dataesr/scientific-paragraphs-categorization huggingface dataset:
Another example is on the AFF dataset when using L = 3, the generated ms-SMBP has a dimension of 40 and takes 5. 58 s to be calculated, with an identification score that still outperforming the state-of-the-art methods, including the MRELBP (74. 45%, against 73. 43%) but with a significantly compact and faster to compute i. e, 20× compact, and almost 2× faster. That is, the proposed ms-SMBP descriptor and its combinations achieved their highest identification score when large-scale macrostructures are encoded, this implicitly affects the computation time and the dimensionality of the final descriptor. However, in our experiments, we found that we can reach similar to high performances even when small macrostructures, with a small number of scale-space levels, are used to derive the final ms-SMBP, this allows us to considerably reduce the computational time and the dimensionality of the texture descriptor without affecting its performances.
Results of experiment n • 2 In this experiment, we report the classification results of a convolutional neural network (CNN) model on different bark datasets. It is important to note that we aim to examine the performance of one CNN model instead of comparing different CNN models. Authors in  [47]  were the first to apply CNN for the bark image classification on the BarkNet dataset by fine-tuning a pre-trained ResNet34 model. In this experiment, we apply the CNN in two separate ways: first by training the ResNet34 model from scratch on each of the bark datasets, namely Trunk12, AFF, Bark101, and BarkNet. Second, we use a pre-trained version of ResNet34 and fine tune it on each of the bark datasets. For the Trunk12 dataset and the AFF dataset, we randomly select half of the bark samples from each species to form the training set and the remaining half for the testing set.
For the Bark101 dataset, we use the provided data splitting with 50% from each species for training  [15]. Finally, for the BarkNet dataset, we follow  [47]  by keeping 80% for training and 20% for testing. For the implementation details, we use the code of  [47]. The initial learning rate is 10 -3. It is reduced at fixed epochs for each dataset by a factor of five. For the optimization method, Adam was used with a weight decay of 10 -3. Empirically, a batch size of 32 was found to achieve better results. We use a random horizontal flip for data augmentation and random cropping of size 224 × 224 as it is the required input size for the ResNet34 model. When the ResNet34 is fine-tuned, we freeze the first layer and retrain the remaining layers since the bark datasets are vastly different from the ImageNet samples  [47].
In addition to this evaluation of the ResNet34 model, we report the classification results of our descriptor ms-SMBP using the k-NN (k = 1), the SVM lin , and the SVM rbf classifiers following the same experimental protocol, i. e. , bark datasets splitting and performance metrics. The ms-SMBP descriptors were extracted from each channel of the bark images, using different colors spaces, and concatenated to form the final descriptor. Table  12  lists the results of the classification with ResNet34 and Table  13  summaries those of the ms-SMBP on bark datasets. The following observations can be made: When the ResNet is trained from scratch, the accuracy increases as a function of the size of the training set, i. e. , the larger the training set, the higher the accuracy. For instance, the accuracy on the Trunk12 dataset is 53.
84% with 198 training samples, and on the BarkNet dataset, the accuracy is 70. 61% with more than 19k training samples. In fact, when learning-based approaches are trained on large sets, they perform better at the generalization on the testing set. For the Bark101 dataset, ResNet34 achieved a low accuracy when trained from scratch, i. e. , 19. 30%, even though the training set is larger than the one of the Trunk12 and AFF datasets. One reason to explain this is the fact that bark classes on the Bark101 dataset are heavily imbalanced. The number of samples per class in the training set varies between 1 and 69 images. This influences the capacity of the CNN model to learn relevant features from these classes, and thus mis-classify them which leads to an important loss of accuracy. When a pre-trained ResNet34 is fine-tuned on the bark datasets, interesting results can be achieved when compared to the ones achieved when it is trained from scratch.
Large improvements of all the performance measures are reported on the Trunk12, AFF, Bark101, and on the BarkNet datasets. For instance, for the accuracy values, the improvements are +36. 41%, +22. 92%, +38. 92% and +12. 00% respectively. This is due to the transfer of pre-learned parameters from early layers of the ResNet34. These parameters were learned on the large ImageNet  [54]  dataset. Regarding the performance of the ms-SMBP descriptor on bark dataset when compared to those of the ResNet34, we can see that the proposed descriptor achieves better results than both of the configurations of the ResNet34, i. e. when trained from scratch and when fine-tuned on the AFF and The Trunk12 dataset. For instance, from Table  13 , the SVM rbf classifier return an accuracy of 96. 92% for the ms-SMBP with RGB bark samples and the ResNet34 fine-tuned returns 91.
17% (see Table  12 ) on the same dataset. The same observation can be made for the AFF dataset with 93. 37% with the SVM rbf classifier on YCbCr samples and 91. 17% for the fine-tuned ResNet34. On the Bark101 dataset, the deep learning-based model scores higher than the ms-SMBP descriptor. We believe that this mainly due to the fact that the ms-SMBP deals poorly with collections of a large number of species showing a high inter-class similarity, like the Bark101 dataset with 101 bark species. However, on the BarkNet dataset, we find that the handcrafted ms-SMBP achieved interesting performance on this very large dataset with an accuracy that is slightly lower than the one achieved by the fine-tuned ResNet34, i. e. , 76. 64% for the ms-SMBP with the SVM rbf classifier on RGB samples compared to 82.
Furthermore, although the comparison methods were shown to achieve state-of-the-art performance in generic texture datasets, they show lower performance for bark datasets, which demonstrates the effectiveness of ms-SMBP for a challenging task. The ms-SMBP descriptor is discriminative since it combines different but complementary texture statistical features. Furthermore, it was found that a combination of three statistical descriptors achieves comparable to high performance on challenging bark datasets. The discriminative power of ms-SMBP is derived from the approximation and representation of botanical aspects of the bark texture's appearance. ms-SMBP produces compact and informative feature descriptors. The low dimensionality of a single ms-SMBP m descriptor relies on the weighting scheme which is a function of the number of the scale-space levels, i.
e. , the macrostructure size L, independently of the number of sampling points. That is, the texture information from large spatial support can be encoded by a lowdimensional descriptor. Moreover, the dimension of the final ms-SMBP descriptor can be further reduced by applying dimension reduction methods as in  [55]. The computational time of feature extraction is moderate and can be further optimized. The bark surface representation with a deep-learning-based approach, such as the ResNet34 CNN model, was found to be suitable, when trained from scratch, on very large-scale datasets with more examples which allows learning distinctive features. Moreover, an interesting improvement of the accuracy of the ResNet34 model was recorded when a pre-trained version is fine-tuned on bark samples, taking advantage of the parameters learned from the large ImageNet dataset.
Conclusion In this paper, we have introduced a set of statistical descriptors for the application of bark species identification. The principle of ms-SMBP is to encode the characterizing macro pattern for different bark species. First, the intensity distribution within a large macrostructure is represented and summarized by simple statistical characteristics. Then, the macro pattern is encoded by a novel in-depth gradient scheme. The in-depth scheme characterizes the relationship between the macrostructure levels and their local adaptive intrinsic statistical prototype at the distribution level. The performance of the proposed approach was evaluated on four bark datasets with varied sizes and acquired under controlled and uncontrolled conditions. It was found that the ms-SMBP outperforms other statistical and multi-scale LBP variants.
We showed that combining complementary statistical descriptors captures various aspects of the bark surface appearance. Furthermore, encoding the bark surface from distinct color spaces was showed to improve the classification accuracy, since the color of bark samples provides complementary information. Setting the optimal size of the characterizing macro pattern across different bark species requires simple computations, with no training, and generated compact and informative representation. Besides the proposed handcrafted texture descriptor, we propose to evaluate the bark species classification with the ResNet34 convolution neural model. The evaluation was performed by training ResNet34 once from scratch and once fine-tuned on bark datasets. The experiments showed that when deep learningbased approaches are trained from scratch, they achieve low performance on small-scale datasets and with imbalanced classes.
However, interesting improvements were achieved when the pretrained ResNet34 is fine-tuned on bark samples. Comparing the performances of two bark content representations: the proposed handcrafted descriptor and the deep learning-based model shows that for small datasets a handcrafted approach is well suited and could achieve interesting generalization results on large datasets too. Finally, the proposed descriptors with the in-depth gradient encoding scheme have interesting properties, like encoding large macrostructures with one macro-pattern that is invariant to monotonic gray-scale, and global rotation changes. Examining its invariance to scale changes could be held in future work since the present ms-SMBP does not provide adaptive scale encoding for the texture images. In addition, we propose to explore the performances of different deep convolutional neural network models on large bark datasets.
scriptors using the statistical prototypes µ scale m and µ pattern m and the configuration {L = 11, R 1 = 1, D s = 1, AllP} on the Trunk12 dataset. It can be observed that the identification rate of all single ms-SMBP m (µ pattern m ) descriptors increases gradually and consistently over scale-levels. The ms-SMBP m (µ pattern m ) outperforms all corresponding counterparts based on the µ scale m prototype when large macrostructures are encoded. Moreover, single ms-SMBP m (µ pattern m.
* and ms-SMBP * MinMaxMeanVar reached a score of 75. 06% using the configuration {L = 4, R 1 = 4, D s = 3, AllP}. ms-SMBP * * reached a score of 74. 45% using the configuration {L = 3, R 1 = 5, D s = 2, AllP}. This representation obtains a higher score than the one of MRELBP (73. 43%) on the AFF dataset.
, 2014 (Treutlein et al. , 2014)  using the following selection criteria: Pearson correlation >=0. 5, p-value (GBA, BH corrected) <0. 05. This resulted in 30 mouse genes characteristic of AT1 cells, 28 genes for AT2 cells, 43 genes for Club cells, and 84 genes for Ciliated cells. The biomaRt R package was used to map mouse genes to human orthologs, resulting in 28 human AT1 markers, 26 AT2 markers, 39 Club markers, and 65 Ciliated markers. For the assessment of lineage markers in human and mouse samples, expression data Z scores were first calculated for each gene across the 546 human lung adenocarcinoma tumors, and across the 10 mouse KP tumorsphere samples. Cell lineage signature scores were then calculated for human adenocarcinoma tumors as the average z-score expression of the human markers for each cell lineage and for mouse samples as the weighted average z-scored expression of the mouse markers with weights set to -log10(p-value).
1  (Liberzon et al. , 2015). The Integrative Genomics Viewer (IGV) was used for visualization of ATAC-seq peaks near genes of interest  (Robinson et al. , 2011). All source code and sequencing data are available at https://github. com/anneleendaemen/G9a. CellIdentity. Lung Zhang, Y. , Liu, T. , Meyer, C. A. , Eeckhoute, J. , Johnson, D. S. , Bernstein, B. E. , Nusbaum, C. , Myers, R. M. , Brown, M. ,  Li, W. , et al. (2008). Model-based analysis of ChIP-Seq (MACS). Genome Biol 9, R137. Zheng, Y. , de la Cruz, C. C. , Sayles, L. C. , Alleyne-Chin, C. , Vaka, D. , Knaak, T. D. , Bigos, M. ,  Xu, Y. , Hoang, C. D. , Shrager, J. B. , et al. (2013). A rare population of CD24(+)ITGB4(+)Notch(hi) cells drives tumor propagation in NSCLC and requires Notch3 for self-renewal. Cancer Cell 24, 59-74. Zylicz, J.
Single projection images of 4-6 z-stack sections from at least 15-20 tumorspheres were constructed and analyzed in the Matlab software package (version R2016b by Mathworks, Natick, MA). Individual cell nuclei were segmented using regional intensity maxima and watershed thresholding on the DAPI channel, and then scored by the presence of relevant IF signal above a global intensity threshold in the area immediately surrounding each nucleus. For BrdU images signal was calculated in each nucleus around intensity maxima. Quality control images were also created by superimposing the cell scoring mask on the raw image data. In vivo treatment with G9a inhibitor UNC0642 6 mice per group were treated with either vehicle (60% PEG400/40% H20) or with UNC0642 10 mg/kg, IP, daily (60% PEG400/40% H20) for a duration of 6 days.
Acknowledgments We appreciate the support of all the  Junttila lab  members. We would like to thank  Elaine Storm  and the  De Sauvage lab  for providing critical reagents. We are grateful to the sequencing lab for their superb work. We also received technical support from the inhouse genotyping and murine reproductive technology core groups, especially  Tiffany Yuan. We would like to acknowledge the laboratory animal research core for facilitating the support needed to enable in vivo experimentation, as well as, the center for advanced light microscopy for providing help with immunofluorescence quantitation and all FACS lab members, especially  Jonathan Paw  and  George Tweet  who were instrumental with devising critical sorting strategies.
Background. The initial management of patients with sarcoma is critical. We used the French National Cancer Institute-funded nationwide sarcoma database NETSARC to report the management and oncologic outcomes in children, adolescents and young adults (AYAs) patients with sarcoma at the national level. Patients and methods. Data from patients with sarcoma are collected, regularly monitored and updated using NETSARC. NETSARC was searched for pediatric (<15 years) and AYA (15-30 years) patients with sarcoma, diagnosed from 2010 to 2017. We report the management, locoregional recurrence-free survival (LRFS), progression-free survival (PFS), and overall survival (OS) in children and AYA treated in French reference sarcoma centers (RSC) and outside RSC (non-RSC), and conducted multivariate analyses adjusted for classical prognostic factors to explore survival.
Background Sarcomas are a group of rare connective tissue cancers, highly heterogeneous with more than 70 histotypes identi ed regardless of age, with a wide array of clinical presentations  (1). Estimation for sarcoma incidence in Europe for all age group is 5. 6 per 100,000 inhabitants per year, including 84% of soft tissue sarcomas (STS) and 16% of bone sarcomas  (2, 3). However, in adolescents and young adults (AYA) patients usually de ned in France as patients aged from 15 to 24 years, more AYA patients present with bone sarcomas (standardized incidence rate of 14. 6 per million), and conversely less AYA patients had STS  (12. 6 per million)  (4). Clinical practice guidelines for sarcoma patients recommend dedicated management involving multidisciplinary teams  (5) (6) (7). The French National Cancer Institute (INCa) funded in 2009 the national clinical sarcoma reference network NETSARC gathering 26 reference sarcoma centers in order to generate data collection and improve the outcome of sarcoma patients.
9%)  (15, 16). Overall survival (OS) and progression-free survival (PFS) data are not yet available. This retrospective study searched the nationwide database NETSARC for data from French patients aged 0-30 years, for whom sarcoma diagnosis was identi ed between 2010 and 2017, and reported the quality of initial surgery, de ned as the rate of macroscopic complete resection with absence of tumor cells in resection margins (R0), microscopically positive margins (R1) or macroscopic residual disease (R2), the implementation of pre-treatment biopsy (before initial surgery), and the implementation of pretreatment imaging (before any oncologic treatment), and provided at 3 years results of locoregional recurrence free survival (3y-LRFS), PFS (3y-PFS), and survival (3y-OS) in children and AYA patients with sarcoma managed in RSC and AYA treated in non-RSC.
Patients And Methods The NETSARC database The French National Cancer Institute (INCa) funded in 2009 the clinical network NETSARC including 26 French RSC to improve the outcome of sarcoma patients. A dedicated network for expert pathology diagnosis in sarcoma (RRePS) gathering 19 pathology reference centers in charge of a second pathological review for each suspected case was also implemented. Any le from patient with suspicious of sarcoma should be reviewed by MDTB. The current NETSARC + database (netsarc. org) gathers all sarcoma cases presented to MDTB, collected data on diagnosis, therapeutic management, and clinical outcomes in terms of relapse and survival, from Jan 1st, 2010  (17). Neoadjuvant treatments including systemic treatment or radiotherapy were added to data collection from 2016.
Patient les may be presented at any time, before any diagnostic procedure, before initial biopsy, before primary surgery, after primary surgery, at relapse, and/or for eligibility to clinical trial. The French Unicancer Sarcoma Group (GSF GETO) validated this research project on pediatric and AYA patients on Jan 18, 2020. Patients received information sheets (https://expertisesarcome. org/espace-patients/adultes/) and non-opposition procedure applied. The NETSARC + database was searched for pediatric and AYA patients, with sarcoma diagnosed from 2010 to 2017, and this study analysis used a limited set of pseudonymized data, describing patient and tumor characteristics, surgery, relapse and survival (8). All methods were carried in accordance with relevant guidelines. Treatment centers were categorized according to the a liation of the surgeon performing the rst surgery.
A surgeon referenced in the NETSARC network led to consider the AYA patient as treated in French RSC (https://netsarc. sarcomabcb. org). Conversely, a surgeon not referenced in the NETSARC network led to consider the patient as being treated in French non-RSC. To note, in France, all pediatric patients (< 15) should be treated in RSC. Data from patient diagnosed from 2010 to 2017 were selected to ensure a 3-year follow-up was achieved. In early 2020, local coordinators were contacted to update patient les. The data cut-off for data analysis was June 26, 2020.
Statistical analysis Categorical data were expressed with frequencies and percentages, and continuous data with median and range. Comparisons were performed with chi-square for categorical variables and Mann-Whitney test for continuous data. The diagnostic date was the date of pathological diagnosis (biopsy or rst surgery). Survival was calculated from the date of diagnosis to the date of last follow-up or death. OS was de ned as the time from the date of diagnosis to the date of the last follow-up or death due to any cause. PFS was de ned as the time from the date of diagnosis to the date of last follow-up or the date of rst local progression, metastatic progression or death, whichever occurred rst. LRFS was computed from the diagnosis date to the date of last follow-up or the date of rst locoregional progression.
Patient population The NETSARC database included 3972 patients aged between 0 and 30 years with sarcoma diagnosed between 2010 and 2017 (Fig. 1 ). Patients with desmoid tumors (n = 369) were excluded because they rarely require to be operated  (20) , making it impossible to determine in which center they were treated. AYA patients with no surgery (n = 384) or with no information on surgery (n = 278) were also excluded. All children < 15 years are managed in RSC in France, and were therefore included in the analysis (Fig. 1 ). A total of 2,941 patients were included in the analysis consisting of: 1. Children treated in French RSC (n = 714). 2. AYAs treated in French RSC (n = 1,290).
Discussion The present work used the French nationwide prospective database NETSARC to assess the survival of young patients with sarcoma diagnosed between 2010 and 2017. To the best of our knowledge, the present study is the rst to report the survival of young patients (children and AYA) treated for sarcoma at a national level. The present study reported 3y-OS rates of almost 80% in AYA patients with sarcoma which is consistent with the 2y-OS of 80% reported by  Raze et al. in 2016 (4). No signi cant difference in OS was reported between AYAs treated in RSC and in non-RSC. To note, the present AYA population considered age ranges from 15 to 30 years i. e. extended range compared to the usual 15-24 years as per French National Cancer Institute INCa de nition. Indeed, the underlying concepts considered the current epidemiology based on better biological and histopathological characterisation of AYA neoplasms  (21, 22) , sarcoma incidence based on pediatric histology stays signi cant up to the age of 30 years (4, 23), and the fact that sarcoma patients aged from 25 to 30 years are still often managed in non-expert centers at the present time and MDTB involving both pediatric and adult oncologists in this group age would also be useful (16, 24-27).
These situations remain marginal and limited, and changes in treatment centers for personal reasons cannot be excluded. The quality of surgery is known as a major prognostic factor for relapse-free survival and overall survival in bone and soft tissue sarcomas  (30) (31) (32) (33) (34) (35). However, in France as in the majority of European countries so far, the initial management of a sarcoma patient may be carried out in non-oncologic-specialized clinic, regardless of sarcoma expertise or number of patients treated. Conversely, in the Scandinavian countries and the United Kingdom, a patient with sarcoma must be managed, upon suspicion, in an expert center  (29, 36). The French database NETSARC had demonstrated that resection in one of the 26 national expert centers compared to non-expert centers reduces the risk of relapse by almost 35% in 35,784 sarcoma patients  (9).
Limits of our study The NETSARC database does not collect all medical treatments chemotherapy and targeted therapy administered before and after surgery, and do not allow to explore the impact of medical treatments administered in expert and non-expert centers so far. However, from 2016, neoadjuvant treatment administrated mostly in pediatric population (76. 3%) and in AYA in RSC (56. 9%) and rarely in AYA patients in non-RSC (14%) was added to data collection which may contribute to better local control and to decrease 3y-LRP in children and in AYA in RSC. Detailed information regarding neoadjuvant treatment (systemic anticancer drugs, radiotherapy or both) are not speci ed and their respective potential impacts on 3y-PFS in children and in AYA in RSC are not accessible. Comparison of sarcoma management is a critical issue in children and AYAs regarding not only age difference but also sarcoma histotypes representations.