dataesr/scientific-paragraphs-categorization huggingface dataset:
More studies in this type of wooded environments would be needed to confirm this assumption. In the absence of such studies we consider these values to be outliers. The SDs for the mean RPP values were calculated using the delta method  (Stuart and Ord, 1994) , a mathematical solution to the problem of calculating the mean of individual SDs (see  Li et al. , 2020 , for more details). The pollen taxonomy and nomenclature follow the system used in the European Pollen Database (EPD;  Fyfe et al. 2009). Broström et al. (2004) ; RPPs for trees from  Sugita et al. (1999)  (reference taxon Juniperus) converted to Poaceae as reference taxon by  Broström et al. (2004). b  Bunting et al. (2005) , reference taxon Quercus and no RPP for Poaceae; RPPs relative to Poaceae calculated by  Mazier et al. (2012)  assuming that the RPP of Quercus relative to Poaceae is the same as the mean RPP of Quercus from three other studies in NW Europe.
c New RPPs from the Czech Republic  (Abraham and Kozáková, 2012). d New RPPs from Poland. Poaceae as reference taxa (see text for more details). e New RPPs from Germany  (Matthias et al. , 2012) , reference taxon Pinus. RPPs converted to Poaceae as reference taxon. We selected the RPP estimates obtained with the dataset of vegetation cover including only the trees that had reached their flowering age (allFIDage) (for more information, see  Matthias et al. , 2012). f New RPPs from Germany  (Theuerkauf et al. , 2012) ; in the original publication, the ERV analysis was performed with the Lagrangian stochastic model (LSM) for dispersal of pollen and with Pinus as reference taxon. For this synthesis, Martin Theuerkauf redid the analysis with the Gaussian plume model for dispersal of pollen  (Parsons and Prentice, 1981; Prentice and Parsons, 1983)  and with Poaceae as reference taxon.
We simulated data under different configurations of sample sizes, harvest rates, and sources of spatial heterogeneity in harvest rate to assess potential model bias in parameter estimates. We then applied the model to data collected from a field study of wild turkeys (Meleagris gallapavo) to estimate local and statewide abundance in Maine, USA. We found that the band recovery model that incorporated a spatial predictive process consistently provided estimates of adult and juvenile abundance with low bias across a variety of spatial configurations of harvest rate and sampling intensities. When applied to data collected on wild turkeys, a model that did not incorporate spatial heterogeneity underestimated the harvest rate in some subregions. Consistent with simulation results, this led to overestimation of both local and statewide abundance.
To introduce spatial variation into harvest rate and survival parameters, we generated Gaussian random fields using the "gstat" package  (Pebesma, 2004)  in program R (R Core Team, 2020), which created a location-specific beta coefficient that described spatial variation across the study area. We assessed the accuracy of estimates under variable sampling within a region and across the study area. To ensure that the simulation accurately presented a range of possible spatial heterogeneities and that the model was robust to those ranges of variation, we simulated multiple spatial configurations of harvest rate using low, medium, or high values for the partial sill, range, and nugget of the variogram used to generate the random field. In practice, this allowed us to vary the magnitude of variation in harvest rate, the maximum distance of autocorrelation, and the amount of small-scale variation in harvest rates, respectively (Figure  S2 ).
| Case study: Wild turkeys in Maine To demonstrate the applicability of the model, we used data collected from wild turkeys in Maine, USA. Maine is a large state (~91,647 km 2 ) with a variety of intermixed land use types and variable hunter densities. As such, we expected significant heterogeneity in harvest rates of turkeys within the state, as has been observed for other states' turkey populations  (Stevens et al. , 2020). Turkeys were captured during the winters (December through March) of 2018 through 2020 using rocket and drop nets and aged as either adult (>1 year old) or juvenile (<1 year old) according to plumage  (Dickson, 1992). We marked turkeys with at least 1 of 4 different marking methods with associated identification numbers, including aluminum butt end leg bands, aluminum rivet bands, plastic colored leg bands, or patagial wing tags.
| Model fitting We fit simulated data to the model, as described in section 2. 1, using a Bayesian approach  (Hobbs & Hooten, 2015). We used JAGS v. 4. 3. 0  (Plummer, 2003)  via the "R2jags" package  (Su & Yajima, 2015)  in the R v. 4. 0. 3 programming environment (R Core Team, 2020). Regression coefficients were given vague uniform priors. Simulation models were allowed 10,000 iterations, discarding the first 5000 from calculations. The model fit to wild turkey data was allowed 50,000 iterations, discarding the first 20,000.
| DISCUSS ION We found that a Lincoln estimator incorporating an SPP in the band recovery model consistently estimated abundance with low bias across a variety of spatial configurations of harvest rate for simulated data. When the SPP was omitted from the model, relative bias in abundance increased for all configurations of spatial heterogeneity in harvest rate. Additionally, we found no difference in relative bias according to either sampling intensity or the underlying nature of spatial heterogeneity in harvest rate. When applied to real data collected from turkeys in Maine, we observed a wide range of harvest rates and abundance among wildlife management districts. As expected, the basic model that did not incorporate spatial heterogeneity in harvest rates via the SPP underestimated harvest rates in some WMDs, which resulted in an overestimation of abundance in those districts, and statewide.
F I G U R E 5 Harvest rate estimates were variable among wildlife management districts across Maine, USA, for both male and juvenile wild turkeys. Regional differences in harvest rates are depicted for adult male turkeys with color indicating the mean harvest rate for each management district. experiencing spatial heterogeneity in one parameter could result in increased variation in estimates of the other. Multiple methods are currently used to monitor turkey populations. Many states approximate population trends using spring harvest data (e. g. ,  CDEEP, 2016; Harms et al. , 2017; Healy, 2000) , but this does not provide an estimate of true abundance, which may be preferable for setting regulations  (Lint et al. , 1995). This method also requires accounting for changes in the abundance of birds and harvest rates, both of which influence the number of birds harvested through time  (Paloheimo & Fraser, 1981).
The BIS 2 and iBIS 2 algorithms The web server works in two different modes by calling two distinguished but related algorithms, 'BIS 2 ' and 'iBIS 2 '. The first mode runs BIS 2 , a combinatorial method specifically designed to find coevolution signals in small or conserved MSAs  (16, 17). The web server BIS2Analyzer provided the first online access to the BIS 2 algorithm, described in detail in  (18). The second mode runs iBIS 2 , an iterative procedure that explores the topology of the phylogenetic tree associated to the input MSA and runs BIS 2 independently on selected subtrees; it is designed to analyze larger MSAs, possibly comprising thousands of sequences. The goal of iBIS 2 is to find coevolution signals within subfamilies, identified by subtrees, which are diluted in the full tree.
The Structural Visualization Page Accessible through the 'View on Structures' button of the Results Page (Figure  1 ), the Structural Visualization Page is organized in several panels dedicated to the visualization of coevolving positions on protein structures (Figure  2 ). The page is organized with three panels which are borrowed from the Results page (Zoom plot, Clusters plot and Clusters Tab) and one or more other panels displaying protein structures uploaded by the user. For each structure, the user can provide either a PDB file, a CIF file or a PDB id, possibly containing multiple chains. Multiple structures can be loaded simultaneously side-by-side (Figure  2B ). In the window opened for a PDB structure, chain-specific on/off switches allow each chain to be viewed or not. The user can enable/disable each cluster for visualization from the Clusters Tab.
We describe LineageD-a hybrid web-based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision-making process, however, is time-consuming, tedious, and error-prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model.
Introduction How individuals of species develop is an essential question in specific subfields of biology. By looking at the historical development of a plant embryo, botanists investigate whether the plant develops normally or not. As an embryo develops, single cells divide into two new cells and these parent-child relationships can be described using temporal hierarchies. The process of creating these hierarchies is called cell lineage assignment and requires biologists to (1) take images of a multi-cell plant embryo using a confocal microscope, (2) segment the resulting 3D volumetric data to identify the embryo cells, and (3) indicate pairs to reconstruct the hierarchy backward toward the root as the first cell (i. e. , the ovum) of the embryo. In our collaboration with botanists, we identified several parts of this workflow that can be improved with a dedicated visual analytics tool.
However, most of these 3D trees were designed to add one dimension to store more information [SKW * 07, TON03, LSTD17]. Our collaborating biologists are most familiar with 2D node-linked diagrams for representing the hierarchy, while the actual cell shapes have to be interpreted in 3D views to assist them with making lineage decisions. We thus rely on a combination of both a 3D view and a 2D tree. One of the most closely related approaches is HyperLabels [KIK * 21], which uses a model's hierarchy to navigate the 3D structures. While Kouřil et al. also combine the abstract hierarchy data with 3D shapes that represent the actual physical entities, their abstract data representations do not represent additional information such as, in our case, the plant embryos' temporal development. The size of datasets is constantly increasing, so researchers have also explored methods to flexibly adjust the space a tree representation takes to increase its efficiency [LTX * 20, BRZW17, Ahu86].
Biology-related Visualization Biologists often study complex patterns or processes. While various stakeholders have different levels of interest in the respective datasets  [LPCB04] , we focus on interactive tools for expertsspecifically botanists interested in plant embryo development. Within the biology domain, several visualization approaches and tools are currently being used. For example, Briggs et al. [BPT * 11] combined dendrograms and heatmaps for the visualization of taxonomic diversity. Such representations, however, are usually not space-efficient  [MR10]. Moreover, we need to show both abstract cell lineage data as well as the actual 3D shape of the entities. Numerous platforms (e. g. , OsiriX  [RSR04] , Fiji ImageJ [SACF * 12], and Avizo) are designed to display such multi-scale 3D data, and these are currently being used by our collaborating experts to establish the cell lineage (e.
Background and Design Considerations As we just discussed, trees can be used to visualize cell hierarchies in embryos. Our collaborator also confirmed that biologists use and appreciate 3D views to assist them with understanding the spatial structure and to make decisions on the lineage. Here they rely on actual 3D meshes that they derive from the confocal microscopy slice data by segmenting and then processing them into the shapes of the embryo cells at the time of data capture. Yet, no suitable tools exist to determine cell lineage for several reasons: A cell lineage tree only represents approximate division stages that apply to most cells, while some cells may remain unchanged from a given level to the next and the lineage hierarchy needs to account for such cases. Consequently, there exists no single generic  (binary) tree that would represent the development of all embryos.
We aim to create complete and accurate cell division hierarchies so that plant biologists can study the division history. Yet every cell divides in a different time and it is impossible to measure the precise division time from a single confocal microscopy dataset. So we rely on the fact that an embryo's cells roughly divide within similar time ranges, and use these division times to build the hierarchy. The tree should also clearly emphasize cells that divide outside of the regular time range. The tree thus not only needs to contain the parent and children relationships but also represents the time-dependent division process. As such, any node in the tree represents a given cell and thus its connected 3D representation at a certain stage, so navigation in the hierarchy needs to lead to adjustments of the 3D visuals.
Front-end Interface Our web-based front end needs to allow the biologists to explore the 3D information in its hierarchy-dependent configuration, traverse the cells at the different hierarchy levels and understand their spatial and derived properties, make decisions about the cell lineage for all cells, and then record these decisions to specify further hierarchy levels. We thus offer the following elements to assist the experts. Our LineageD system (Figure  3 ) centers around two main views: the main 3D view of the embryo and the hierarchy view that shows the cell lineage tree the biologists are building. The hierarchy on the right of the interface initially consists just of small blocks, where each represents a single cell at the time when the confocal microscopy dataset was captured; i.
Server-side Back-end Our tool's server-side takes care of data processing and analysis. Our datasets comprise the cells' names (i. e. , numeric labels) and mesh data for their surfaces. The experts create these datasets with dedicated tools (e. g. , Avizo, Fiji [SACF * 12]). In the back-end we use this input to build the necessary data structures to later be able to construct the hierarchy. We also derive the needed information such as the 3D cell locations (centers), neighbor counts and respective neighbor look-up tables, normalized cell volumes, normalized cell surfaces, and normalized shared areas between neighboring cells. After the completion of the lineage, the back-end also allows us to export the results in the tree data format needed by the biologists. An essential part of the back-end is the creation of the ML model (D4).
For this purpose we also maintain a collection of 97 past (manual) cell lineage assignments provided to us by the biologists. For these we also computed the data-derived parameters mentioned above and used this data to train a neural network model to allow us to predict the lineage for future datasets. The classifier computes a probability for all possible direct neighbors to be sister cells, and we then pick the one that has the highest chance. This reduces the number of possible pairs, and we continue picking the pair with the highest probability until this probability falls below 50%. This prediction functionality can then be accessed from the front-end.
Interaction Design Our interaction design centers around the processes of proposing new matches of likely sister cells (manually and with the help of our neural network model) and the verification of whether these matches are correct. Both processes rely on abstract as well as 3D information, so our expert users have to constantly switch back and forth between the abstract hierarchy view and the spatial 3D views. Next, we describe how, in LineageD, we support the biologists both in the top-down and the bottom-up lineage specification. For a new dataset we show all cells as blocks on the lowest hierarchy level on the right, and in the 3D view we show the embryo with the cells colored based on their 3D location. Usually the first step of the biologists is to remove those cells from the consideration that are part of the supporter (i.
Implementation Details We implemented LineageD in JavaScript and Express. js (v. 4. 17. 1). For the front-end, we rely on D3  [BOH11]  to realize the hierarchy tree rendering and render the embryo in the 3D views with vtk. js (v. 19. 0. 4)  [SML96]. We implemented the back-end in Node. js and used MongoDB to create our database. We used TensorFlow's (v. 2. 8. 0) Keras library to train the multi-layer sequential neural network model offline with Python. We created two hidden layers with ReLU activation and adopted softmax for the final output layer to get the probability score, and the model's single-pair accuracy reaches approximately 94%. The raw data is produced by Avizo and contains the cells' information including names, vertices, and triangles. We then parsed the raw shape data and set each neighboring pair as one record.
For each pair, we extracted features for the training data based on the recommendations by the biologists: the normalized distance between the cell pair, their normalized shared surface area, their neighbor counts, their normalized volumes, normalized surface area, and the directions of neighbors with target cells. With Tensor-Flow. js (v. 3. 8. 0), we used the trained model to predict assignments in the website back-end. For an embryo with 256 cells, such as one of our default datasets that we show in Figure  3 , there are more than one million vertices with more than five million triangles. Depending on the network speed when connecting to the Web tool, typical datasets can thus be loaded in less than a minute and can be rendered at interactive speeds (approx. 20 fps for the 256-cell example on a macOS with AMD Radeon Pro and Intel UHD Graphics 630).
In this section we present the overall experimental setup, our observations, and the user feedback from the biologists interacting with the system. The specific embryo we used in the study was a real Arabidopsis thaliana dataset provided by our collaborators, which comprises 64 cells (not counting the cells of the supporter). Datasets that are needed to be analyzed in practice typically range from several tens of cells to several hundreds of cells, so this example dataset is realistic (and still manageable in our study) as well as already challenging to analyze for biologists, and they could compare this experience to their past work. The whole investigation was approved by our institution's (Inria) ethical review board (AVIS n°2021-46) and we pre-registered the study setup and design (osf.
Participants We recruited 5 expert biologists (2 females, 3 males, ages 31-54 years, mean 43. 4 years) via social networking and mailing lists, denoted as P1-P5. We offered prospective biologists free access to our tool as compensation. Before the study, we asked them to fill in a consent form, a research media records release form, and a background and demographic data collection form. All of the participants had a PhD degree and have been doing post-PhD research on plant biology or related fields for 2. 5-25 years (mean: 14. 1 years, sd: 9. 4 years, median: 12 years). Two of them were experts in plant biology, while the other ones focus on computational biology, biomathematics, or bio-images processing. We conducted our study via videoconferencing due to the COVID19 limitations.
Procedure Our case study experiment consisted of two parts: first we conducted an observational study of the experts using our tool with the example data, followed by a survey and a semi-structured interview. Observational study. We conducted the experiment with each participant individually. We began by presenting a brief introduction about the features of LineageD. Next, we asked the participant to open the website on Chrome using the credentials we provided and to share their screen. We then briefly explained to them the components of LineageD and introduced the main interaction mechanisms, as explained in Section 4. Then the participant could freely explore and establish the lineage for the 64-cell dataset. We used the thinkaloud protocol and with the participant's permission, we recorded the screen and audio during the process for our later analysis.
Study results A whole participant session took 60 to 100 minutes. All biologists reported that the 64-cell embryo was challenging for them to assign, and none of them finished the complete hierarchy history reconstruction within the study session. This was also not to be expected since they had to learn and get used to the new tool and its interaction concept first as well as answer our questions in the experiment. All of the participants, however, said that they plan to work with the tool more after the study and try it in practice. Learning the Functionality. We found that three of our participating experts traditionally used 2D slice-based interaction (i. e. , in TreeJ) to navigate the embryo dataset and to mark sister cells, as opposed to using 3D interaction with a surface-based dataset that we offer.
This means that participants first had to get used to the fundamental 3D interaction techniques (rotation, translation, scaling) in projected 3D views. They also traditionally used text-based node-link representations of the hierarchy (see Appendix A), so they had to get used to the icicle plot representation we used. Webbased implementations as we used for LineageD were also new to one participant: P1 thought that the tool is quite different from the traditional software he is using. He stated that they generally use desktop software, but he believes that with sufficient time to play with it, most people could learn the new tool quickly. A final aspect was that P3 said that it was difficult for her to learn the actions to expect from the system based on specific input, such as what part of the tree to which the prediction applied.
P2 was excited about the possible insights and wanted to try other datasets with the tool, such as animal embryos. P3 thought that some visualizations, like the target and sister view, make it easy for her to observe the development of the embryo's organization over time. P4 said he better perceived certain features of the embryo with the information we presented in the tool, such as the cells' volumes and shared surfaces. P5, finally, said that 3D views are essential for getting a good understanding of the embryo. Machine Learning Experience. Next, we inquired whether our participants think that the automatic assignment based on machine learning helps them with the lineage process. P1 and P4 thought they cannot fully evaluate it because the experience time was not enough for assigning big embryos.