dataesr/scientific-paragraphs-categorization huggingface dataset:
The prediction of gene functions is crucial for a large number of different life science areas. Faster high throughput sequencing techniques generate more and larger datasets. The manual annotation by classical wet-lab experiments is not suitable for these large amounts of data. We showed earlier that the automatic sequence pattern-based BrEPS protocol, based on manually curated sequences, can be used for the prediction of enzymatic functions of genes. The growing sequence databases provide the opportunity for more reliable patterns, but are also a challenge for the implementation of automatic protocols. We reimplemented and optimized the BrEPS pattern generation to be applicable for larger datasets in an acceptable timescale. Primary improvement of the new BrEPS protocol is the enhanced data selection step.
Manually curated annotations from Swiss-Prot are used as reliable source for function prediction of enzymes observed on protein level. The pool of sequences is extended by highly similar sequences from TrEMBL and SwissProt. This allows us to restrict the selection of Swiss-Prot entries, without losing the diversity of sequences needed to generate significant patterns. Additionally, a supporting pattern type was introduced by extending the patterns at semi-conserved positions with highly similar amino acids. Extended patterns have an increased complexity, increasing the chance to match more sequences, without losing the essential structural information of the pattern. To enhance the usability of the database, we introduced enzyme function prediction based on consensus EC numbers and IUBMB enzyme nomenclature.
ProDom  [5]  and Pfam  [6]  provide patterns related to protein domains while other databases focus on patterns to categorize proteins into families, like PRINTS  [7] , HAMAP  [8]  and PRIAM  [9]. InterPro is a database classifying protein sequences into families predicting important domains and sites  [10]. InterPro incorporates 14 different databases, including those mentioned above and other databases like TIGRFAMs  [11] , CCD  [12]  and SFLD  [13]. The Braunschweig Enzyme Pattern Search (BrEPS) is a fully automated protocol, that generates enzyme specific sequence patterns based on manually annotated Swiss-Prot sequences  [14]. Here we present BrEPS 2. 0, the reimplementation of the original BrEPS protocol, enhanced with a more reliable data-source, an additional pattern type and more refinements to optimize the pattern creation.
Implementation We reimplemented the original BrEPS protocol to be able to process larger datasets in an acceptable time frame. The original BrEPS workflow  [14]  was used as guide for the new implementation. BrEPS 2. 0 consists of six steps (Fig  1 ). The first step is the selection of sequence data from the UniProt Knowledgebase  [15]  (Fig  1A ). In the second step, all enzyme sequences are aligned to each other, using NCBIs BLAST+  [16]  program (Fig  1B ). The third step is the agglomerative hierarchical clustering of sequences, using the E-value obtained from the BLAST run, using the complete linkage clustering algorithm  [17, 18]  (Fig  1C ). In the fourth step, a multiple sequence alignments (MSA) are performed on certain cluster nodes, using Clustal Omega  [19]. Based on the MSA consensus lines, the BrEPS patterns are generated  (Fig 1D).
All patterns get verified in the fifth step. The prediction quality of the patterns is verified by comparison with the initial database and non-enzymes. The positive predictive value is calculated based on matches of EC numbers  (Fig 1E). A final step was introduced in BrEPS 2. 0, that creates the final compressed BrEPS database (Fig  1F ). The BrEPS 2. 0 protocol follows the rules of the original protocol, major optimizations in the new protocol are described in more detail further below.
In this case,   3. Based on the consensus EC number, the function is annotated from the IUBMB database  [23]. The recommended enzyme name is used for full EC numbers. For partial EC numbers the class, subclass and sub-subclass (if given) description is used instead. All EC numbers are stored with the number of occurrences, to keep track of the composition of the proposed EC number. Also, the UniProt accessions are kept for reference.
Clustering. A major bottleneck in the original BrEPS pipeline was the clustering step, because it needed an extensive amount of main memory for the clustering. We implemented a database assisted clustering algorithm based on the optimally efficient clink algorithm  [17]. The distance matrix is saved and modified in the MySQL database and only dictionaries for nearest neighbors and their distances are stored in the main memory. Using this algorithm, the clustering needs a negligible amount of main memory, while running in an acceptable timescale of less than a week on a single CPU core. Verification. The verification step is crucial to ensure a high quality of the generated patterns. To use a reliable source of sequences for verification, all patterns are verified, using the Swiss-Prot seed sequences and non-enzymes with evidence on protein level.
Runtime and resource usage BrEPS 2. 0 was implemented using Python 2. 7, with MySQL as database backend. All steps, except the clustering step, are parallelized for usage on a computer cluster. On a computer cluster with 36 CPU cores, BrEPS 2. 0 takes about 10 days to create and verify the patterns. The maximum usage of main memory is about 1-2 GB (pattern verification step).
Data selection The new data selection creates a set of sequences for pattern generation. The working database for the UniProt release 2017_1 contained 29,765 enzyme sequences with evidence on protein level. In comparison, the data selection of BrEPS 1 would have selected 197,451 SwissProt sequences. The 30,317 seed sequences were used to select 348,603 similar non-redundant sequences from TrEMBL. For the verification of the generated patterns, 196,554 non-enzyme sequences were parsed from UniProt.
Evaluation of pattern quality To evaluate the prediction rate of the patterns generated with the new BrEPS protocol, a standard of truth independent from the dataset used for pattern creation has to be defined. Here, a reliable source of sequences in combination with an annotated EC number is needed to check for the prediction rate of patterns. The enzyme database BRENDA  [24]  matches the criteria as reliable source of sequences with annotated EC numbers. BRENDA gets the sequences from UniProt, but does their own annotation using literature data of enzymes with experimental verification. Therefore, the annotation for the sequences in BRENDA is independent from and superior to UniProt, as Swiss-Prot entries with evidence on protein level does not strictly include experimental evidence of the annotated function.
BRENDA contains sequences not just from Swiss-Prot, but also from TrEMBL. To get a solid set of sequences for the evaluation, all BRENDA sequences with an annotated EC number were used. The sequence set for evaluation consisted of 14,106 Swiss-Prot sequences and 11,384 TrEMBL sequences. Evaluation procedure. The evaluation set of sequences was matched against the BrEPS database using the BrEPScmd command line tool. Every pattern can either hit or miss a sequence. If a sequence is not found by any pattern in the BrEPS database, the sequence was considered as a negative (N) match. Positive matches were further classified by comparison of the EC numbers of the BRENDA annotation and the BrEPS annotation. Here, two levels of EC number matches were used: Strict match: The EC number of the sequence exactly matches one of the BrEPS EC numbers, incomplete EC numbers (i.
e. 1. 1. 1. -) do not match. Fuzzy match: Like the strict match, but incomplete EC numbers can match (i. e. 1. 1. 1. -can match 1. 1. 1. 1). Undefined positions count as wild card character. Hits were counted as true positive (TP), if one of the pattern EC numbers matched the EC number annotated in BRENDA, otherwise as false positive (FP) hit. As the sequences are derived from UniProt, it is possible that it was used for pattern generation, therefor we checked if the sequence were present in the BrEPS working database to assess the reliability of the evaluation. DR ¼ TP þ FP TP þ FP þ N ð1Þ PPV ¼ TP TP þ FP ð2Þ Evaluation results. For the comparison with the original BrEPS implementation, we used the UniProt release 2014_10 with the new protocol to generate BrEPS patterns. We used the described evaluation procedure with the original BrEPS patterns (BrEPS 1.
0) and our reimplementation with the described enhancements (BrEPS 2. 0). The evaluation results (Table  4 ) show an increased detection rate (Eq (1)) for BrEPS 2. 0 compared with BrEPS 1. 0 of more than 50%. The positive predictive value (PPV, Eq (2)) of BrEPS varies, depending on the EC number matching level used. For the strict matching, BrEPS 2. 0 has a higher PPV than BrEPS 1. 0, while for the fuzzy matching, both protocols are on par with about 90% PPV. The extended patterns of BrEPS 2. 0 contributed to the fuzzy matches with 101 true positive hits and 29 false positive hits which had not been hit by a standard pattern. The sequences used for evaluation from BRENDA are also part of the UniProt database, and therefore, could have been part of the BrEPS pattern generation process. BrEPS 2.
0 was able to detect more sequences which have not been part of the pattern generation with a positive predictive value of 86. 35% and a detection rate of 31. 36%. It should be noted, that the presence of a sequence in the working database does not imply, that a pattern is associated with it. However, due to the addition of similar TrEMBL and Swiss-Prot sequences, it is possible that a similar sequence is present. We also created the BrEPS database using UniProt release 2017_01 (BrEPS version 2017. 1) and evaluated the patterns (Table  5 ). Due to the larger UniProt database, the BrEPS patterns have a higher positive predictive value compared to the BrEPS pattern based on UniProt release 2014_10. This shows, that future updates will further enhance BrEPS patterns due to UniProt data updates.
Comparison with InterPro InterPro is a manually annotated database, that incorporates different databases and approaches to generate a comprehensive resource for annotation of protein functions  [10]. We used our standard of truth to evaluate InterPro version 63. 0 data, using a comparable approach to the BrEPS evaluation. InterPro provides different classifications, so multiple and contrary annotations are possible for a protein. Therefore, we used a strict and a loose evaluation. For the strict evaluation all EC numbers of the InterPro results have to match the standard of truth EC number to generate a true positive hit, otherwise a false positive hit is generated. For the The results of our analysis are shown in Table  6. We compared the evaluation of the strict matching of BrEPS 2. 0 (as described above) with the two InterPro evaluations.
Conclusion We have completely reimplemented the original BrEPS protocol for generation of enzyme specific sequence patterns. The new protocol has a high maintainability for future usage of the pattern generation pipeline. The new data selection criteria ensures a high-quality sequence source for pattern generation by using enzyme sequences with evidence on protein level. The newly introduced extended patterns are based on the amino acid similarity commonly found in proteins. While retaining the initial structure of the pattern, the extension with similar amino acids create slightly more complex patterns. Analysis of the BrEPS verification step and the evaluation with BRENDA annotations showed, that the enhanced complexity of the extended patterns is detectable and can improve the pool of BrEPS patterns.
The evaluation was performed using a non-restricted set of sequences based on BRENDA annotations. It could be shown, that the BrEPS 2. 0 patterns have a high detection rate and positive predictive value, with the limitation for similar sequences used for generation. The new protocol shows improved values for the detection rate and positive predictive value, although the initial pool of Swiss-Prot sequences and annotations was reduced. However, as all similarity based methods, BrEPS is limited to enzymes which are well described and characterized. Keeping the limitations in mind, BrEPS is a highly sensitive source for enzyme annotation and especially, due to the high sensitivity, for the verification of annotations. Additionally, regular updates of the BrEPS database will further improve the detection rate, as long as the Swiss-Prot part of UniProt grows (compare Tables  4  and 5 ).
Availability The BrEPS database is freely available using the new designed web-service at http://breps. tubs. de. The web-service features a BrEPS sequence search and a full-text search for patterns, as well as a comprehensive overview for all patterns with full annotation of EC numbers and accessions. Additionally, the BrEPS database can be downloaded in MySQL and SQLite format. For large scale batch analysis, we offer a command line tool BrEPScmd, written in Python, which can be used to search the downloadable database. The database and command line tool (BrEPScmd) are available at our website http://breps. tu-bs. de. The BrEPS source-code is available under GPL-3. 0 license on our website.
This only requires the import of the 3D density and temperature profiles from the EMC3-EIRENE simulations into the antenna codes. In addition, this work is part of a continuous effort to valid ate the EMC3-EIRENE code using available data from present tokamaks in order to carry out future simulations for ITER with confidence and to define best gas injection locations to optim ize ICRF antenna coupling. This work completes the already published simulations for AUG  [8]. AUG is a middle sized tokamak with major radius/minor radius = 1. 65 m/0. 5-0. 8 m, plasma volume = 13 m 3 and B T /I p = 2. 5 T/0. 8 MA. JET is a larger tokamak with major radius/minor radius = 2. 96 m/1. 25-2. 10 m, plasma volume = 100 m 3 and B T /I p = 2. 7 T/2. 5 MA. A full-tungsten wall is used in AUG while an ITER-like wall (tungsten divertor and Beryllium main chamber wall) is used In JET.
The complex ionization processes are calculated in EIRENE based on the AMJUEL/HYDHEL database and an electron density cloud is formed in front of the GIMs where the ionization happens. The cloud of enhanced density can be seen in figure  7  (right column). The extension of the density cloud mostly depends on the spreading of the neutral gas while the peak density largely depends on the ionization rate. In the density cloud, the electron temperature is lower than the background plasma temperature. Due of the rapid motion of charged particles along field lines (i. e. the parallel transport), density in regions which are magnetically connected to the density cloud are soon enhanced. The density increase in the SOL depends on the structures of the field lines, on the density cloud as well as on the perpendicular transport.
the simplified antenna geometry and reduced physics in the 1D antenna code; 3. the errors in the experimental data which is used for validating the background plasma (section 2); 4. errors of the experimental R c values; 5. the simplified wall structures in the EMC3 simulations; 6. the imprecise settings of the transport parameters in our simulations; 7. local ioniz ation effects due to the large RF fields close to the antennas that are not included in the simulations  [22] ; 8. radiation patterns from impurities and plasma; 9. plasma-wall interaction (sputtering, reflection on metallic wall versus carbon wall). To understand the influence of the transport parameters on the RCCR values, further EMC3-EIRENE simulations are carried out with different particle transport parameters specified in the far SOL (case A: = ⊥ - D 6 m s 2 1 ; case B: = ⊥ - D 3 m s 2 1 ; case C: = ⊥ - D 9 m s 2 1 ).
Conclusions and outlook In continuation of a previous study for AUG  [8] , realistic and comprehensive gas puff simulations were carried out in JET-ILW with the 3D edge plasma fluid and neutral particle code EMC3-EIRENE. In the simulations, we have implemented grid with 360° toroidal extension and included all the essential plasma facing components, the Gas Injection Modules (GIMs) and the gas pump. Various gas puffing scenarios including the local gas puff and the combined gas puff are investigated. Our simulated electron density and neutral pressure are in quantitative agreement with the experimental ones. The simulations confirm that during mid-plane gas puffing, the edge density is increased most significantly but locally. The largest ICRF increase (~130%) is found for the antenna nearest to the mid-plane GIM, and this increase gradually decays with the toroidal distance of the antenna to the gas injection point.
Local ionization effects due to large RF-fields close to the antenna structures were not included in the simulations. To ensure the best ICRF coupling efficiency, using GIMs which can generate local gas magnetically connected to the antennas is important. The simulations also confirm that combined gas puff can be effective in increasing the SOL density and the ICRF coupling globally. The validation of 3D simulations with the EMC3-EIRENE code and the experimental results in AUG  [8]  and JET-ILW make us confident that similar simulations can be carried out for ITER and other tokamaks. From our studies, it can be inferred that using middle plane gas valves closer to the powered ICRF antenna is the optimized way in terms of maximizing ICRF power coupling. We plan to do similar simulations for ITER and DEMO in the near future.
Avoiding radiation during the imaging protocol is also a major advantage. The acquisition of both CT and MR images of the patient is already part of the clinical workflow for some indications. MR data is used to define the target volume (i. e. the tumour) and CT data to plan the treatment. Image registration is used to define a spatial relationship between the two images allowing any manual contouring from the MRI to be mapped to the planning CT. However, with this approach, the workflow is extremely dependent on the quality of the image registration  [3, 4]. The increased cost and workload for clinicians, when using two different image modalities, is also undesirable  [5]. Due to these limitations, there is a growing interest in using an MRI-only RTP workflow. However, as no fundamental relationship between MR image intensities and electron density values exists  [6] , an accurate method to derive CT equivalent information from MR data (referred to as synthetic CT) is required to perform dose calculations.
Although these studies showed promising results, their use is limited by the manual delineation step, making them non-viable in an online workflow. Automatic delineation is challenging as bone is not easily distinguishable in traditional MR sequences, due to bone's short T2 ⁄ relaxation time. Despite these challenges, bulk density assignment approaches have recently been made available in clinical RTP software platforms, such as the MRCAT  [12]  by Philips (Philips, Best, Netherlands), and are already used in practice for cone beam CT-based dose calculations  [13]  and to account for tissue heterogeneities (i. e. presence of metal implants). Other methods exist to obtain synthetic CT (sCT) images automatically from MR images and many have been applied to RTP. Hsu et al. [14]  used a fuzzy c-means algorithm to segment a set of structural MR images into five tissues classes.
The sCT was generated by assigning relative attenuation coefficients with weights based on the probability that each class exists at a given location. Jonsson et al. [15]  applied the method developed by Johansson et al. [16]  where a sCT was obtained from a Gaussian mixture regression model linking the MRI intensity values to the CT HUs. Another family of methods, the atlas-based methods, rely on a single template  [17]  or a database of MR and CT image pairs  [18] [19] [20] [21] [22] [23]. First, a non-rigid registration between the atlas and test subject MR images is performed. Then, the same transformation is applied to the associated CT images and finally, for the multi-atlas methods, the registered CT images are fused to generate the final sCT. The fusion can be obtained by computing the voxelwise median  [21] , using a probabilistic Bayesian framework  [22] , an arithmetic mean process or pattern recognition with Gaussian process  [23]  or a local image similarity measure  [18, 19].
Instead of using a database of images, Andreasen et al. [24]  employed a dictionary of MR and CT patches. The sCT was predicted by extracting patches from the test subject MRI, running an intensity-based nearest neighbour search in the patch database and fusing the selected CT patches using a similarity-weighted average. Combining segmentation and use of a template database, Siversson et al. [25]  proposed a statistical decomposition algorithm to automatically generate sCTs. The multi-atlas CT synthesis approach evaluated in this work was first developed for brain applications  [26, 27]  and then extended to H&N cancer  [18]. In this paper, we present a thorough validation of the method, not only for H&N but also for patients with prostate cancer. The main difference with most of the other multi-atlas methods  [21] [22] [23]  is that the fusion of the atlases is based on the local similarity between each atlas and the test subject.
Data acquisition Retrospective data from six H&N patients (with oropharyngeal cancer) treated with volumetric arc therapy (VMAT) and fifteen prostate patients treated with fixed-field intensity-modulated therapy (IMRT) were included in this study. Each patient had a planning CT scan (Philips Big Bore CT), a T1-and T2-weighted turbo spin echo MRI (Siemens 1. 5T MRI), a CT delineated structure set and a clinically approved treatment plan (Pinnacle 3 , Philips Medical Systems) to a total dose of 65 Gy and 67-74 Gy for H&N and prostate patients, respectively. All patients were imaged on the same day and in the same position -head-first supine -for both MR and CT image sessions. For H&N patients, the same fixation device was used while, for prostate patients, a different couch was used for MR (curved couch) and CT (flat couch) imaging sessions.
For all H&N patients, the resolution of both T1-and T2weighted MR scans was 0. 104 Â 0. 104 Â 0. 2 cm 3. For all prostate patients the resolution of T1-and T2-weighted MR scans was 0. 164 Â 0. 164 Â 0. 5 cm 3 and 0. 146 Â 0. 146 Â 0. 5 cm 3 , respectively. For H&N patients, the resolution of the planning CT was 0. 117 Â 0. 117 Â 0. 2 cm 3 while, for prostate patients, it was 0. 098 Â 0. 098 Â 0. 2 cm 3. All patients included in this study had given consent for their data to be used for research purposes. Because of the retrospective nature of the study, inconsistencies exist between the different imaging modalities acquired. A large field-of-view (FOV) was available for the CT scans (scanning level for H&N patients extends from the top of the head to the apex of the lungs and for prostate patients from the abdomen to the lower limbs).
Multi-atlas CT synthesis The approach for the generation of the sCT a has been described in detail in previous publications  [18, 26, 27]. Briefly, the proposed method relies on pre-acquired pairs of non-rigidly registered T2and/or T1-weighted MR and planning CT images. The non-rigid alignment was necessary to compensate for position differences (and the use of different couches for the prostate patients) between the MRI and CT acquisitions. For the H&N patients, the atlas database was composed of seventeen pairs of T2-weighted MR and CT images, as described in  [18] , and the method was validated using the images of six other H&N patients not included in the database. Regarding the prostate patients, the atlas database was composed of both T1-and T2-weighted MR, and CT images of fifteen patients.