Optimal Tree Model Research Articles

Framework of Virtual Plantation Forest Modeling and Data Analysis for Digital Twin

Plantation forests, cultivated through artificial seeding and planting methods, are of great significance to human society. However, most experimental sites for these forests are located in remote areas. Therefore, in-depth studies on remote forest management and off-site experiments can better meet the experimental and management needs of researchers. Based on an experimental plantation forest of Triploid Populus Tomentosa, this paper proposes a digital twin architecture for a virtual poplar plantation forest system. The framework includes the modeling of virtual plantation and data analysis. Regarding this system architecture, this paper theoretically analyzes the three main entities of the physical world, digital world, and researchers contained in it, as well as their interaction mechanisms. For virtual plantation modeling, a tree modeling method based on LiDAR point cloud data was adopted. The transitional particle flow method was proposed to combine with AdTree method for tree construction, followed by integration with other models and optimization. For plantation data analysis, a database based on forest monitoring data was established. Tree growth equations were derived by fitting the tree diameter at breast height data, which were then used to predict and simulate trends in diameter-related data that are difficult to measure. The experimental result shows that a preliminary digital twin-oriented poplar plantation system can be constructed based on the proposed framework. The system consists of 2160 trees and simulations of 10 types of monitored or predicted data, which provides a new practical basis for the application of digital twin technology in the forestry field. The optimized tree model consumes over 67% less memory, while the R2 of the tree growth equation with more than 100 data items could reach more than 87%, which greatly improves the performance and accuracy of the system. Thus, utilizing forestry information networking and digitization to support plantation forest experimentation and management contributes to advancing the digital transformation of forestry and the realization of a smart management model for forests.

Prediction of molecular diffusivity of organic molecules based on group contribution with tree optimization and SVM models

Prediction of mass transfer diffusion coefficient as a functional of group contribution in the molecular structure was carried out using machine learning techniques. Two machine learning methods including Tree Optimization (TO) and SVM (Support Vector Machine) were implemented to simulate the values of diffusion coefficients for various compounds. A bunch of data were collected from references for the diffusivity of nonelectrolyte organic molecules at infinite dilution in aqueous solution. The results can be beneficial for design and applications for wastewater treatment processes where the organic molecules must be removed from aqueous streams. For the modeling, 148 diverse functional groups were taken into account as the model’s inputs, while the diffusivity of the compound was taken as the sole model’s output in the computational study in this work. For modeling of the diffusion coefficients, 3000 datasets are chosen at random for the training procedure of the machine learning models. The simulation results revealed that the optimized Tree model is better at estimating the output parameter. The SVM model, on the other hand, can only forecast the outcome marginally with low accuracy compared to the Tree model.

26 PREDICTION OF PREOPERATIVE CHEMOTHERAPY FOR ESOPHAGEAL CANCER USING ARTIFICIAL INTELLIGENCE

Abstract Currently, preoperative chemotherapy is usually done for advanced esophageal cancer according to guidelines. It is still difficult to predict the effect of chemotherapy before the chemotherapy. If the effect would be poor, the patient may miss the possibility of treatment. We have been examining the possibility of predicting the chemotherapy effect based on test results before and during chemotherapy. In this study we used artificial intelligence to predict the effect of chemotherapy. Methods 80 patients who underwent preoperative chemotherapy for esophageal cancer in our department from 2012 to 2019. We predict the chemotherapy effect according to age, sex, type of chemotherapy (FP/DCF), number of chemotherapy, T, M, N, blood examination results before chemotherapy as WBC, neutrophil count, lymphocyte count, hemoglobin, platelets, total protein, Albumin, CRP, IgG, IgA, IgM, IL6, creatinine, lowest WBC, neutrophil and lymphocyte count, during chemotherapy. The effect of chemotherapy was classified: CR, PR, and PD. Prediction were performed using MatLab R2019b classification learner and Neural Net Pattern Recognition. Results The actual effect was CR/PR/SD/PD = 0/33/42/5 cases. The correct answer rate was 66.3% for the optimized tree model, 66.3% for the optimized Naïve Bayesian model, 67.5% for the optimized SVM model, and 56.3% for the optimized ensemble model. With optimized SVM, the sensitivity to predict PD was 20%, specificity was 94.5%, and AUC was 0.83. Moreover, the sensitivity to predict PR was 75.8%, and the specificity was 76.6%. With Neural Net Pattern Recognition, the correct answer rate was 90%, and the sensitivity to predict PD was 20%, specificity was 94.7%. Conclusion In this study, Neural Net Pattern Recognition as a deep learning model could predict the effect of preoperative chemotherapy more accurately than that of classification learner. The poor prediction of PD was due to the small number of teacher models, and the prediction of PR with many teacher models was more accurate. We will add more teacher models, and establish a prediction model that can be used clinically for both sensitivity and specificity.

A fast and powerful tree-based association test for detecting complex joint effects in case-control studies.

Multivariate tests derived from the logistic regression model are widely used to assess the joint effect of multiple predictors on a disease outcome in case-control studies. These tests become less optimal if the joint effect cannot be approximated adequately by the additive model. The tree-structure model is an attractive alternative, as it is more apt to capture non-additive effects. However, the tree model is used most commonly for prediction and seldom for hypothesis testing, mainly because of the computational burden associated with the resampling-based procedure required for estimating the significance level. We designed a fast algorithm for building the tree-structure model and proposed a robust TREe-based Association Test (TREAT) that incorporates an adaptive model selection procedure to identify the optimal tree model representing the joint effect. We applied TREAT as a multilocus association test on >20 000 genes/regions in a study of esophageal squamous cell carcinoma (ESCC) and detected a highly significant novel association between the gene CDKN2B and ESCC ([Formula: see text]). We also demonstrated, through simulation studies, the power advantage of TREAT over other commonly used tests. The package TREAT is freely available for download at http://www.hanzhang.name/softwares/treat, implemented in C++ and R and supported on 64-bit Linux and 64-bit MS Windows. yuka@mail.nih.gov Supplementary data are available at Bioinformatics online.

Tree Model Optimization Criterion without Using Prediction Error

The use of prediction error to optimize the number of splitting rules in a tree model does not control the probability of the emergence of splitting rules with a predictor that has no functional relationship with the target variable. To solve this problem, a new optimization method is proposed. Using this method, the probability that the predictors used in splitting rules in the optimized tree model have no functional relationships with the target variable is confined to less than 0.05. It is fairly convincing that the tree model given by the new method represents knowledge contained in the data.

Real-Life Implementation of Treatment Guidelines for Adjuvant Chemotherapy in Invasive Breast Cancer: Retrospective Analysis of a Large Single Centre 10-Year Database.

Abstract Background and objective: Decision to administer adjuvant chemotherapy to N-/M- invasive breast cancer (IBC) patients remains a clinical challenge. Several patient-related factors, such as age, tumor size and grade, or hormone receptor (HR) and HER2 status are incorporated in treatment guidelines. Recommendations have become more complex with time and availability of additional tests. Using the breast cancer database developed at Institut Paoli Calmettes (IPC) from 1999 onwards, our objective was to analyze the importance of key patient-related factors in chemotherapy decision and their evolution over time.Methods: Using our database containing 5854 N-/M- IBC patients, descriptive statistics and classification tree analysis (CART) examined the following factors: age (cut-off 40y), tumor size (cut-off 20 mm), SBR histologic grade, HR positivity, perivascular infiltration (PVI). At IPC, HER2 became clinical routine from 2000 onwards, and cut-off for tumor size was reduced to 15 mm after 2005.Consequently, a second cohort comprised of patients diagnosed between 2000 and 2005 with homogeneous diagnosis and management practices was analysed.Results: SBR-1 and SBR-2 represented 32% and 46% of all cases (n=5854) resp., and SBR-3 22%. Distribution of patients for the other factors of interest was: tumor size > 20 mm for 27%, HR+ for 80%, age >= 40 yr for 91%, PVI+ for 24%. Adjuvant chemotherapy was administered to 1597 patients (27,3%). The optimal tree model based on pruning and 10-fold cross-validation correctly predicted 83.5% of decision, with 93% sensibility and 61% specificity. SBR was the most important t decision factor, allowing to improve the prediction rate from 72.7% to 76.4%. The next factors were date of diagnosis, then size for SBR 1-2, and age for SBR3. Adjuvant chemotherapy was more frequent after 2000 than before: 34.2% vs 8.9% resp. for SBR1-2, and 83.2% vs 39.3% resp.for SBR3. In the 2000-2005 cohort (n=1658, SBR1=37%, SBR2=40%, SBR3=23%), chemotherapy was chosen for 754 patients (45.5%). The optimal tree model based on pruning and 10-fold cross-validation correctly predicted 79.3% of cases with 82.8% sensibility and 76.4% specificity. SBR was again the primary decision factor, improving the prediction rate from 55.5% to 75.0%. For SBR1-2 chemotherapy was used in 35% of patients, and tumor size, PVI and age were the next decision factors. For the SBR-3, grade was the only relevant decision factor in the algorithm, leading to chemotherapy in 82% of patients.Conclusion: Hierarchy of factors driving chemotherapy decision is remarkably stable over time. Tumor grade, a major indicator of proliferation, remains the most contributive factor for adjuvant chemotherapy in N-/M- IBC, despite the progressive availability of new pathologic indicators over time. Considering the variability of tumor grading by histology across centers, and the limited informative power of intermediate SBR-2 grade, improvement in tumor grading may provide a simple approach to refine currently validated treatment algorithms and allow for a more individualized treatment strategy. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 2089.

Model-Based Conifer Canopy Surface Reconstruction from Photographic Imagery

signed to extract canopy surfaces with high variability in the Canopy surface data are desirable in forestry, but they are vertical direction. difficult to collect in the field. Existing surface reconstruction Sheng et al. (2001) introduced a model-based photogramtechniques cannot adequately extract canopy surfaces, es- metric approach to tree crown surface reconstruction. They expecially for conifer stands. This paper develops an integrated pressed the crown morphology of a tree using 3D geometric model-based approach to reconstruct canopy surface for models and modeled it as a generalized 3D hemi-ellipsoid. conifer stands analytically from the crown level. To deal with They manually established the optimal tree model from aerial dense stands, critical problems are addressed in the process photos, and used the initial crown surface derived from the of model-based surface reconstruction. These include the tree model to guide image matching in crown surface reconocclusion problem in disparity (parallax) prediction from tree struction. The potential of this model-based approach was models, the edge effect of tree models on the disparity map, demonstrated using a single tree. When reconstructing the canand the foreshortening effect in image matching. The model- opy surface of a dense stand of multiple trees, we need to estabbased approach was applied to recover the canopy surface of lish optimal tree models in a more efficient manner and cona dense redwood stand using images scanned from 1:2,400- sider problems such as occlusion. This paper extends the scale aerial photographs. Compared with field measurements, capability of the model-based approach from recovering the crown radius and tree height derived from the reconstructed crown surface of a single tree to reconstructing the canopy surcanopy surface model have an overall accuracy of 92 percent face of a tree stand, and further develops the model-based and 94 percent, respectively. The results demonstrate the method to canopy surface reconstruction for complicated tree approach’s ability to reconstruct complicated stands. stands. The improved method addresses the problems of occlusion, foreshortening, and tree edge effects, and was applied to