Arsa weights=varConstPower(form=~ takılmış(.))) >özet(Model.30.1) >Model.30.2<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML",random=~1/DBH1+BAL+NT| Arsa weights=varPower(form=~ takılmış(.))) >özet(Model.30.2) >Model.30.3<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML",random=~1/DBH1+BAL+NT| Arsa weights=varExp(form=~ takılmış(.))) >özet(Model.30.3)
</li>
		<li>AIC, BIC, Loglik ve LRT'ye göre model için en iyi varyans işlevini belirleyin. >anova(Model.30, Model.30.1) >anova(Model.30, Model.30.2) >anova(Model.30, Model.30.3)
</li>
		<li>Otomatik ilişkiyi hesaba katmak için yaygın olarak kullanılan üç otomatik ilişki yapısını (bileşik simetri yapısı (CS), birinci dereceden otomatik gerileyen yapı [AR(1)] ve birinci dereceden otomatik gerileyen ve hareketli ortalama yapıların [ARMA(1,1)] birleşimini tanıtın. >Model.30.3.1<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML", random=~1/DBH1+BAL+NT| ARSA, weights=varExp(form=~fitted(.)), corr= corCompSymm()) >özet(Model.30.3.1) >Model.30.3.2<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML", random=~1/DBH1+BAL+NT| ARSA,weights=varExp(form=~ takılmış(.)), corr=corAR1()) >özet(Model.30.3.2) >Model.30.3.3<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="ML", random=~1/DBH1+BAL+NT| ARSA,weights=varExp(form=~ fitted(.)), corr=corARMA(q=1,p=1)) >özet(Model.30.3.3)
</li>
		<li>AIC, BIC, Loglik ve LRT'ye göre en iyi otomatik ilişki yapısını belirleyin. >anova(Model.30.3, Model.30.3.2) NOT: Heteroskedasticity ve autocorrelation yoksa Gi ve Φi tanımlanamaz.</li>
		<li>Sınırlı maksimum olasılık (REML) yöntemini kullanarak karma efekt modelinin nihai sonuçlarını çıktısını elde edin. >Mixed.model<-lme(Y~1/DBH1+BAL+NT+EL,data=model.development.dataset, method="REML",random=~1/DBH1+BAL+NT| Arsa weights=varExp(form=~ takılmış(.)), corr=corAR1()) >özet(Mixed.model)
</li>
	</ol></li>
</ol>4. Önyargı düzeltmesi
<ol><li>Logaritmik ölçekteki son modeli kullanarak bazal alan artışı için öngörülen değerleri özgün ölçeğe dönüştürün. Ancak, günlük dönüştürülmüş bir modelden tahmin edilen değerin böyle doğrusal bir geri dönüşümü ilişkili bir günlük dönüştürme yanlılığı üretir. Günlük yanlılığıyla başa çıkmak için, belirli bir ağaç için gerçek tahmin edilen bazal alan artışlarını tahmin eden bir düzeltme faktörü türetilmiş ve tahmin denklemine entegre edilmiştir [Denklem (2)]: <img alt="Equation 2" src="/files/ftp_upload/60827/60827eq2.jpg">(2) burada <img alt="Equation 13" src="/files/ftp_upload/60827/60827eq13.jpg"> modelden bazal alan artışı logaritmik değeri tahmin edilirken, <img alt="Equation 14" src="/files/ftp_upload/60827/60827eq14.jpg"> log-dönüşüm yanlılığı için düzeltildikten sonra 5 yıl boyunca bazal alan artışlarının tahmin edilen geri dönüştürülmüş değeri <img alt="Equation 15" src="/files/ftp_upload/60827/60827eq15.jpg"> σ dir.</li>
	<li>Bazal alan artış ( <img alt="Equation 14" src="/files/ftp_upload/60827/60827eq14.jpg"> ) çap artışına dönüştürün.</li>
</ol>5. Model tahmini ve değerlendirmesi
<ol><li>Bölüm 1.2'de üretilen model doğrulama veri kümesini tahmin için hazırlayın.</li>
	<li>Tek tek ağaç bazal alan artışını tahmin etmek için doğrusal karışık efektler modelini kullanın. Rastgele bileşenler aşağıdaki en iyi doğrusal tarafsız tahmin edici kullanılarak hesaplanmıştır: <img alt="Equation 3" src="/files/ftp_upload/60827/60827eq3.jpg">(3) <img alt="Equation 16" src="/files/ftp_upload/60827/60827eq16.jpg">rastgele bileşenler için bir vektör <img alt="Equation 17" src="/files/ftp_upload/60827/60827eq17.jpg"> nerededir; çizimler arasındaki değişkenlik için varyans-kovaryans <img alt="Equation 18" src="/files/ftp_upload/60827/60827eq18.jpg"> matrisidir; tamamlayıcı gözlemlerde hareket eden rastgele bileşenler için tasarım matrisidir; bileşenleri <img alt="Equation 19" src="/files/ftp_upload/60827/60827eq19.jpg"> bazal alan artışları ile sabit efektler modeli kullanılarak öngörülen artışlar arasındaki farkla verilen artık vektördür.</li>
	<li>Aşağıdaki üç istatistiksel göstergeyi kullanarak temel modelin ve doğrusal karma efektler modelinin tahmin yeteneğini değerlendirin ve karşılaştırın23,33. <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) burada obji bazal alan artışlarıdır, esti tahmin edilen bazal alan artışlarıdır, <img alt="Equation 20" src="/files/ftp_upload/60827/60827eq20.jpg"> gözlemlerin ortalamasıdır ve N gözlem sayısıdır.</li>
</ol>

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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>

Yazarların açıklayacak bir şeyi yok.

Karma efektli modellerin geliştirilmesi için önemli bir konu, hangi parametrelerin rastgele efektler olarak değerlendirilebileceğini ve hangilerinin sabit efekt olarak kabul edilmesi gerektiğini belirlemektir34,35. İki yöntem önerildi. En yaygın yaklaşım, tüm parametreleri rasgele efektler olarak ele almak ve ardından AIC, BIC, Loglik ve LRT tarafından seçilen en iyi modele sahip olmaktır. Çalışmamızın kullandığı yöntem buydu<sup class="...

Çift yaşlı monokültür ile karşılaştırıldığında, birden fazla hedefe sahip düzensiz yaşlı karma tür orman yönetimi son zamanlarda artan ilgi gördü1,2,3. Farklı yönetim alternatiflerinin öngörüsü, özellikle karmaşık düzensiz yaşlanmış karma türler ormanı4için sağlam orman yönetim stratejileri oluşturmak için gereklidir. Orman büyüme ve verim modelleri, çeşitli yönetim şemaları5,6,7altında ağaç veya stand geliştirme ve hasat tahmin etmek için yaygın olarak kullanılmıştır. Orman büyüme ve verim modelleri bireysel ağaç modelleri, boyut sınıfı modeller ve tüm stand büyüme modelleri6, 7,8olarak sınıflandırılır. Ne yazık ki, boyut sınıfı modeller ve tüm stand modelleri, orman yönetimi karar verme sürecini desteklemek için daha ayrıntılı bir açıklama gerektiren düzensiz yaşlı karma tür ormanları için uygun değildir. Bu nedenle, bireysel ağaç büyüme ve verim modelleri, çeşitli tür kompozisyonları, yapıları ve yönetim stratejileri 9 , 10,11ile orman standları için tahminlerde bulunma yetenekleri nedeniyle son birkaç on yıl boyunca artan ilgi gördü.
Sıradan en az kareler (OLS) gerilemesi, bireysel ağaç büyüme modellerinin geliştirilmesi için en yaygın kullanılan yöntemdir12,13,14,15. Aynı örnekleme ünitesinde (örn. örnek arsa veya ağaç) sabit bir süre boyunca tekrar tekrar toplanan bireysel ağaç büyüme modelleri için veri kümeleri, bağımsızlık eksikliği ve gözlemler arasında yüksek mekansal ve zamansal korelasyon ile hiyerarşik bir stokastik yapıya sahiptir10,16. Hiyerarşik stokastik yapı OLS gerilemesinin temel varsayımlarını ihlal eder: yani bağımsız artıklar ve normalde eşit farklılıklara sahip dağıtılmış veriler. Bu nedenle, OLS regresyonunun kullanımı kaçınılmaz olarak bu veriler için parametre tahminlerinin standart hatasının önyargılı tahminlerini üretir13,14.
Karma efekt modelleri, yinelenen ölçü verileri, boyuna veriler ve çok düzeyli veriler gibi karmaşık yapılarla verileri analiz etmek için güçlü bir araç sağlar. Karışık efekt modelleri, hem tam popülasyonda ortak olan sabit bileşenlerden hem de her örnekleme düzeyine özgü rastgele bileşenlerden oluşur. Buna ek olarak, karışık efektli modeller, diyagonal olmayan varyans-kovaryans yapısı matrisleri17 , 18,19tanımlayarak uzay ve zamanda heterosklastisiteyi ve otomatik tutarlılığı dikkate alır. Bu nedenle, karma efekt modelleri, çap yüksekliği modelleri20 , 21, taç modelleri 22,23,kendiliğinden inceltme modelleri24,25ve büyüme modelleri26,27gibi ormancılıkta yaygın olarak kullanılmaktadır.
Burada temel amaç, doğrusal bir karma efekt yaklaşımı kullanarak bireysel ağaç bazal alan artış modeli geliştirmekti. Karma efekt yaklaşımının geniş çapta uygulanabileceğini umuyoruz.

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			<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>
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			<td height="21" style="height:21px;">Microsoft Office 2013</td>
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			<td height="21" style="height:21px;">R x64 3.5.1</td>
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			<td></td>
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	</tbody>
</table>

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

Burada, Çin'in kuzeybatısındaki Sincan Eyaleti'nde bulunan 779 örnek arsadan 21898 Picea asperata ağacı içeren bir veri kümesine dayanarak 5 yıllık bazal alan artışlarından oluşan bir bireysel ağaç modeli geliştirdik. Aynı örnekleme ünitesinden gözlemler arasında yüksek korelasyonları önlemek için, stochastic değişkenliği hesaba katmak için rastgele çizim etkisine sahip doğrusal bir karma efekt yaklaşımı kullanarak modeli geliştirdik. Ağaç boyutu, rekabet ve site durumu endeksleri gibi çeşitli ağaç ve stand düzeyi değişkenleri, artık değişkenliği açıklamak için sabit efektler olarak dahil edildi. Ayrıca varyans fonksiyonları ve otokorrelasyon yapıları getirilerek heterosklastisite ve otokorrelasyon tanımlanmıştır. Optimal lineer karma efekt modeli birkaç uygun istatistikle belirlendi: Akaike'nin bilgi kriteri, Bayes bilgi kriteri, logaritma olasılığı ve olasılık oranı testi. Sonuçlar, bireysel ağaç bazal alan artışındaki önemli değişkenlerin, meme yüksekliğinde çapın ters dönüşümü, konu ağaçtan daha büyük ağaçların bazal alanı, hektar başına ağaç sayısı ve yükseklik olduğunu gösterdi. Ayrıca, varyans yapısındaki hatalar en başarılı şekilde üstel işlev tarafından modellenmiş ve otomatik ilişki birinci dereceden otoregressif yapı (AR(1)) ile önemli ölçüde düzeltilmiştir. Doğrusal karışık efektler modelinin performansı, sıradan en az kare gerilemesi kullanılarak modele göre önemli ölçüde geliştirilmiştir.

P. asperata için temel bazal alan artış modeli Denklem (7) olarak ifade edildi. Parametre tahminleri, karşılık gelen standart hataları ve uygun olmayan istatistikler Tablo 2'de gösterilmiştir. Kalan arsa Şekil 1'de gösterilmiştir. Kalıntılarda belirgin heterosklastisite gözlendi. <img alt="Equation 7" src="/files/ftp_upload/60827/60827eq7.jpg">(7)
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<td></td>
			<td>Tahmin</td>
	...

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

Karma efekt modelleri, ormancılıkta hiyerarşik stokastik bir yapıya sahip verileri analiz etmek için esnek ve kullanışlı araçlardır ve orman büyüme modellerinin performansını önemli ölçüde artırmak için de kullanılabilir. Burada, doğrusal karma efekt modelleri ile ilgili bilgileri sentezleyen bir protokol sunulmuştur.

Doğrusal Karma EfektLer Yaklaşımı Kullanılarak Tek Tek Ağaç Bazal Alan Artış Modelinin Geliştirilmesi

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

3.3K Görüntüleme.  Beijing Forestry University. Karma efekt modelleri, ormancılıkta hiyerarşik stokastik bir yapıya sahip verileri analiz etmek için esnek ve kullanışlı araçlardır ve orman büyüme modellerinin performansını önemli ölçüde artırmak için de kullanılabilir. Burada, doğrusal karma efekt modelleri ile ilgili bilgileri sentezleyen bir protokol sunulmuştur.

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.

Microfabricated Post-Dizi-Dedektörleri (mPADs): Mekanik Kuvvetleri yalıtın bir Yaklaşım

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

Biyoloji

Development of New Therapeutic Applications Using Microfluidics

Mikroakiskan kullanarak Yeni Tedavi Uygulamaları Geliştirme

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.

SWAT modeli kullanarak Tarım Koruma Uygulamalarının Mekansal Çok Amaçlı Optimizasyon ve Evrimsel Algoritma

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.

Buruşuk Koloni Gelişimi kullanarak Biyofilm Oluşumu değerlendirin Bir Yarı-kantitatif Yaklaşım

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.

Bir Multiplex Antikor Tarama Yöntemi kullanılarak bir Luminex xMAP Tahlil için bir Capture ELISA Dönüşüm

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.

Zebra balığı Geliştirme Sırasında Photoconvertible Proteinler kullanma Cep Takip

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.

Bıldırcın-ördek Chimeras kullanma Kraniyofasiyal Kalkınma için Türlere özgü Katılımlar değerlendirilmesi

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.

İç Pozitif Kontrol ile Kantitatif Recombinase Polimeraz Amplifikasyon Testi Geliştirme

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.

Safra Yolunun İnternalasyonunu İncelemek İçin Bir Tam Montajlı İmmünohistokimyasal Yöntem Kullanmak<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.

Doğrusal Karma EfektLer Yaklaşımı Kullanılarak Tek Tek Ağaç Bazal Alan Artış Modelinin Geliştirilmesi

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