DataSheet1_Forest emissions reduction assessment from airborne LiDAR data using multiple machine learning approaches.docx

Objective: This study aims to evaluate the accuracy of different modeling methods and tree structural parameters extracted from airborne LiDAR for estimating carbon emissions reduction and assess their reliability as Certified Emission Reduction (CER) assessment techniques.Methods: LiDAR data was collected from an afforestation project in Beijing, China. Various modeling methods, including statistical regression and machine learning algorithms, were used to estimate biomass and carbon emissions reduction. The models were evaluated under two schemes: tree-species-specific modeling scheme (Scheme 1) and all-sample modeling scheme (Scheme 2) using cross-validation and compared with ground-based estimations and pre-estimated emission reductions.Results: Totally, the biomass estimation models in scheme 1 showed better accuracy than scheme 2. In scheme 1, The Random Forest (RF) and Cubist models achieved the highest prediction accuracy (R2 = 0.89, RMSE = 22.87 kg, CV RMSE = 52.00 kg), followed by GDBT and Cubist, with SVR and GAM performing the weakest. In scheme 2, Cubist model had the highest accuracy (R2 = 0.75, RMSE = 33.95 kg, CV RMSE = 36.05 kg), followed by RF and GBDT, with SVR and GAM performing the weakest. LiDAR-based estimates of carbon emissions reduction were closer to ground-based estimations and higher than pre-estimated values.Conclusion: This study demonstrates that LiDAR-based models using tree structural parameters can accurately assess carbon emissions reduction. The models outperformed traditional methods in terms of cost and accuracy. Considering tree species in the modeling process improved the accuracy of the models. LiDAR technology has the potential to be a reliable assessment technique for carbon emissions reduction in forestry projects. The pre-trained models can be used for multiple predictions, reducing the cost of carbon sink surveys. Overall, LiDAR-based models provide a promising approach for assessing carbon emissions reduction and can contribute to mitigating climate change.</p

Lineweaver-Burk plots for activation of α-arbutin on mushroom tyrosinase for the catalysis of L-Dopa at 30°C, pH 6.8.

<p>The reaction media (3.0 mL) contained 50 mM phosphate buffer (pH 6.8), different concentrations of L-Dopa assubstrate,different concentrations of α-arbutin and mushroom tyrosinase (6.67 µg/mL). Concentrations of α-arbutin for curves 1∼3 were 0, 5, 10 mmol·L⁻¹, respectively.</p

Progress curves for the inhibition of monophenolase of mushroom tyrosinase by α-arbutin at 30°C.

<p>The reaction media (3.0 mL) contained 0.5 mM L-tyrosine in 50 mM phosphate buffer (pH 6.8), the indicated concentration of α-arbutin, and mushroom tyrosinase (20 µg/mL). The concentrations of α-arbutin for curves 1∼4 were 0, 1.67, 3.34, 4.18 mmol·L⁻¹. The reaction was started by the addition of the enzyme.</p

Activation rate of diphenolase of mushroom tyrosinase by α-arbutin.

<p>Assay conditions: 3.0 ml 50 mM phosphate buffer pH 6.8, containing 0.5 mM L-Dopa, different concentrations of α-arbutin and mushroom tyrosinase (6.67 µg/mL).</p

Progress curves for the activation of diphenolase of mushroom tyrosinase by α-arbutin

<p>. The reaction media (3.0 mL) contained 0.5 mM L-Dopa in 50 mM phosphate buffer (pH 6.8), the indicated concentration of α-arbutin, and mushroom tyrosinase (6.67 µg/mL). The concentrations of α-arbutin for curves 1∼3 were 0, 5, 10 mmol·L⁻¹.</p

Kinetic parametes of diphenolase by α-arbutin.

<p>Kinetic parametes of diphenolase by α-arbutin.</p

Effects of α-arbutin on the enzyme activity and the lag time of monophenolase activity of mushroom tyrosinase.

<p>Assay conditions: 3.0 ml 50 mM phosphate buffer pH 6.8, containing 0.5 mM L-tyrosine. The reaction was started by the addition of the enzyme (20 µg/mL).</p

The chemical structure of ascorbic acid.

<p>The chemical structure of ascorbic acid.</p

The application of nanotechnology in kidney transplantation - supplementary material

Table S1 Nanotechnology for kidney transplantation</p

Additional file 1 of Low-intensity pulsed ultrasound promotes mesenchymal stem cell transplantation-based articular cartilage regeneration via inhibiting the TNF signaling pathway

Additional file 1 Table S1: Lists of hUC-MSCs qPCR primers. Table S2: Lists of C28/I2 qPCR primer