dataesr/scientific-paragraphs-categorization huggingface dataset:
The NB-ARC domain is the most conserved region of NBS-LRR genes  [3, 4]. Different classes are characterized by their N-terminal domain  [5]. Genes without the following N-terminal domains are classified as NLs: N-NB-ARC; L-LRRs. CNLs contain an N-terminal coiled-coil domain, TNLs contain an N-terminal Toll-interleukin receptor-like (TIR) domain, and RNLs contain an N-terminal RPW8 domain. To date, NBS-LRRs have been difficult to predict using automatic gene annotation tools, due to their duplicated and clustered nature leading to fragmented or absent annotations  [2, [5] [6] [7]. This is compounded by the fact that NBS-LRRs are sometimes annotated as being repetitive sequences and so are present in repeat databases  [8]. Additionally, some disease resistance genes are lowly expressed except during infection so RNA-Seq data would not provide supporting evidence for gene annotation.
NLR Candidate Identification Pipeline The pipeline uses a double pass process for NBS-LRR gene identification in the genome, based on methods used in previous manual genome-wide screens using BLAST to search the genome for the NB-ARC domain  [5, 13]. The first pass focuses on the identification of initial NBS-LRR candidates using the NB-ARC domain, as it is the most conserved region present in all NBS-LRR genes. During the first pass, tBLASTn  [14]  is used to search the genome, using the NB-ARC sequences based on the Pfam profile (PF00931)  [15]  and custom consensus sequences based on the four classes of Vitis vinifera NBS-LRR genes (CNL, TNL, NL, and RNL) retrieved from NCBI. Overlapping hits are merged and then adjacent hits on the same strand are combined (within 1000 bp) to handle small regions of divergence and introns.
Discussion Identification of NBS-LRR genes is an important step in the understanding of plant disease resistance. The most important step of the identification of NBS-LRR genes has in the past relied on gene predictions, which may not be accurate or exhaustive. NLGenomeSweeper allows for an accurate automated identification of NBS-LRR candidates without the requirement for previous gene predictions or additional functional information and shows broader performance than a previous tool, NLR-Annotator, which has poor performance for RNL genes. Moreover, the output format is designed to support downstream manual annotation by providing information on surrounding ORFs (Open Reading Frame) and potential functional domains. This candidate identification and supporting information becomes increasingly important as the ease of generating high-quality genome assemblies from long-read data increases and the necessity of identifying disease resistance genes genome-wide becomes more common.
Consequently, users of a multiple sensor instrument are provided with an increased amount of information. Therefore, for the development of e-nose applications it is essential to deeply examine problems related feature selection, by removing redundant sensors that are possibly adding noise into the system, instead of improving discrimination. Evolutionary computation techniques can be applied to optimize sensor selection, feature selection, and classification stages  [23]. In this way, one can find an optimal subset of sensors for a particular application while choosing sensing devices from a larger database of sensors. These techniques help to create smaller application-specific sensor arrays and help to reduce the potential cost associated with new sensor developments to solve complex olfactory problems.
The following data collection procedure was applied. At the beginning of measurements, a small amount (1 mL) of wine sample was put in a concentration chamber, and the volatile compounds were collected for 30 s. The first stage of measurement, lasting 10 s, was used to collect the baseline sensor response when the e-nose was exposed to pure air. Then, prepared gas with the sample odor was pumped into the sensor chamber for 80 s to measure the response during the adsorption phase. After that, pure air was again pumped to the sensor chamber, and the response during the gas desorption phase was also collected for 90 s. The sampling rate was set to 18. 5 Hz, and transient sensor resistance values were collected. After that, the set-up was exposed to pure air for 600 s to purge volatile residues and relax the sensors.
Classification Modeling Odor recognition using electronic noses, from the data processing point of view, is a classification task using machine learning models. In this section, we describe various elements of our approach including extraction of features used for modeling (Section 3. 1), validation of classification model performance (Section 3. 2), employed modeling technique (Section 3. 3), and feature selection by wrapper and filter methods (Section 3. 4). For the calculations presented in this paper we developed programs in Python 3. 7 language using the scikit-learn module  [38].
Model Validation An important part of the machine learning modeling process is validating the model's performance, which should be done on a dataset independent from the dataset used for model training. As one might notice in the description of examined odors in Section 2. 2, there can be correlation between the studied wine samples because they come from the same bottle. Rodriguez Gamboa et al. [19]  proposed to use a cross-validation procedure with the "leave-one-group-out" scheme. Measurements performed on samples coming from one bottle were kept apart for testing the model performance, and the rest of the data were used for model training. This procedure was performed in a loop over all bottles, and the performance results were averaged. In our modeling, we opted for another validation scheme and implemented the "group shuffle split" method.
In this case, the available dataset is split into training and validation sets in a 75/25% proportion, with a restriction that all samples coming from a particular bottle should be in either training or validation sets. This was based on random selection; thus, in order to obtain more reliable metrics of the model performance, the procedure was repeated 100 times and the results were averaged. We verified that the results were not affected by increasing the number of repetitions. We also verified various proportions of the training/validation split, and the chosen value gave optimal results in terms of the spread of individual model performance. The reason to chose different approaches is as follows: The model's performance calculated by the leave-one-group-out method was about 2-3 percent points higher than the one obtained by the group shuffle split approach.
As a metric for model comparison we used accuracy statistics (number of properly classified records/all number of records) calculated on the validation dataset. It should be mentioned that the variant of the forward feature selection method, specifically designed for the support vector machine model, has been used for electronic nose data by Gualdrón et al. [52]. We should keep in mind that both the backward selection and forward selection methods do not guarantee that the optimal subset of modeling features, leading to the best possible model performance, is found. The fulfillment of such a hard task would require comparisons of all possible subsets of the whole set of features (exhaustive search), which, even for moderate values of N, is computationally prohibitive and requires training and comparison of 2 N performance models.
The size of the usually available training datasets is in the range of hundreds or thousands. In such a case, the time to train the model required for wrapper methods, which usually provides a better selection of features in terms of model performance, is acceptable on modern computer hardware. In our work, the Mutual Information, Fisher Score, and RelfiefF methods  [49]  were used for comparisons with recursive forward selection method described above. We would like to emphasize works of other authors in which the application of filter feature selection methods relevant to electronic nose data have been reported. Mutual Information was applied, for example, by Wang and co-workers  [54, 55]. Nowotny et al. [56] , Yin et al. [57] , and Sun et al. [30]  used multivariate Wilks' Λ statistics and Mahalonobis distance to optimize the sensor array.
Results Model performance was studied using the standard leave-one-group-out cross-validation method. We obtained an average model accuracy equal to 96. 6%, but for such a validation scheme, the standard deviation of the model was equal to approximately 11% (percent points). When the chosen validation scheme was group shuffle, with the proportion of training and validation datasets of 75/25% and 100 repetitions, the estimated model performance was smaller, equal to 94. 3%, with a standard deviation of 7. 5%. We performed both these calculations on all extracted features without selecting the best ones. In our opinion, the group shuffle method more reliably estimated the model's performance, and the remaining results were obtained in this way. In Figure  4  we present results when only part of the data is used for training the classification model, namely the data collected by a smaller number of sensors.
The features extracted from both gave some of the best results. Yet, the difference between these sensors was distinguishable. When data from only the 2nd sensor were used, the models exhibited a significantly better stability than models trained on the data obtained from other sensors. On the contrary, sensors 3 and 6 were also of the same type, but as one can notice, the models trained on features extracted only from the 3rd sensor had good performances, but for the 6th sensor this was not the case. In Figure  7  we present results with the aim to address two other issues concerning data transformation and selection. As was already mentioned, we extracted modeling features from sensor response characteristics. The original measurements provide information in terms of sensor resistance R, but in our work we extracted features not only from the resistance but also using sensor conductance G, which is just the inverse of R.
Conclusions In this paper, different issues concerning features selection associated with electronic nose applications have been considered. Particular attention has been devoted to data reduction by eliminating excessive numbers of sensors employed in the electronic instrumentation of a given e-nose. From the data processing point of view, odor recognition by using an e-nose is a classification task assisted by machine learning models. A number of approaches have been used for examining extraction of features, validation of model performance, modeling technique, and feature selection by wrapper and filter methods. The usefulness of the approaches has been assessed using a multisensor system database created by Rodriguez Gamboa et al. [19]  and gathering measurements of wine quality. Interesting results concerning model performance have been obtained.
4%. This result is better than the best one reported by Rodriguez Gamboa  [19]  (97. 7%). One has to keep in mind that our estimates were obtained with a different cross-validation scheme, which, as we demonstrated, gave less optimistic estimates of classification accuracy. We believe that such a result may be encouraging to re-examine some publicly available datasets. Looking at the data extracted from just one sensor, such as the 2nd, 3rd, or 5th sensors, we can see from Figure  6  that the model accuracy was about 95. 5%, which is very close to the performance of models trained on features extracted from all sensors. We can also observe that for the models trained on data extracted from only the 2nd sensor, the standard deviation of accuracy, interpreted as a kind of model stability, was comparable to that obtained for the model trained on all data coming from all sensors.
Implementation in Cast3M The proposed method can be easily implemented in the code Cast3M without supplementary developments. Particularly, the material behavior generator MFront  [41]  is used here for implementing the softening mechanical response due to the cracking. For summarizing, the overall phase field algorithmic implementation is illustrated in Algorithm 1. In addition, this work makes use of the convergence acceleration tools available in Cast3M, i. e. the fix point acceleration techniques  [39] , when solving the mechanical problem.
Acknowledgement This research was conducted in the framework of the ' PLEIADES ' project, which is supported financially by the  CEA (Commissariat à l'Energie Atomique et aux Energies Alternatives) ,  EDF  ( Electricité de France ) and  Framatome. In addition, the authors would like to acknowledge the financial support of the  Cross-Disciplinary Program on Numerical Simulation of CEA (PTC FUEL ).
Scientific Corpora We constructed two corpora of scientific articles from bibliometric entries found in the Web of Science (WoS) database. WoS contains scientific articles considered to be at the top of their field, but has the important limitation of including mainly journal articles written in English. We selected articles that could be categorised either as promoting a LSP or a LSH approach. To do so, we developed sets of keywords and used them to identify LSS articles in the WoS database. Our first scientific corpus (i. e. , 'small corpus'), gathers articles published in WoS through 2016 mentioning the exact phrases 'Land sparing' (168 results) or 'Land sharing' (128 results) in the WoS 'topic' field. We combined these two databases, cleaned the data, 2 and obtained the 'small corpus' with 200 articles.
Their sustainability reports were found in the Sustainability Disclosure Database 6  maintained by the Global Reporting Initiative (GRI) 7  , which is the data source for reporting on Sustainable Development Goal 12. 1 (corporate reporting). Where available, we collected these reports from 2005 to 2014, to coincide with the date of the pivotal article in the LSS controversy. However, most reports in our corpus date from 2007 as sustainability reporting was not common beforehand. between LSP and LSH notions is how to find the 'best farming method' that can balance the trade-offs between agriculture and biodiversity. The  Green et al. (2005a)  article serves as the critical junction for our analysis because it was the first to explicitly use the term 'land sparing'. The authors asked: "How should we best resolve the need for increased food production with the desire to minimize its impact on what remains of wild nature?" To answer their question, they developed a simple model based on the relationship between biodiversity and yield.
The term 'land sharing', with its current meaning, was coined in 2011 by Green's group, who referred to a "lowest-yield land sharing strategy" 12 These articles do not appear in Figure  1 : the Vandermeer and Perfecto article is included in the WoS database but not selected by our keywords, while the Matson and Vitousek article is not included in the WoS database. Land Sparing Land Sharing  (Phalan et al. , 2011b). Their main argument was no longer centred on higher yields for a growing population, but shifted in focus towards biodiversity conservation and the possibility that land sparing could reduce deforestation and habitat degradation if it were truly accompanied by a policy of protected areas. The politics of consumption also began to enter the debate as the authors called upon consumers to reduce their meat consumption and proposed alternative hypotheses about the causes of food system inefficiencies (such as food waste and petroleum-based cultures)  (Phalan et al.
Compositionalism vs. functionalism To clarify these world-views in LSS, we drew upon a classic text in conservation ethics to develop a set of terms that we searched for within our databases: the Current Normative Concepts in Conservation by  Callicott et al. (1999). This seminal text introduced two notions to operationalize two poles of biodiversity conservation philosophy, what they called 'compositionalism' and 'functionalism'. For compositionalism, culture separates humans from nature and due to cultural practices, humans alter the wilderness and the pristineness of nature. The discipline of ecology that is affiliated with this philosophy is based on a biological hierarchy of organisms at the heart of species' populations that interact within biotic communities. The concept of conservation that is privileged is the preservation of biodiversity (or conservation biology), biological integrity and ecological restoration away from the pervasive effects of human actions.
In functionalism, humans are a part of nature and this ethic is captured with the scientific discipline of ecosystem ecology, which studies the functioning of ecosystems. The conservation approaches linked to this philosophy focus on understanding ecosystem health, ecological services and adapting human economies to ecological exigencies. Table  3  shows the words used by Callicott et al. to explain both philosophies and our adaptation of them into the language of CorTexT. Callicott and colleagues classify these schools of thought as complementary and non-competitive, meaning that empirically some practices can bridge or combine the two ethics. Source: Conservation concepts in  Callicott et al. (1999)  Note: we excluded the functionalist term 'sustainable development' because of its broad use in the literature and its co-optation by the world of agricultural development and agribusiness after the 1999 publication.
We selected only the 6 other most frequently occurring functionalism terms in the large scientific corpus so to have the same number as the compositionalism terms. To identify affinities between compositionalism/functionalism and LSP/LSH approaches, we compared the co-occurrence of terms representing LSP and LSH (Table  1 ) with those of conservation ethics (Table  3 ). Our contingency matrix (Figure  2 ) illustrates the correlations (measured through chi2) between the 12 conservation ethics terms and the 12 most frequently used terms for LSH/LSP in the large scientific database. This analysis tends to indicate cumulatively stronger co-occurrences between LSP terms and compositionalism terms (top left quadrant, Figure  2 ), and between LSH terms and functionalism terms (bottom right quadrant, Figure  2 ).
Note: These queries were made in the large scientific corpus. The contingency matrix shows the degree of correlation between any pair of terms from the conservation ethics list and those representing LSH or LSP. We combined the term lists to create a single matrix and we limited the number to 12 so to cover all of the ethics terms. The x-axis lists the conservation ethic terms, and the y-axis the land sparing/sharing terms. The size of the squares refers to the frequencies of the words found in the database. Red cells are the most correlated (many documents mentioning the term on the y axis also mention the term on the x axis within a 5-sentence range). Blue ones are anti-correlated (rarely does a document mentioning the term on the y axis also mention the term on the x axis within a 5-sentence range).
A large cluster of Chinese scholars (top right) working together on 'ecological services' is distant from the rest of the LSH clusters, which suggests that there are no co-publications. Their engagement in the literature occurs via the LSP work and via the Lambin (Stanford and Louvain) cluster that straddles the LSS controversy by contextualizing where each approach might work better  (Lambin and Meyfroidt, 2011). The difference between the LSH and LSP groups that focus on 'preservation of biological diversity', which is the most pervasive ethic in the database, shows that these authors may discuss the same topic, but from different sides of the debate. For instance, while the LSP group seeks to achieve this preservation by creating protected areas, the studies contained in the LSH group demonstrate the importance of farmers as key conservationists through on-farm preservation of biodiversity  (Bawa Kamaljit et al.
The extreme variability of the folding pattern of the human cortex makes the recognition of cortical sulci, both automatic and manual, particularly challenging. Reliable identification of the human cortical sulci in its entirety, is extremely difficult and is practiced by only a few experts. Moreover, these sulci correspond to more than a hundred different structures, which makes manual labeling long and fastidious and therefore limits access to large labeled databases to train machine learning. Here, we seek to improve the current model proposed in the Morphologist toolbox, a widely used sulcus recognition toolbox included in the BrainVISA package. Two novel approaches are proposed: patch-based multi-atlas segmentation (MAS) techniques and convolutional neural network (CNN)-based approaches.
Database The training base is composed of 62 healthy brains selected from different heterogeneous databases and labeled with a model containing 63 sulci for the right hemisphere and 64 for the left hemisphere. The "unknown" label is used to designate unidentified structures (usually small sulci). The two ventricles are labeled but not considered as sulci. Most of the subjects are right-handed men, aged 25 to 35 years old. Unfortunately, there is no gold standard definition of sulci morphology. Even the boundaries of the well-known central sulcus can be difficult to define ( Fig. 2  ). Moreover, Fig. 2  shows that the definition of sulci morphology impacts the level of granularity of the nomenclature. Therefore, for this study, the elementary folds of each brain were manually labeled according to a sulcus nomenclature following a long iterative process to achieve a consensus across a panel of several experts on cortex morphology.
The last iteration of the database labeling was performed using the TileViz visualization tool  ( Mancip et al. , 2018 ). This tool allows the entire database to be visualized and labeled simultaneously on a wall of screens  (See Fig. 19  in supplementary material). Until now it was only possible to label and simultaneously evaluate a limited number of hemispheres, generally four, on a standard screen. Thus, this tool helps to limit the bias of labeling induced by a restricted view of the database. To support this new iteration, the elementary folds were manually cut when necessary, which was not possible during the study of  Perrot et al. (2011). Note that compared to traditional labeling approaches where only one expert can label images, this database has been progressively labeled by several experts, both successively and simultaneously.
This consensus-based labeling has sometimes led to the introduction of new sulci labels when it was considered necessary, making it essential to use the video wall. However, the different experts have thus not produced independent labelings, which prevents us from assessing human-level performance on this dataset. Compared to  ( Perrot et al. , 2011 ) , the same MRI acquisitions were used but a new iteration of labeling was performed, resulting in the introduction of four new sulci in the nomenclature used. The new nomenclature is described in the Fig. 3. A more detailed description is provided in the Fig. 23  of the supplementary materials subsection. The manually labeled database is now available on the BrainVISA website ( http://brainvisa. info/data/sulci _ database/ base _ 62/2019 ).