dataesr/scientific-paragraphs-categorization huggingface dataset:
For the genes identified in this way, clustering algorithms and gene ontology annotation are used to identify those entries that are directly related to the subject of interest. These genes are then used as hooks to build interaction networks in order to pull out additional functionally relevant genes. All of the databases and analysis tools are open access. The core set of databases and web tools used in the internship are: The Allen Brain Atlas (gene expression data based on donor brains and correlation analysis; http://brain-map. org/), Venny (Venn diagram generator; http://bioinfogp. cnb. csic. es/tools/venny/), DAVID (Database for Annotation, Visualization and Integrated Discovery; functional annotation, pathway information, and clustering; https:// david. ncifcrf. gov/; Huang et al. , 2009) , PythonAnywhere (statistics, graphing; https://www.
Allen Brain Atlas/Gene Expression The Allen Brain Atlas combines genomic data with neuroanatomy through the generation of gene expression maps obtained from Affymetrix data  (Hawrylycz et al. , 2012). The Allen Brain human database contains gene expression data for 6 donor brains. This human database is queried using the differential search function, which enables a search to identify gene expression enrichment in one brain region as compared to another. For example, learning and memory are typically associated with the hippocampus, so in this case the differential search function is used to find genes that have enhanced expression in the hippocampus relative to other regions of the brain. Details on the usage of the differential search function can be found at the Allen Brain site (http://help.
brain-map. org/display/humanbrain/Microarray+Data#MicroarrayData-GeneSearch). Students are taught how to interpret Affymetix heatmaps, evaluate gene expression data (fold difference values, error, and threshold cutoff), and use spreadsheet editing, sorting, and graphing functions for the organization and analysis of large datasets. The cleaned gene sets are then compared by the students to detect common and distinct elements using an online program (Venny) that evaluates lists and generates a Venn diagram as a visual representation. The genes that are common among all donors are then analyzed in DAVID for functional annotation, clustering, and pathway information. Genes that are associated with project relevant themes, such as behavior, nervous system development, and/or specific diseases, are used to build interaction networks, which consist of protein-protein interactions that are supported by multiple lines of evidence, such as experimental, text mining, and co-expression in the STRING database.
The interaction networks are used to identify potential gene candidates that may be involved in the same behavioral process or disease, and are also used to identify network substructures, such as hubs and motifs, which indicate important and possibly functionally related entities. Functional classification is assessed using DAVID to identify interactions that are relevant to the project topic. For an extended analysis, students can use the most pertinent genes extracted from the networks to identify additional candidates that have similar spatial expression profiles in the brain tissue of interest. The correlation analysis is done in the Allen Brain database using the correlation search function (http://help. brain-map. org/ display/humanbrain/Microarray+Data#MicroarrayData-Correla-tiveSearch).
5. The authors need to include more information about the project itself (i. e. a discussion of the modules used for teaching, the mentoring, etc. ). The reader is left wondering if the students are doing actually self-initiated projects, or if they are being led by the modules. It was helpful to read about the databases that the students use, but the reader does not gain any knowledge about the actual e-internship program through which the students progress (or are those the modules? It is unclear. ). In other words, how were they mentored? Is the mentoring effective for student growth? How are the modules designed? Is there any research to show that the module design is effective? Do they present and defend their research in a scientific forum (even online)? It would also help to put the Project Design section first (before the database discussion).
Reviewer's comment 3) The authors need to include more information about the project itself (i. e. a discussion of the modules used for teaching, the mentoring, etc. ). The reader is left wondering if the students are doing actually self-initiated projects, or if they are being led by the modules. It was helpful to read about the databases that the students use, but the reader does not gain any knowledge about the actual e-internship program through which the students progress (or are those the modules? It is unclear. ) How are the modules designed? Is there any research to show that the module design is effective?.
Author's response The modules are based on a protocol that was designed in our laboratory for profiling gene expression data to identify genes of interest associated with a behavior or brain disorder. The protocol is broken down into steps so as not to overwhelm the students with too much information at once. Modules which consist of detailed instruction are built around each step of the protocol. The purpose of the module is to provide students with a detailed reference in addition to the mentoring sessions so that they can work in the databases and with the data independently. The effectiveness of the modules has not been formally evaluated. We would like to also provide the students with screen casting videos for improved instruction. We hope to make the videos available to students this season.
Reviewer's comment 4) In other words, how were they mentored? Is the mentoring effective for student growth? Author's response Student mentoring primarily occurs through video conferencing and includes a walk-through of each database using screen sharing, project/topic discussion, and troubleshooting. Mentoring sessions occur weekly and last anywhere from 30 min -90 min. Students also email us with questions. The effectiveness of the mentoring sessions on student growth has not been assessed formally but we believe that mentoring is necessary for the students to perform their research and complete their projects.
We present a method for the automatic detection of vanishing points in urban scenes based on finding point alignments in a dual space, where converging lines in the image are mapped to aligned points. To compute this mapping the recently introduced PClines transformation is used. A robust point alignment detector is run to detect clusters of aligned points in the dual space. Finally, a post-processing step discriminates relevant from spurious vanishing point detections with two options: using a simple hypothesis of three orthogonal vanishing points (Manhattan-world) or the hypothesis that one vertical and multiple horizontal vanishing points exist. Qualitative and quantitative experimental results are shown. On two public standard datasets, the method achieves state-of-the-art performances.
5, 1. 5] range for the twisted space. We repeated this experiment for the 96,423 line segments extracted from the York Urban and Eurasian Cities VP datasets (Section 5. 2) and observed that their distribution is different but the range of interest is the same as before. Figure  6  shows the data for the points in the datasets. Based on these observations, the limits of the dual domains where the point alignment detector will be executed are set to [-1, 2] × [-1, 2] for the straight transform and [-2, 1] × [-1. 5, 1. 5] for the twisted transform. 3 Points that fall outside each domain are discarded. Once the alignment detector is run in each dual space, each alignment found determines a candidate vanishing point v i in the image, with an associated meaningfulness NFA i (Section 2. 1). In the next step, the location of the VPs will be refined, and redundant VP detections (detections produced in both dual spaces) will be removed.
Main Algorithm Algorithm 1 presents the pseudo code for the main VP detection procedure. Each subroutine is detailed and explained in the rest of this section. Parameters are detailed in each subroutine and in Table  1. The following notation will be used: • bold lowercase letters for vectors (e. g. v) • bold uppercase letters for lists (e. g. L) • uppercase letters for matrices (e. g. Q). • x v and y v for the horizontal and vertical coordinates of a point v in the image, respectively. Algorithm 1: Main body of the algorithm input : An image I. Parameters τ , θ, δ, ζ, f , p, γ S , γ R , ω, λ, κ, (see Table  1 ). output: A list V of detected vanishing points and l hor , the estimated horizon line. The input to the algorithm is the image I. The output is a list V of relevant vanishing points locations, and the estimated horizon line l hor.
Line Segment Denoising The aim of this step is to remove short, noisy line segments, and to capture the direction of higher level structures formed by groupings of line segment endpoints. Algorithm 2 presents the pseudo code for this procedure. To achieve its goal, this procedure uses the alignment detector of  [13, 14]  to find alignments of line segment endpoints. The line segments are grouped by length and orientation. First, they are divided into "short" and "long" segments using a threshold τ. Then, they are subdivided by orientation in angular slots of 30 • with 10 • overlap. The alignment detection algorithm is run on the endpoints of the line segments contained in each slot (lines 5 to 7 and lines 9 to 11). The list of final line segments is composed by the long segments and the segments found as endpoint alignments (lines 3, 8 and 12).
Algorithm 5 presents a short pseudo code for this procedure. Note that the domain of the input points must be defined and passed to the algorithm. When working in the image, as in Algorithm 2, the domain is the same as the image domain ([0, W ] × [0, H]). When working in the straight and twisted dual spaces, the domains are [-1, 2] × [-1, 2] and [-2, 1] × [-1. 5, 1. 5] respectively, as explained in Section 3. 4. For clarity, this has not been explicited in the calls to detect alignments in Algorithms 1 and 2. Algorithm 5: detect alignments: Point alignments detection. input : A list of points X, a rectangular domain D, the Number of False Alarms threshold ε = 10. output: A list A of alignment detections. 1 A ← detect alignments(X, D, ε) // Detailed in  [14]  Lines corresponding to alignments detected in the dual space are converted to points in the image space by using the inverse PCLines transform described in Algorithm 4.
Note that [2Q, -p] is a 3x4 matrix and v λ is a 4x1 vector, because v is in homogeneous coordinates. In MATLAB, the function null() is used to obtain the result. It could happen that the matrix Q is ill conditioned, for example when the line segments in the group are mostly parallel. The method detects ill conditioned cases by measuring the normalized distance between the original VP and the refined VP. If it is above a certain threshold ζ, the original VP is kept (Algorithm 7, lines 21 to 24). The second step in the refinement is the removal of the redundant detections, described in Algorithm 8. These redundancies can have two causes. One type is caused by detections corresponding to the same VP found both in the straight and twisted spaces. The alignment detection algorithm can also produce some redundant detections when a noisy cluster of aligned points triggers multiple thin detections.
When evaluating the algorithm on standard benchmark datasets (Section 5. 2), we found that a few of them needed to be manually adjusted according to the type of images to be processed (see Table  2 ). Many of them did not need to be adjusted for each dataset. A procedure for automatically determining parameter values according to the type of input image remains to be developed. The most sensitive parameter of the method is the focal length f. This value can sometimes be obtained if the camera model is known, or from the information included in the EXIF 5  header for some type of image formats. The EXIF header can provide the focal length in mm, the CCD size in mm and the CCD resolution in pixels, which can be used to compute the focal length in pixel units. If the camera is available but its characteristics unknown, f can be estimated by camera if ||v (h) -p|| < λ then 15 Append v (h) to V f inite hor 16 end 17 end 18 if V f inite hor = ∅ then 19 V f inite hor ← arg min v (h) ∈V hor ||v (h) -p|| 20 end 21 if V ortho hor V f inite hor = ∅ then 22 V ← V ortho hor V f inite hor 23 else 24 V ← arg min v∈V hor NFA(v) 25 end 26 Append v (v) to V 27 l hor ← horizon line voting(V, κ) // Algorithm 16 calibration  [11].
Otherwise, a general rule of thumb is to choose f ∈ [0. 3W, 3W ], supposing W is the largest size of the image. Also needed to compute the VP in the Gaussian sphere, the principal point of the camera, p, can be safely assumed to be at the image center [W/2, H/2], if the image was not cropped from a bigger image. The threshold τ for the length of line segments can have a big impact in the final result, if there happens to be a significant group of structures in the image whose length is close to the threshold. This threshold should vary with the image size. The chosen value of √ W +H 1. 71 is not strictly scale invariant, but, as most of the parameters in Table  1 , it has been found empirically using the benchmarking datasets of Section 5. 2. Two threshold parameters remain. The threshold λ is a simple distance threshold used to determine if a point lies at "infinity".
Acceleration of the Point Alignments Detection In terms of computational complexity, the bottleneck of the proposed method is the alignment detection stage. The alignment detector of  [13]  performs an exhaustive search over all pair of points and for each pair it tests different template configurations to compute possible alignment candidates. The counting of points inside each candidate rectangle makes the exhaustive version of the algorithm to have O(Cn 3 ) complexity, where n corresponds to the number of points and C to all the different possible shapes for the alignment templates. One way to accelerate the method is to use a fast algorithm to propose alignment candidates, instead of evaluating every pair of points in the dataset. To this end, we propose to use the unsupervised Gaussian Mixtures algorithm of Figueiredo et al.
[7] , exploiting the fact that an elongated cluster can be approximated by a thin Gaussian. This algorithm detects Gaussians of any shape and can automatically discover the number of Gaussian clusters in the dataset. The clusters found by this algorithm can be considered as potential alignment candidates and then be evaluated by the a contrario criterion of  [13]. The computational complexity of Figueiredo's algorithm is similar to the EM algorithm in 2D, which is approximately O(nk), where k is the number of iterations. Since the algorithm converges in at most a few hundreds of iterations, this is usually much faster than O(n 3 ). To cope with the non-deterministic nature of the algorithm (given by its random initialization), the algorithm can be run multiple times to obtain different lists of candidates.
In the proposed method it is run three times, and a single list of candidates is produced by merging the output of all runs. Algorithm 17 presents the pseudo code for the accelerated version. For each Gaussian detected by Figueiredo's algorithm, we consider a line segment along the longest axis of the Gaussian, centered at the mean and with length twice the square-root of the largest eigenvalue of the co-variance matrix of the Gaussian (lines 6 and 7). The set of endpoints of all such segments are then passed to the alignment detector algorithm, which will compute candidate alignments using only those endpoints (line 11). The GMM procedure in line 3 uses the original code of  [7] , downloaded from the author's web page  6. The detect alignments with candidate endpoints procedure in line 11 of Algorithm 17 implements the alignment detector of  [13, 14] , except that instead of going through each pair of points to form candidate alignments, it only considers pairs of candidate endpoints obtained with the Gaussian mixtures algorithm.
Example Results For the experiments presented in this section, we used the C code of  [13]  for alignment detection and the C code of  [9]  for line segment detection. The rest of the algorithm was implemented in MATLAB. Processing a 640x480 image takes an average of 22 seconds in a 2. 4 Ghz Intel Core i5 laptop with 8 GB of RAM. The bottleneck of the method is the computation of point alignments in straight and twisted dual domains, which accounts for more than 90% of the processing time. This can be accelerated as explained in Section 4. Figures 9, 10 and 11 show example results of the method. The resulting image shows, on the topright corner, the grouping of line segments in the image corresponding to each VP and the estimated horizon line. On the bottom rows, the PClines straight and twisted spaces are shown, with the point alignments that have been detected, and the representation of the final VPs.
Quantitative Evaluation A commonly used measure to assess the performance of vanishing points detection is the horizon line estimation error. This measure is defined as the maximum distance in the image domain between the estimated horizon line and the ground truth, divided by the image height. In this section, we use this measure to establish the performance of the method in two standard and widely used datasets. The first dataset is the York Urban Dataset (YUD)  [5]. It includes 102 images of outdoor and indoor scenes, the camera parameters, and the ground truth VPs. All the images satisfy the Manhattan world assumption, so the ground truth consists of 3 orthogonal VPs. Example results for this dataset are shown in Figure  12. Figure  11 : Example result of the method when the Manhattan-world hypothesis is not applicable.
Best viewed in electronic format. Top left: Original image. Top right: Algorithm result, line segments corresponding to each final VP detection and horizon line. Bottom: PClines straight (left) and twisted (right) dual space. Alignments (shadowed rectangles) and final VPs (colored dashed lines). In this example multiple horizontal VPs have been found. On the dual space, one can see that the lines corresponding to the multiple horizontal VPs intersect close to a point which represents the horizon line in the image. There is one misdetection in the straight space and two misdetections in the twisted space that do not correspond to a true VP, but have been discarded by the algorithm in the refinement step. For this example f was set to 1. The second dataset is the Eurasian Cities Dataset (ECD)  [4] , which presents a much more challenging dataset of 103 urban scenes that do not satisfy the Manhattan world assumption in general.
They depict different architectural and urban styles and are taken by different cameras. Under these conditions, the method described in Section 2. 5. 2 is used. Example results for this dataset are shown in Figure  13. Following the protocol of  [4]  and other recent works  [17, 16, 18] , the first 25 images of each dataset are used to adjust the parameters of the method and the evaluation is performed on the remaining images. The performance score is measured as the area under the curve (AUC) of the cumulative histogram of the horizon line detection error. The third and fourth columns present the result of the algorithm when not applying the Manhattan-world on YUD (applying the exact same method as for ECD). At the moment of publication these results were state-of-the-art  [12]. mized by grid search on the first 25 images of each dataset.
The performance scores obtained in both training and testing subsets for both datasets are reported in Table  3. It also includes the result of applying the non-Manhattan version of the algorithm on YUD. All the results in Table  3  have been obtained with the non-accelerated version of the algorithm. In the companion code to this article, two auxiliary scripts are provided for evaluating the performance on YUD and ECD. In this subsection we present an experimental analysis of the effects of using the accelerated version of the point alignment detector instead of the one based on exhaustive search. As pointed out in Section 4, the Gaussian mixtures algorithm of  [7]  (which we will refer to as GMM) has a random initialization, which makes its result stochastic. Thus, the result of the accelerated vanishing point detector, which depends on the result of the GMM procedure, will also be stochastic.
This behavior is illustrated in Figure  14. Each run of GMM produces a different result. However, the most important Vanishing Point Detection in Urban Scenes Using Alignments   4. 0 ± 1. 3 secs 6. 9 ± 2. 9 secs 9. 8 ± 4. 6 secs 13. 0 ± 6. 6 secs 7. 02 ± 9. 1 secs Table  5 : Computation time of the vanishing points detection algorithm for one image, using the algorithm of  [7]  to generate candidate point alignments. The acceleration parameter k is the number of times the Gaussian mixtures algorithm is run to create a list of possible clusters of aligned points. The time shown is the average computation time, evaluated on the whole dataset and divided by the number of images in it, averaged on 10 runs. The CPU used is a 2. 6GHz Intel Xeon E5 with 126GB of RAM. clusters are usually captured, albeit with minor deviations.
Accelerated Version Dataset k = 3 k = 6 k = 9 k = To obtain a more precise result, one may run the GMM procedure many times, producing a bigger list of ellipses and thus more candidates for point alignments. Naturally, as the algorithm is run more times, the probability of finding the good clusters increases, as does the computation time. An analysis of the cost in both performance and computation time of running multiple instances of the GMM procedure to generate alignment candidates is presented in Tables  4  and 5. In Table  4 , the same measure of performance used in Section 5. 2, namely the horizon line estimation error, is used. The parameter k indicates the number of times the GMM procedure is run to produce candidate alignment endpoints. Since the algorithm is non-deterministic, it is run 10 times for each value of k, and the performance results are averaged.
AGA sequences analysis The cDNA sequence of A. tabida venom AGA (ACX94224) was previously reported  [8, 9]. L. heterotoma venom AGA (KP888635) was identified from venom apparatus transcriptomics data  [10]. The A. tabida (AtAGA), L. heterotoma (LhAGA), and Homo sapiens (hAGA, P20933) predicted AGA protein sequences were aligned with MAFFT  [24]. Signal peptide and N-glycosylation sites prediction were performed with SignalP (http://www. cbs. dtu. dk/ services/) and NetNGlyc (http://www. cbs. dtu. dk/services/NetNGlyc/), respectively. Pfam (http://pfam. janelia. org/) was used to search for protein domains. Protein disulfide bonds were predicted using DIpro (http://scratch. proteomics. ics. uci. edu/). Molecular modeling of mature AtAGA and LhAGA was done with the Phyre server using default parameters (http://www.
sbg. bio. ic. ac. uk/phyre2/)  [25]. For both proteins, the model with highest confidence (100%) and coverage (76% and 75% for AtAGA and LhAGA, respectively) was obtained using the crystal structure with PDB code 1P4V as template. Model quality was assessed by the QMEAN server (http://swissmodel. expasy. org/qmean/) [26]. Briefly, QMEAN score is a global reliability score with values ranging between 0 (lower accuracy) and 1 (greater accuracy). The associated Z-score relates this QMEAN score to the scores of a nonredundant set of high-resolution X-ray structures of similar size with ideal values being close to 0. Visualization of AtAGA and LhAGA model structures, superposition with the hAGA crystal structure (PDB code: 1APY)  [27] , and root mean square deviations were obtained using PyMOL v0.