dataesr/scientific-paragraphs-categorization huggingface dataset:
Following forestry inventory standards, a multi-stem tree is considered to be one tree if the beginning of the fork is above 1. 3 m AGL. Conversely, it is inventoried as multiple trees if the fork is below 1. 3 m AGL. Trees that were identified as multiple trees by the field inventory, but as one tree in the TLS dataset (i. e. , 21 trees; 7%), were problematic for validating ITD performance. Therefore, they were excluded from the second analysis. Our tree matching algorithm that identified Ntrees paired was implemented in R, and it was derived from the three-step procedure proposed by  [43]. We set the TLS tree crown centroid as reference for the matching procedure instead of using the x,y value at the bottom of the stem  [43]. This minimized matching errors for tilted or curved stems. Crown centroids of ALS/ULS trees (hereafter, referred to as "test") were used to test any possible match with TLS tree crown centroids (hereafter, referred to as "reference").
ITD Performance Automatic ITD algorithms over the stand detected 57%-73% of Ntrees det , of which 40% to 71% were Ntrees paired (Table  4 ). The best ITD performance was obtained from the ULS-V-Pcloud dataset that was acquired in leaf-off conditions using the SimpleTree bottom-up ITD (Figure  11D ), with a total of 71% Ntrees det and 71% Ntrees paired (Table  4 ). The best raster-based ITD performance was achieved in leaf-off conditions on ULS-V-Raster, with a total of 73% Ntrees det and 50% Ntrees paired. Table  4. Detailed ITD performance that was obtained from each aerial dataset with their main acquisition parameters and the ITD algorithm that was applied. Ntrees det represents the total number of trees that detected over the stand among the N = 477 trees that were measured in the field inventory (Ntrees FI ).
Ntrees paired represents the number of Ntrees det that formed matched pairs with the N = 258 TLS reference trees (Ntrees TLS ) and are presented by height class. Point density differences between ULS-R (353 points/m 2 , Figure  11A ) and ALS point cloud (27 points/m 2 , Figure  11B ), which were both collected under leaf-on conditions, did not change performance of the raster-based ITD for either Ntrees det (58% and 57%, respectively) or Ntrees paired (42% and 40%, respectively). A significant difference between Ntrees det was observed for leaf-on ULS-R-Raster (57%, Figure  11B ; Table  4 ) versus leafoff ULS-V-Raster (73%, Figure  11C ; Table  4 ). However, a smaller difference was noted for Ntrees paired with 40% for leaf-on ULS-R-Raster and 50% for leaf-off ULS-V-Raster. Again, the difference in point density between these two datasets (353 points/m 2 and 1585 points/m 2 , respectively) does not seems to have a major impact on the raster-based ITD results (Table  4 ).
Dataset ITD algorithms were further assessed for their ability to detect trees from multiple canopy layers using the TLS trees as reference (Figure  12 ; Table  4 ). Both point cloud-based and raster-based ITD were efficient for detecting trees in the upper canopy (over 90% of Ntrees paired ≥ 18 m height, for all datasets). However, the ability of raster-based ITD to detect trees in the intermediate (12-18 m height class) and lower canopy layers (6-12 m height class) was gradually lower, with detection rates varying from 13% to 24% and from 2% to 9%, respectively. The graphical comparison of ULS-V-Pcloud and ULS-V-Raster distributions of Ntrees paired (Figure  12 ) shows that the point cloud-based ITD (Ntrees paired = 71%) outperformed the raster-based ITD (Ntrees paired = 50%). Substantial improvements also occurred in both intermediate canopy layers (Ntrees paired = 51% with ULS-V-Pcloud vs.
Tree-Level Structural Attribute Accuracy Tree height was estimated with high accuracy from all aerial datasets, with R 2 values between 0. 8 and 0. 9 and bias less than 1 m (Figure  13A ). Poor CD estimates were found for ALS-Raster, ULS-R-Raster, and ULS-V-Raster datasets, with R 2 values of 0. 22, 0. 24, and 0. 29, respectively (Figure  13 (B1-B3)). No major difference in CD estimation accuracy was found between ALS-Raster that was constructed from the 27 points/m 2 ALS point cloud (R 2 = 0. 22, Figure  13 (B1)) and ULS-R-Raster that was constructed from the 353 points/m 2 ULS-R point cloud (R 2 = 0. 24, Figure  13(B2 )); both were acquired under leaf-on conditions. A slight improvement in CD estimation accuracy was observed for leaf-off ULS-V-Raster (R 2 = 0. 29, Figure  13 (B3)), compared to the leaf-on ULS-R-Raster (R 2 = 0.
Stand-Level Inventory Attribute Accuracy The tree count estimated from all datasets and methods was significantly lower relative to the reference value, especially when using raster-based ITD. The closest estimate of tree count was obtained using method #3 on the ULS-V-Pcloud, i. e. , using the point cloudbased ITD and the cylinder-fitting approach (84% of trees detected, i. e. , 403 out of 477 trees; Figure  15F ). BA estimates were systematically underestimated using the predictive modeling (method #1), by -26. 6% for ALS-Raster (Figure  15B ), -25. 1% for ULS-R-Raster (Figure  15C ), -22. 9% for ULS-V-Raster (Figure  15D ) and -12. 9% for ULS-V-Pcloud (DBH pred ) (Figure  15E ). Conversely, BA was slightly overestimated (+3. 5%) using the cylinder-fitting (method #3) on ULS-V-Pcloud (DBH fit ) (Figure  15F ), which turned out to be the closest estimate of stand BA.
DBH-distribution of trees in 5-cm class increments (Figure  15 ) showed good estimates of large-sized trees (DBH > 35 cm), for all datasets and approaches. Yet, the ALS-Raster, ULS-R-Raster, and ULS-V-Raster DBH-distributions failed to represent the typical fieldmeasured inverse J-shaped DBH-distribution. This is mainly due to trees that were missing in the lower and intermediate size-classes (10 cm < DBH ≤ 30 cm). Instead, the estimated DBH-distribution approached a unimodal shape that was similar to a Gaussian curve. The ability to detect small-sized DBH trees improved gradually with the successive use of the following datasets: ALS-Raster, ULS-R-Raster, ULS-V-Raster, and ULS-V-PCloud (DBH pred and DBH fit ). Although slightly right-biased with respect to the field reference, the ULS-V-Pcloud DBH fit -distribution yielded the most accurate representation of the DBHdistribution shape (Figure  15F ).
Sensitivity Analysis A sensitivity analysis was applied to assess how the sigma parameter of the SEGMA Gaussian filter affect estimates from ALS-Raster, ULS-R-Raster, and ULS-V-Raster of tree count, median tree height, median tree crown diameter, and BA over the stand (Figure  16 ). Graphical representation of stand attributes revealed that on the one hand, a higher sigma value resulted in detection of fewer trees (Figure  16A ) and the delineation of larger crowns (Figure  16C ). On the other hand, tree height (Figure  16B ) and BA estimates (Figure  16D ) were much less affected by the sigma value than was tree count or crown diameter. Sigma slowly affected tree height estimation. tree height; (C) median tree crown diameter; and (D) basal area with increased sigma values (x-axis) for the ALS-Raster, ULS-R-Raster, and ULS-V-Raster datasets.
Transferability of ITD Algorithms to ULS Data Our first specific objective focused on assessing transferability of point cloud-based and raster-based ITD algorithms (SimpleTree and SEGMA, respectively) to an uneven-aged hardwood stand that was surveyed during leaf-on and leaf-off conditions. Our results demonstrated transferability of both ITD to ULS data under certain conditions. In leaf-off conditions, SimpleTree achieved the best ITD performance on ULS-V data, detecting 71% of the trees in the stand, with 71% of the trees correctly paired with TLS reference trees. Applying SEGMA to the same dataset was less efficient, with 73% of trees detected over the stand, but for only 50% of the trees that were correctly paired with TLS reference trees. This situation was caused by false positive tree detection from the CHM.
At the stand-level, method #1 resulted in underestimation of BA from about 25% for both ALS and ULS-R datasets (Figure  15B, C ). BA estimation is based on all individual tree DBHs in the stand. Its underestimation can therefore be explained by an underestimation of tree numbers by the ITD, underestimation of tree DBH by the allometric model, or both. We found that BA underestimation was slightly attenuated when using leaf-off ULS-V data (Figure  15D ). Under leaf-off conditions, LiDAR penetrates deeply into the canopy cover, thereby improving the detection of understory trees  [130]. However, the increase of LiDAR pulse penetration also results in more gaps within larger crowns, resulting in detection of multiple crowns for a single large tree  [130]. As a result, overestimation of the number of intermediate-sized trees and underestimation of large-sized trees compared to the field reference is observed and explains the differences between leaf-on and leaf-off predicted DBH-distribution.
Unfortunately, the accuracy of cylinder fitted DBH is still beyond acceptable for field inventory standards. Imminent improvement in the technical specification of portable LiDAR sensors would provide narrower beams and higher ranging accuracy. These improvements will translate to improvements in ITD and attribute estimation accuracy. Given that ULS sensors are becoming more affordable, additional studies evaluating available methods are required to test them under a wide range of forest conditions before more general conclusions can be drawn. Overall the use of a bottom-up ITD on leaf-off ULS point clouds demonstrate a strong potential for overcoming ITD limitations that are currently encountered when using raster-based methods in hardwood stands. Further developments in tree mapping and estimates of crown or stem structural attributes can take place using ULS data that are taken during leaf-off conditions, because canopy occlusion under leaf-on conditions on any abovecrown LiDAR data is strongly limiting any method's improvement.
Acknowledgments: We wish to acknowledge  NHRI  and  FPInnovations  for contributing the datasets used in this study and for sharing their expertise and vision for Forestry 4. 0. In particular, we are grateful for the technical and logistical support provided by  Pamela Hurley-Poitras (NHRI)  for the data collection. We are also grateful to  Vhan Tho Nguyen ,  Jean-François Prieur ,  Safia Abdelmounaime ,  Sebastien Bauwens  and  Nicolas Latte  for their comments and suggestions on programming development, and to  Adrien Michez  for his contribution to the R code development on the tree matching procedure. Special thanks to  Alexandre Piboule  and  Joris Ravaglia  for their support on the Computree platform and to  W. F. J. Parsons  for English language revision.
According to the ENSEMBL database, the human LINC00460 gene has seven splice variant transcripts reported to date  (32). It has been observed that LINC00460 functions as an oncogene, acting as a miRNA sponge  (17, (33) (34) (35) (36) (37) (38) (39) (40) (41) (42) (43). In these models, LINC00460 sponging activity and its association with cancer-related processes such as increase in proliferation, epithelial to mesenchymal transition (EMT), migration, invasion and metastasis  (44) (45) (46)  is well described. For example, LINC00460 promotes hepatocellular carcinoma progression by sponging miR-342-3p and hence increasing AGR2 expression level  (47). Similar LINC00460 sponging and cancer-related mechanisms across distinct tumors are described in several reports  (38) (39) (40) (41) (42) (43) 48).
MATERIALS AND METHODS The Cancer Genome Atlas (TCGA) Datasets LINC00460 expression levels were screened in 31 TCGA tumor datasets and their corresponding GTEx normal tissues using the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) platform (http://gepia2. cancer-pku. cn/#index) (57). The 31 tumors included in the analysis are summarized in Supplementary Table  1  and enlisted as follows: Acute myeloid leukemia (LAML); Adrenocortical carcinoma (ACC); Bladder urothelial carcinoma (BLCA); Brain lower grade glioma (LGG); Breast invasive carcinoma (BRCA); Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC); Cholangiocarcinoma (CHOL); Colon adenocarcinoma (COAD); Esophageal carcinoma (ESCA); Glioblastoma multiforme (GBM); Head and neck squamous cell carcinoma (HNSC); Kidney chromophobe (KICH); Kidney renal clear cell carcinoma (KIRC); Kidney renal papillary cell carcinoma (KIRP); Liver hepatocellular carcinoma (LIHC); Lung adenocarcinoma (LUAD); Lung squamous cell carcinoma (LUSC); Lymphoid neoplasm diffuse large B-cell lymphoma (DLBC); Ovarian serous cystadenocarcinoma (OV); Pancreatic adenocarcinoma (PAAD); Pheochromocytoma and Paraganglioma (PCPG); Prostate adenocarcinoma (PRAD); Rectum adenocarcinoma (READ); Sarcoma (SARC); Skin cutaneous melanoma (SKCM); Stomach adenocarcinoma (STAD); Testicular germ cell tumors (TGCT); Thymoma (THYM); Thyroid carcinoma (THCA); Uterine carcinosarcoma (UCS); Uterine corpus endometrial carcinoma (UCEC).
Mesothelioma (MESO) and Uveal melanoma (UVM) were removed from the analysis, since the correspondent normal tissues were not available in the GEPIA2 platform. Additional analysis with TCGA breast cancer, normal tissues and HNSC HPV status, was performed using data retrieved from the TANRIC platform (https://ibl. mdanderson. org/tanric/_ design/basic/main. html) (58). Violin plots, box plots and notch plots were constructed using the ggplot2 R package. Validation of LINC00460 over-expression in BRCA versus normal tissues was performed using data retrieved from the GSE29431 dataset (https://www. ncbi. nlm. nih. gov/geo/) and analyzed using the lnCAR platform (https://lncar. renlab. org/) (59). LUAD, LUSC and KIRC tumor stage plots were generated in the GEPIA2 platform. LINC00460 expression levels were considered significantly correlated with tumors when log2 fold change (Log2FC)>1 and p-value<0.
Breast Cancer Samples Differential Expression Analysis Breast cancer RNA seq counts were obtained from The Cancer Genome Atlas (TCGA) data portal (https://portal. gdc. cancer. gov). After dataset preparation, we identified the LINC00460 ID (ENSG00000233532), which is located in chromosome 13: 106,376,563-106,378,217. We then downloaded the LINC00460 expression counts and performed filtering of transcripts with 10 counts or less. In order to generate the high and low LINC00460 expression groups, we calculated two percentiles from the count expression data. The first percentile (25) contains the lowest LINC00460 expression counts, and the upper percentile  (60) , contains the highest expression levels for this transcript. We then performed differential expression analysis with the DESeq2 tool from the Gene Pattern platform, using the default parameters (http://software.
Pathway Enrichment Analysis Pathway enrichment analysis was performed using Ingenuity Pathway Analysis ® (IPA) software. Z-scores and p-values were also computed using this platform. Only differential statistically significant genes were included in this analysis (see criteria above). Overrepresentation Enrichment Analysis (ORA) was performed with the web-based Gene SeT Analysis Toolkit (WebGestalt) platform (www. webgestalt. org) (64). LINC00460 significantly correlated genes were included in this analysis.
BRCA and HNSC Datasets for Validation Survival analyses from independent BRCA Gene Expression Omnibus (GEO) cohorts were performed using the Kaplan-Meier plotter (KM plotter) site (http://kmplot. com/analysis/) (65). These GEO datasets contain gene expression data from 6,234 BRCA samples analyzed with multiple microarray platforms, including Human Genome U133 and Human Genome U133 Plus 2. 0 Arrays (Affymetrix). We used GEO-derived cohorts, the GSE16446 and GSE21653 datasets, or a combination of several datasets. The GSE16446 dataset was selected, since it contains 120 microarray experiments from primary ER-negative breast tumors of anthracycline-treated patients  (66). The GSE21653 dataset contains data of 266 invasive breast adenocarcinomas, with all BRCA subtypes included  (67, 68). We used the Affymetrix probe ID 1558930_at, that targets LINC00460, and the Affymetrix probe ID 210248_at, which targets WNT7A.
In addition, we performed survival analysis of hsa-miR-103a expression levels, using the mirPower tool  (69) , included in the KM plotter website. LINC00460:WNT7A ratio for TNBC survival analyses was also performed in the KM plotter tool. LINC00460 and WNT7A signature expression validation as anthracycline predictive markers were analyzed with the ROC Plotter platform (http:// www. rocplot. org/site/index) (70). For HNSC validation, we downloaded the GEO dataset GSE3292. This cohort has 36 freshly frozen HNSC samples, stratified by HPV status (negative or positive) and analyzed by gene expression microarrays (Affymetrix Human Genome U133 Plus 2. 0 Array). For further TNBC validation, we downloaded the GEO dataset GSE76250, which includes 165 samples. We then analyzed the expression profile of these samples, and classified them with the TNBCtype tool to obtain the Lehman subtypes (http://cbc.
mc. vanderbilt. edu/tnbc) (71). We then re-classified these samples into the TNBCtype4 re-assigning IM and MSL subtypes to the second highest correlated centroid, as described in  (72). For LINC00460 expression in TNBC cell lines validation, we screened the Cancer Cell Line Encyclopedia (CCLE) online tool (https://portals. broadinstitute. org/ccle) (73). The validation datasets analyzed for this study can be found in the Gene Expression Omnibus (GEO) web site (https://www. ncbi. nlm. nih. gov/geo/).
miRNA Interaction Prediction Analysis We used the miRcode prediction tool (http://www. mircode. org/) (74) in order to identify potential LINC00460 miRNA targets. We further corroborated these potential interactions using the lncTAR tool (http://www. cuilab. cn/lnctar) (75). Interaction prediction was considered valid when normalized delta G (ndG) values reached the -0. 1 cutoff. We then performed a third in-silico prediction analysis, using the miRPathDB tool (https://mpd. bioinf. uni-sb. de/) (60). We searched the top 15 correlated mRNAs with LINC00460 expression levels (see Table  4 ) and downloaded the miRNAs interaction list. We then compared these new miRNAs list with the previous one described above, and identified potential miRNA-mRNA-linc00460 interactions.
Statistical Analysis It has been previously shown that cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level  (76). Thus, LINC00460 expression distributions from the TCGA and validation BRCA, KIRC, LUAD and HSNC datasets were first tested for normality distribution using the Kolmogorov-Smirnov and the Shapiro-Wilk tests. After results computation, we selected comparison statistical tests accordingly. Overall Survival (OS) using all patients from CESC, GBM, HNSC, LGG, LUAD, PAAD and SARC and the Relapse Free Survival (RFS) analysis using all patients from KIRC, LUAD, READ and SARC was performed in the GEPIA2 platform. Distant Metastasis Free Survival (DMFS) was computed using the KM plotter tool described above. OS of the BRCA TCGA patients and our independent cohort was analyzed with the Kaplan-Meier model and the multivariable Cox regression model.
LINC00460 Expression Is Deregulated in Multiple Tumors LINC00460 expression was evaluated in 31 tumor types and normal tissues included in TCGA database. We found that LINC00460 is overexpressed in five different epithelial cancers, namely: Breast cancer (BRCA) (Figure  1B ); Colon adenocarcinoma (COAD); Head and Neck Squamous cell carcinoma (HNSC), Pancreatic adenocarcinoma (PAAD) and Rectum adenocarcinoma (READ) (Log2FC>1; p<0. 05) (Figure  1A ). Interestingly, we have also detected two central nervous system cancers with LINC00460 low expression, comparing with its normal tissue counterparts: Glioblastoma Multiforme (GBM) and Low-Grade Glioma (LGG) (Log2FC<1; p<0. 01) (Supplementary Figure  S1 ). These observations potentially suggest a different role for LINC00460 in central nervous system tumors, which can further be analyzed in future studies.
Among the seven LINC00460-deregulated tumors from the TCGA cohort, we identified two novel (not previously reported) tumors, namely LGG and GBM (Supplementary Figure  S1 ). We have also confirmed previous observations regarding LINC00460 overexpression in BRCA  (33) , comparing with normal tissue, using two independent cohorts, namely TCGA (Tanric data, T test; p=1. 6845E-14) and the GEO cohort GSE29431 (Figure  1  and Supplementary Figure  S2 ). These observations suggest a ubiquitous role for LINC00460 in cancer biology. distinct cancers: LUSC, LUAD and KIRC (ANOVA; p<0. 05; Supplementary Figures  S3A-C ). In the KIRC model, LINC00460 over-expression is significantly associated with high histological grade (Mann Whitney U-test; p=0. 0001; Supplementary Figure  S3D ). In addition, LINC00460 overexpression is correlated with further aggressive tumor phenotypes, such as HPV negative HNSC (TCGA: Mann-Whitney U, p< 0.
05. GSE3292: one-way ANOVA, p= 0. 02) and basal-like BRCA (TCGA: Kruskal-Wallis test, p=3. 14E-13. Mexican cohort: Kruskal-Wallis test, p=0. 012), using two independent cohorts for each cancer type (Supplementary Figure  S4  and Figures  2A  and B ). This last observation was further validated in a panel of BRCA cell lines, where we detected that LINC00460 is mainly expressed in basal TNBC cells (Figure  2C ). CCLE screening confirmed TNBC LINC00460 enrichment (Supplementary Figure  S6 ). As LINC00460 expression is significantly related to Basal-like BRCA, we reasoned that LINC00460 over-expression might also be associated with Immunohistochemistry (IHC)-detected Estrogen Receptor (ER) and Progesterone Receptor (PR) status in BRCA patients. As expected, LINC00460 expression is enriched in IHC-detected negative Estrogen receptor (ER) (Mann-Whitney U; p=0.
We have also assessed RFS in the GSE21653 dataset (n=240; HR = 0. 59; 95% CI [0. 38 -0. 94]; logrank p = 0. 024) and DMFS (n=120; HR=0. 78; 95% CI [0. 6-1. 02]; logrank p= 0. 062) in BRCA patients in the GEO-derived cohorts (Figure  4C  and Supplementary Figure  S5 ). We found a corresponding significant association with improved RFS for LINC00460-high patients. Although nonsignificant, we observed a solid tendency to a higher DMFS in the BRCA LINC00460-overexpressed group (Supplementary Figure  S5 ). These data strongly suggest that LINC00460 might play a dual prognostic role across different tumors, as high LINC00460 expression predicts an increased OS, RFS and DMFS in the BRCA model, but it is also a marker for poor prognosis in at least   eight distinct solid tumors (see Figure  3C ). These intriguing findings aimed us to further investigate the role of LINC00460 in BRCA.
LINC00460 Is Significantly Enriched in Basal-Like 2 TNBC and Its Overexpression Predicts a Favorable Clinical Course We then directed our efforts to elucidate the role of LINC00460 in OS prediction of the aggressive Basal-like BRCA model, a subtype which comprises the majority of TNBC cases  (77). Interestingly, the improved OS effect shown in all BRCA patients (Figures  4A, B ), is reproduced when we analyzed two independent cohorts of Basallike BRCA tumors only, namely the TCGA cohort (TCGA via Tanric tool, n=139; logrank p=0. 042) and GEO derived cohort GSE16446 (using basal-like samples only; n= 76; HR=0. 23[0. 08 -0. 63]; logrank p = 0. 0019) (Figures  5A, B ). RFS/DMFS (GSE16446; n= 76; HR = 0. 27; [0. 11 -0. 68]; logrank p = 0. 0027) were also significantly improved in LINC00460 over-expressed BRCA basal-like samples (Figure  5C ).