dataesr/scientific-paragraphs-categorization huggingface dataset:
Regex-based tools include DILIMOT  (11) , SLiMFinder  (12)  or MotifHound  (8). The popular MEME Nucleic Acids Research, 2017, Vol. 45, Web Server issue W471 suite  (13) , which includes MEME and GLAM2  (14)  for SLiM discovery, uses position weight matrices and Gibbs sampling, respectively. Several algorithms based on HMMs were also reported (NestedMICA  (7) , whmm  (10)  and dhmm (  9 )). However, the latter three all lack web-server access and are more difficult to use for bench scientists. More recently, de Brujin graphs were tested for SLiM discovery in proteins  (15). De novo SLiM search methods can also be classified as either non-discriminative, which only require a set of putative SLiM-containing proteins as input (these include SLiMFinder, MEME, DILIMOT and whmm); or discriminative, which in addition require a negative dataset of proteins that do not contain the putative SLiM.
Therefore, additional biological knowledge is necessary for these methods in order to define a negative dataset for the sought-after SLiM. However, this knowledge does not always exist. Mo-tifHound and dhmm are examples of discriminative de novo SLiM predictors. Several papers and reviews provide a comprehensive overview on SLiM discovery methods, as well as the inherent problems in finding novel SLiMs  (8, 16, 17). As SLiM discovery is computationally a difficult problem, none of the de novo SLiM search methods reported to date are able to discover SLiMs in proteins with reasonably good recall and precision. In fact, in de novo SLiM prediction, one has to typically trade off one for the other, reaching either high recall (e. g. GLAM2 with default settings) or high precision (e. g. SLiMFinder with default settings).
Selection of closely related orthologs. As SLiMs can either be lost, gained or move along the sequence in evolution, we decided to only include closely related orthologs to the queries for building HMMs. BLAST searches  (19)  against the NCBI non-redundant (nr-) protein database are carried out to identify close homologs of each input sequence that fulfill the following criteria: e-value ≤ 1e-10; identity ≥ 70% and ≤ 95%; coverage ≥ 90%. These settings exclude too similar, as well as too distant orthology candidates. For candidates fulfilling these criteria, reciprocal BLASTs are used to verify orthology relationships; we consider all isoforms of the query for verification of orthologs. In advanced mode, users can provide their own lists of orthologs for further processing. Residue masking.
N min is computed on the basis of the dataset size using a dynamically estimated false positive rate (FPR) on negative data that lack a common motif: N min is chosen such that the FPR is <1% for each set size (for details, see Supplementary Data and Supplementary Table  S1 ). This low FPR is consistent with the >99% specificity that HH-MOTiF demonstrates on ELM data. However, a motif occupies only a small fraction of a protein's sequence. Therefore, owing to the false positive paradox  (25) , a high specificity in this case does not ensure an equally high precision. Motif definition and evaluation. In the next step, positions with significant conservation in the motif tree are identified and evaluated. First, a score for each position in the motif tree must be calculated. We derive this position score from the conservation signs in the hhalign output between the motif root and its leaves: motif tree positions with at least N min -1 alignment hits of high conservation (indicated by '|' or '+') score two points; whereas those, where this requirement is fulfilled by also considering moderate conservation (indicated by '.
Rootleaf pairs, which both locate to the same overall homology region are already discarded at an earlier stage. However, two leaves can still appear within a shared conserved domain or larger homologous region between two query proteins. In this case, only one of the two leaves will be used for scoring, but both will be reported in the results. Thus, the effective number of proteins corrected for homology N corr is used for further calculations instead of the total number N tree of proteins, which participate in a specific motif tree. Regex generation and statistical evaluation. For motif trees that pass, a regex is generated from its conserved columns for further motif evaluation and final reporting to the user. For each regex, the probability to occur by chance within the submitted dataset is calculated.
Optimization and evaluation of HH-MOTiF and comparison with other de novo SLiM search tools First, we used all experimentally verified SLiMs from the ELM database  (29) , which occur in at least three proteins to optimize HH-MOTiF. These included 176 motifs (classes) grouped into six types. The types CLV and DEG were used as training set; all other types (DOC, LIG, MOD and TRG) were used as test set. It would be tempting to introduce a simple evaluation protocol, where each annotated SLiM is either 'rediscovered' (true positive) or 'missed' (false negative), as well as each predicted motif is either 'correct' (true positive) or 'incorrect' (false positive). However, in reality, predicted motifs are usually correct only to some extent, as they contain true positive residues or sequence stretches with varying de-grees of additional false negatives and false positives.
Therefore, we did not rely on a binary classification on motiflevel for performance evaluation. Instead, we used performance measures calculated residue-wise and site-wise for all selected 176 SLiMs in the ELM database. We primarily used the balanced F1-score (F1) for evaluating performance, which offers a balanced measure between sensitivity and specificity. We calculated an overall F1 based on simple averaging across all 176 SLiMs from the ELM database. As an approximate binary classification on motif-level, we also counted how many ELM classes out of the 176 reached a residue-wise F1 of at least 0. 5. To allow comparison with other statistical evaluations, we also provide data on balanced accuracy (BA) and the performance coefficient (PC) for all tested methods in Supplementary Data, where readers can also find the details on calculating F1, BA and PC.
Being fairly balanced between recall and precision, HH-MOTiF reached a site-based F1 of 0. 333 and a residuebased F1 of 0. 280 (Table  1  top row, Figure  3  and Supplementary Tables  S2,  and S3 ). We used the same dataset to compare our method to other, published de novo SLiM search tools. We focused on methods that work nondiscriminatively and which provide a stand-alone version for local usage. Software packages considered included MEME (v4. 0), GLAM2 (v4. 11. 1) and SLiMFinder (v5. 2. 3). The downloadable version of the HMM-based method whmm did not work in our hands. We could there-  fore only compare our results to the originally published data (  (10) , see Supplementary Data). We tested different parameter values for all selected tools and chose those settings, which yielded the highest F1 (performance measures for all selected settings are available in Supplementary Table  S2 ; performance dependencies of HH-MOTiF on several parameters are discussed in Supplementary Data and are shown in Supplementary Figure  S2 ).
HH-MOTiF had the best F1 compared to all other tested tools, closely followed by SLiMFinder (see Table  1 , Figure  3  and Supplementary Tables  S2-S6 ). Our method reached a reasonable recall with a fairly good precision. For SLiMFinder, we used settings that turned the tool more sensitive, at the cost of its otherwise high precision with standard settings (see Supplementary Table  S2 ). GLAM2, on the other hand, scored highest of all in recall, however performed poorly in precision. HH-MOTiF scored also better in site-wise PC than others, while GLAM2 performed better in BA. SLiMFinder had the best residue-wise PC, which could be explained by the fact that it tends to predict SLiMs that are shorter than the ELM annotation and no false positives due to flanking residues are produced. We also observed a dependency of F1 on the size of the dataset for some tools (Supplementary Table  S7 ).
These data indicate that SLiMFinder performs best on more abundant motifs, MEME on the longest ones, and HH-MOTiF on repeated motifs. Nevertheless, we think that simple averaging is the most useful approach for performance evaluation, as it is reasonably unbiased: it does not allow for 'easy' cases (long, abundant and protein tandem repeats) to outweigh the 'hard' ones (short and less frequent motifs).
Second, the motif-tree structure also allows us to consider flanking residues to a higher degree, even though they will not appear in the reported SLiM. The final SLiM is scored based on the initial pairwise alignment scores, not only on the regions of the SLiM, which is conserved in the minimum set of sequence queries. As a result, flanking regions contribute substantially to identifying SLiMs in HH-MOTiF. Finally, HH-MOTiF can detect several independent motif trees that occur in independent, possibly overlapping subsets of the provided input sequences (data not shown). HH-MOTiF does not filter full-length sequences for homology, but rather candidate SLiMs at the level of their HMM-alignments. Therefore, it allows for graceful handling of homologous regions, conserved domains, and low complexity regions in the input proteins.
As an example, due to extended low complexity regions of proteins containing the ELM motif LIG EF ALG2 ABM 2, SLiMFinder classified the whole dataset as too homologous and returned no results, while GLAM2 with default settings returned excessive putative positives, resulting in a precision <1%; with our optimized settings, it failed to find this SLiM. HH-MOTiF on the other hand correctly identified this SLiM as the only hit in the dataset. Consequently, users must not remove too closely related sequences prior to submission to the HH-MOTiF web-server. Furthermore, the fact that we do not explicitly filter for low complexity regions enables HH-MOTiF to distinguish between low complexity SLiMs and unrelated low complexity regions (see exemplary motifs LIG SH3 3 and LIG AP GAE 1 on the Tests site of our web-server).
This analysis showed a significant sharing domain binding between OCT4 and a number of transcription factors in TADs (Fig. 5e ). Assuming that the loci bound by transcription factors are connected in three dimensions to genes within the same TAD, the enrichment of particular transcription factors in the same TAD suggests that they co-regulate genes found in the TAD. To visually demonstrate our analysis, we used "Juicebox" software for visualizing data from Hi-C mapping experiments. By this analysis, for example, the MITF target gene TRPM1 was found to be in an OCT4 and MITF shared TAD (Fig. 5f ), supporting our hypothesis that OCT4 interferes with MITF transcriptional activity by sharing the same TADs (Supplementary Table  7 ). These findings indicate that cell type-specific regulatory sites can be engaged by a combination of transcription factors in a cooperative manner.
Study design and participants A phase II, randomised, open-label feasibility trial was conducted-ClinicalTrials. gov: NCT01419145. Eligible patients met the following criteria: age 18-80 years; stage III/IV nonsmall cell lung cancer or inoperable pancreatic cancer; due to commence chemotherapy; Karnofsky performance status >70; no contraindication to the study interventions (primarily the anti-inflammatory medication); body mass index <30 kg/m 2 ; and <20% weight loss in the previous 6 months. Patients who had received any systemic anti-cancer therapy in the preceding 4 weeks, or who were taking regular oral steroid medication, were not eligible. Patients who were participating in other interventional clinical trials or who within 30 days prior to inclusion were taking other agents for the prevention or treatment of cachexia (such as megestrol acetate, progestational agents, growth hormone, dronabinol, marijuana, or other anabolic agent) were not eligible.
The secondary endpoints were assessment of weight, muscle mass (assessed by CT measurement of muscle mass), physical activity (ActivPAL and 6MWT), hand grip strength, nutritional status (AveS and aPG-SGA score), and fatigue score. These were assessed at baseline and after 6 weeks (endpoint). Safety and survival were also assessed as secondary endpoints. Muscle mass was assessed using CT scan images performed as part of patient management, which were retrieved from digital storage in the picture archiving and communication system. Muscle mass levels at L3 are highly correlated to total body muscle mass (r2 = 0. 86). 20 Axial images at the L3 level were selected out and analysed using the 'Automated Body Composition Analyzer using Computed tomography image Segmentation' (ABACS) software. 21 Using Hounsfield unit thresholds of À29 to 150 for skeletal muscle, À50 to 150 for visceral adipose tissue, and À190 to À30 for subcutaneous adipose tissues, the program recognized shapes and predictable patterns to accurately predict values.
The sum of skeletal cross-sectional muscle areas was normalized for stature (m2) and reported as lumbar skeletal muscle index (cm2m-2). The primary endpoints were chosen to assess the feasibility of a delivering a multimodal intervention for cancer cachexia. The secondary endpoints were regarded as exploratory to inform future trial design, should the primary endpoints be positive and future trials be deemed worthwhile. It was anticipated that most patients entering the trial would have non-small cell lung cancer. Independent of the treatment given, the majority of these patients have 2 cycles of chemotherapy over a total of 6 weeks. The endpoint after 6 weeks was chosen to reflect the standard chemotherapy treatment regimens in the UK and Norway and enabled the trial to use existing radiological data (CT scans) and assessments to coincide with hospital visits.
Conclusions This trial is the first to demonstrate that patients with advanced cancer who have a high risk of developing cachexia are willing and able to participate in a randomized controlled trial of a complex intervention that includes a defined exercise programme. The positive effect of the multimodal cachexia intervention on weight provides grounds for optimism that cachexia need not be an inevitable consequence of advanced cancer but rather may be attenuated through a multimodal intervention targeting its genesis. A larger, pragmatic, multimodal phase III trial assessing the effectiveness of anti-inflammatory treatment (EPA/NSAID), nutrition and exercise in cancer cachexia is now underway (EudraCT 2013-002282-19). Should this demonstrate that such an intervention can prevent or attenuate cancer cachexia; this would have considerable implications for clinical cancer care Contributors TS, BL, KF, and SK were responsible for the conception, design and interpretation of the data.
TS, BL, KF, SK, PF, TRB, AB, and GS drafted the protocol. Drafting of the manuscript was led by BL and TS. Recruitment was done by BL, TS, SK, TRB, GS, and AB. MF contributed to study design and interpretation of the data. CT analysis was undertaken by NJ. EPA analysis was undertaken by TRB and CP. Data analysis was led by BL and supervised by PF.
Acknowledgements The authors would like to thank the following: United Kingdom:  West of Scotland Hepatobiliary and Pancreatic Surgery Department :  Ross Carter ,  Colin McKay ,  Ewan Dickson ;  Jonathan Hicks  and the  Respiratory Oncology Team  based in the  New Victoria Hospital/Southern General Hospital, Glasgow ;  Liz-Anne Lewsley  and  Colleagues, Cancer Research UK , Clinical Trials Unit, based in the  Beatson West of Scotland Cancer Centre ;  Dr Nicholas MacLeod  and  Dr Claribel Simmons ;  Anne Todd  and  Veronica Davey ;  NHS Greater Glasgow and Clyde ;  Matthew Maddocks , based in the  Cicely Saunders Institute, Kings College, London. Norway: St. Olavs Hospital, Trondheim University Hospital ,  Sveinung Sørhaug ,  Ingunn Hatlevoll ,  Cinzia Marini ;  Cancer Fund, St. Olavs Hospital ;  Nordic Cancer Union ;  Norwegian Cancer Society and Vardesenteret St.
907). Using an FVF ranging within [0. 58, 0. 82] and a mean inter-axon gap ranging within [0. 1, 1. 1] µm, MVF ranged within [0. 32, 0. 44] and fr ranged within [0. 39, 0. 71], which is consistent with the histology. The proposed algorithm is implemented in the open-source software AxonPacking (https://github. com/neuropoly/ axonpacking) and can be useful for validating diffusion models as well as for enabling researchers to study the interplay between microstructure parameters when evaluating qMRI methods.
Balls and Frank (2009)  proposed to add fibers across several iterations until a user-defined density is reached, which precludes the possibility to optimize packing from it. When looking at the problem in 3D, non-parallel fibers are usually simulated randomly without packing optimization  (Kamiya et al. , 2015). In this paper, we present AxonPacking 4 , a novel, easy-to-use and open-source Matlab algorithm for simulating axon packing with user-defined diameter distribution of the axons and the gap between axons. This algorithm is based on (MD)without any border constraints. Disk (i. e. , axon) density is optimized by migrating disks toward the center of the 2D space. A couple of features were specifically added to model the white matter realistically: gamma distributions of axon diameter, presence of a gap between axons, and myelin thickness.
Axonpacking Algorithm The simulator is implemented in Matlab (R2014a). The different steps to process packing are the following (Figure  2A ): (i) the diameters of the disks are randomly chosen using a Gamma distribution parameterized with the mean (µ), standard deviation (σ) and number of axons (N); (ii) the positions of disks are initialized on a grid, and then migrate toward the center of the packing area until the maximum disk density is achieved. A video illustrating the packing is available at: http://www. neuro. polymtl. ca/downloads. The random disk diameters are obtained using a Hasting Metropolis algorithm  (Chib and Greenberg, 1995). Briefly, from an initial diameter value, subsequent diameters are randomly drawn with a Gaussian law and accepted (or rejected) according to a probability depending on the Gamma probability density function.
Outputs: 3 Matlab structures and one image • The axon features (N, d_mean, d_var, Delta, g_ratio, and the drawn diameters d) stored in axons. mat, • The packing results (initial positions of disks  (initial_positions)  and final positions of disks (final_positions) stored in packing. mat, • The statistics results with the values for each metric computed in the packing (FVF, FR, MVF, AVF) stored in stats. mat, • A png image of the final packing with three different labels (intra-axonal, myelin and extra-axonal).
CONCLUSION AxonPacking is a novel open-source software for simulating white matter microstructure, in which axons are assumed to be parallel cylinders. AxonPacking generates random disk packing with user-defined diameter distribution and gaps between the disks, and then computes the following microstructure features: (MVF), (FVF) and restricted water fraction (fr). AxonPacking can be useful for validating diffusion models as well as for enabling researchers to study the interplay between microstructure parameters when evaluating qMRI methods. AxonPacking can be downloaded at https://github. com/neuropoly/axonpacking.
ACKNOWLEDGMENTS We thank the  MS Society of Canada  (Duval, # 2370 ), the  SensoriMotor Rehabilitation Research Team (SMRRT)  of the  Canadian Institute of Health Research , the  Canada Research Chair in Quantitative Magnetic Resonance Imaging (JC) , the  Canadian Institute of Health Research [CIHR   FDN-143263 ], the  Fonds de Recherche du Québec-Santé  [28826], the  Fonds de Recherche du Québec-Nature et Technologies  [ 2015-PR-182754 ], the  Natural Sciences and Engineering Research Council of Canada  [ 435897-2013 ] and the  Quebec BioImaging Network. We also thank people of the  NeuroPoly Lab  and in particular  Victor Herman  and  Aldo Zaimi.
The primary outcome for this analysis was 3-month steroid-free clinical remission at 1 year after HSCT (Crohn's Disease Activity Index [CDAI] <150). We also examined the degree of endoscopic healing at 1 year. Multivariate analysis was performed to identify factors associated with achieving the primary endpoint by using logistic regression, and factors associated with experiencing a serious adverse event using Poisson regression. Participants were not masked to treatment, but the adjudication panel that reviewed radiology and endoscopy was masked to allocation and visits. All patients who underwent HSCT and had data available at baseline and 1-year follow-up were included in the primary and safety analysis. This trial is registered with ClinicalTrials. gov, number NCT00297193. FINDINGS Between June 28, 2007, and Sept 1, 2011, 45 patients were enrolled in the ASTIC trial from 11 European transplant units.
Assessments and time points All patients underwent the same schedule of assessments prior to and over the year following HSCT. This comprised (i) disease activity, haematology, biochemistry, adverse events according to EBMT guidelines,  15  concomitant medication every 6 weeks (ii) quality of life (IBD-Q and EQ-5D) every 6 months and (iii) endoscopic and radiological assessment of the entire intestine at baseline and one year. Baseline data was recorded prior to mobilisation in the patients undergoing immediate HSCT and prior to conditioning in those undergoing HSCT at one year. Outcome data were recorded one year after conditioning for all patients. An adjudication committee who were blinded to time of assessment and prior treatment assignment reviewed all radiology and endoscopy reports to determine intestinal disease activity.
An identical schedule of clinical, endoscopic and radiological assessments was performed over the subsequent year. Therefore, assessment of baseline and one-year outcome from the combined group of patients in the ASTIC trial irrespective of timing of HSCT has allowed a robust assessment of the impact of stem cell transplantation. Importantly it includes sufficient numbers to identify factors that predict clinical benefit. The potential for bias inherent in an 'open label' assessment is reduced by robust nature of the data collection and monitoring that was required for a controlled trial. In addition, all endoscopic and radiological investigations were reviewed and scored by an adjudication panel who were blinded to the timing of the assessment and the treatment assignation of the patient.
Supplementary Figure 1: Consort diagram In the original ASTIC trial 132 patients were screened for eligibility, 99 were provisionally approved by the TSC, 62 signed consent forms and 50 proceeded to be registered for trial inclusion having met all criteria for inclusion. Forty-eight underwent mobilisation, and 45 proceeded to randomisation. Twenty-three patients underwent early HSCT and 22 continued conventional therapies. One of the delayed HSCT patients withdrew consent immediately after randomisation, 3 underwent surgery, one improved such that no intervention was required and 17 underwent HSCT (6 at an accelerated time point due to disease deterioration and 11 at one year as planned). Data one-year post HSCT is available for 21 of the early HSCT patients (one died 20 days after the start of conditioning and one withdrew from follow up at 26 weeks) and all 17 of the delayed HSCT patients.
ACKNOWLEDGEMENTS Financial and Material Support: The  Broad Medical Research Program Professor Hawkey 's  National Institute for Health Research Senior Investigator Award  The  University of Nottingham Medical School Dean's Fund The Nottingham University Hospitals NHS Trust Research & Development Fund Sponsors: European Group for Blood and Marrow Transplantation (EBMT) Autoimmune Diseases Working Party  and the  European Crohn's and Colitis Organisation (ECCO).