dataesr/scientific-paragraphs-categorization huggingface dataset:
6b ). The use of more intuitive headings, tool-tips, additional explanatory texts and improved predefined filtering options helped to increase the interpretability of AMLVaran's results. A major improvement was the introduction of the central overview of the mutation status for known driver mutations (Fig. 5 ). In this overview, both detection and exclusion of these mutations were represented as intuitive traffic light color codes. After these improvements, a second user study was carried out with a different user group of 14 experts, and updated tasks. Regarding the interpretation of hotspot mutations, 5 out of 6 test cases were now correctly interpreted by all 14 participants. The sixth test case was excluded due to ambiguity of the data. In addition to the improved performance, the second group of users also reported a higher degree of confidence while using the software during the interviews.
99 for hotspot regions) were reached for validated datasets, which should be generally sufficient for clinical use. However, the signal quality and the incidence of mutations per gene needs to be considered for a thorough interpretation. Considerable differences between AMLVaran and the appreci8 reference implementation were limited to dataset AML-1, and were largely caused by an improved normalization method and different versions of annotation databases. For updated versions of appreci8, we therefore expect smaller differences between this pipeline and AMLVaran. Normalization of the different callers' variant lists before combination, as well as the use of newer annotation databases, had a relevant influence on the outcome of the filtering process. In particular, the PM flag in the dbSNP database, which plays a crucial role in the appreci8 filtering scheme, is subjected to frequent changes, making the pipeline sensitive to database updates.
Although biological validation was lacking for the AML-1 dataset, we assume, that the pipeline optimizations led to improved precision in comparison to the original appreci8 output due to the apparent improvements in the newer dbSNP version. The deviations on dataset AML-1 show, that the output of individual callers and different pipelines can differ greatly, even for low-volume targeted sequencing data. We demonstrated, that existing variant calling pipelines are very sensitive to small adjustments of analysis parameters, an effect that is most notable in regions with lower data quality. It is therefore important, that these parameters are chosen based on a sufficient amount of valida-tion data for each dataset to account for differences in sequencing depth, differences in sample preparation, and other parameters of data generation.
This is especially important for the design of a complete variant calling pipeline, which combines several variant calling tools to achieve optimal results. In general, the performance of the presented pipeline can be improved through the incorporation of additional information regarding disease type and hotspot data. AMLVaran's default appreci8-like analysis pipeline takes such information into account. In our datasets, the sensitivity and precision of variant calling was higher for hotspots than on the whole target region. Even with dataset AML-1, 171 of 172 calls in hotspot areas were matching between both pipelines. This might be explained by the fact, that some regions of the genome are difficult to sequence in general and therefore the knowledge about these regions has limitations. For clinical use, not all detectable mutations are of the same relevance.
Strengths To the best of our knowledge, AMLVaran is the first freely available web-based software that combines a sophisticated but generic variant calling pipeline, a complex filter strategy, and disease-specific evaluation of predefined mutations in a form that is suitable for clinical application. The novel Scoring Scheme Definition Language (SSDL) allows a standardized, reproducible, cross-platform definition of complex variant filtering systems. Both, sensitivity and precision of the variant calling reach the specified goal of 0. 95 or higher for hotspot regions in all three evaluated datasets. The software not only supports confirmation, but also exclusion of predefined driver mutations by means of an integrated coverage analysis. This aspect is often lacking in common variant calling pipelines.
Since personalized medicine and genetic diagnostics is a moving target, frequent changes and updates of medical knowledge have to be taken into account to guarantee optimal variant filtering and interpretation. AMLVaran stores variants and annotation data separately. This flexible annotation approach enables to add, update or remove complete annotation databases at any time. There is no need to explicitly re-annotate previously analyzed samples after a database update. The databases stored on AMLVaran's server can also be used for independent real-time annotation without a full AMLVaran analysis  [32]. In addition to the functionality, usability of a software system is a key success factor with regards to its practical use. We therefore assessed the usability of AMLVaran with medical experts, and refined the software iteratively.
Transferability / universality AMLVaran is a generic application for NGS variant analysis, as it offers a freely customizable scoring scheme interface, which can be used to define a multitude of score calculation and filter schemes. Furthermore, it can be equipped with any variant calling tools, and different targeted sequencing panels are available for selection. Custom panels can be imported via the web interface as well. To adapt AMLVaran for other diseases, predefined driver mutation sites have to be changed and the rule system for treatment guidelines is to be adapted in the database. Although AMLVaran has been developed and optimized for targeted NGS sequencing, it may be used for wholeexome (WES) data as well. Some notes on recommended adjustments, as well as a comparison of runtimes and number of detections when used with a WES germline sample can be found in Additional file 1: Section 6.
Conclusions With AMLVaran, we have developed a software system that enables variant analysis on NGS sequencing data and fulfills central requirements for the utilization in clinical routine care. The presented software platform performs highly reliable variant calling, and achieves sensitivities of at least 0. 95 on several datasets, as well as precision scores of at least 0. 92, with a minimum precision of 0. 99 on hotspot regions. None of the eight variant callers was able to achieve such high values on the tested datasets, when applied alone. This underlines the need for validation of present NGS pipelines before use. AMLVaran is flexible enough to employ and combine arbitrary variant calling tools, and provides a user-friendly interface that has been evaluated in order to facilitate use by medical staff.
Availability and requirements Project Name: AMLVaran Project home page: https://amlvaran. uni-muenster. de [56]  Source code repository: https://github. com/cwuensch/ AMLVaran  [66]  Operating system(s): Linux Programming language: PHP, JavaScript, Python Other requirements: Apache web server, MySQL, Java 1. 8, Python 2. 7, VariantTools 2. 7. 0, samtools 1. 3, vcftools 0. 1. 15, SNPeff 4. 2, bam-readcount 0. 8. 0, reference genomes and the variant calling tools License: GNU GPL v3 Any restrictions to use by non-academics: none The presented web platform AMLVaran is available for demonstration purposes at https://amlvaran. uni-muenster. de [56]. The following credentials can be used to view the published test datasets: MDS-1: user: "test1", password: "Sweden2017". MDS-2: user: "test2", password: "Sweden2017".
AML-1: user: "test3", password: "Halle2015". (Upload and analysis of own samples is purely for research purposes and only possible after contact with the corresponding author. ) The source code is freely available from GitHub at https://github. com/cwuensch/AMLVaran [66] , and can be adapted for new projects under the provided license. A Docker installation script is available that guides a bioinformatician through the process of assembling and configuration of the software components. A QuickStart Guide that helps with setup and usage of the software is provided in Additional file 2.
Acknowledgements Jan Stenner ,  Sebastian Windau  and  Henning Ridder  helped with coding parts of the website. Sarah Sandmann , developed appreci8, on which our default score calculation scheme is based. Bo Peng , developed VariantTools, which is used for normalization and integration of the results of different caller tools, and gave a lot of support with usage. Carolin Walter helped with proof-reading and language editing of the manuscript. V. Gradenwitz ,  L. Möller ,  B. Wünsch  also helped with proof-reading the manuscript.
Methods A literature search was conducted in PubMed, EMBASE, Cochrane and ClinicalTrials. gov for studies published since 2000 that investigated the short-and long-term outcomes of regional treatments (surgical or non-surgical) for mCRC in patients aged �65 years. Pooled data analyses were conducted by calculating the risk ratio (RR), mean differences (MD) and hazard ratio (HR) between older and younger patients or between two different approaches in older patients.
Study design This is a systematic review and meta-analysis designed to describe and evaluate the outcomes of different regional treatments (i. e. , surgery, radiofrequency, cryotherapy, microwave ablation, electroporation, and radioembolization) in the case of treatable mCRC in older patients. The present report is structured according to recommendations by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines  [31, 32] , and the study protocol has been registered in the PROSPERO database (provisional registration number: 132956).
Search strategy Relevant human studies were identified up to March 2019 from the following online available databases: MEDLINE (through PubMed), EMBASE, Cochrane Library, and ClinicalTrials. gov register. For each database, a specific research equation was formulated using the following key words and/or MeSH terms: metastatic colorectal cancer, metastasis, elderly, older, age �65, age �70, age �80, surgery, surgical resection, liver resection, metastasectomy, radiofrequency ablation, radioembolization, local ablation treatment, cryotherapy, microwave ablation, electroporation, regional treatment. In addition, manual searches in the references sections of eligible studies and relevant review articles were crosschecked to identify additional records. The literature search was limited to the time frame January 2000 -March 2019, and only English literature was considered.
Data extraction and quality assessment The following variables were extracted from the selected articles and collected in an excel spreadsheet: authors, year of publication, journal, country, study time frame, study design, patient population, type of regional treatments, type of surgical procedure, intraoperative and postoperative outcomes, 90-day morbidity (overall postoperative complications; type and severity of postoperative complications according to Dindo-Clavien classification, including pulmonary complications, hepatic complications, and major complications) and 30-, 60-or 90-day mortality, survival (overall, disease-free, cancer-specific) at any reported time point (1, 2, 3, 5 years or more after the intervention). The risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) for case-control and cohort studies  [35].
Pooled data analyses Data from the included studies were used for a qualitative and quantitative synthesis according to the patients' age or the type of intervention. For binary outcomes, the risk ratio (RR) and 95% confidence intervals (CI) were estimated using the Mantel-Haenszel method. For continuous data, the mean differences and 95%CI were estimated using inverse variance weighting. Outcome measures were extracted as the mean (SD) or median (interquartile range) as provided. To calculate the mean values from the median, we applied the method described by Hozo et al. [36]. To compare OS and DFS between older and younger patient groups or between different types of interventions for mCRC, we calculated the hazard ratio (HR) and 95%CI as described by Tierney et al. [37]. Heterogeneity was assessed by the I 2 statistic, and values of 25%, 50%, and 75% were considered low, moderate, and high heterogeneity, respectively  [38, 39].
Literature search and selection Results of the literature review in the considered databases and the step-by-step study selection are shown in Fig 1. Overall, the combined search on the different databases identified 266 articles. After removing duplicates and non-pertinent studies upon title and abstract, 146 articles underwent a full-text evaluation. Of these, 124 were excluded because they were not pertinent to the review question. Finally, 29 articles were selected and eligible for pooled data analyses (availability of outcome data). The two reviewers had an optimal interexaminer agreement in the selection process (kappa: 0. 978).
NSD for survival rates. • Over 80 y. o. subgroup: NSD for survival and postop morbidity between LH and OH COPD = chronic obstructive pulmonary disease; CRLM = colorectal liver metastasis; CT = chemotherapy; MMT = multimodality treatments; NR = not reported; NSD = not significant difference; PSM = propensity score matching; SEER = Surveillance, Epidemiology and End Results database https://doi. org/10. 1371/journal. pone. 0230914. t003.
The paper is organized as follows. In Section 2, we describe the various data set-used in this study, the ARPEGE winds and the DYNED-Atlas eddy data base, and the CROCO ocean model used for our realistic numerical simulations of the Mediterranean Sea in 2015 and 2016. Section 3 presents the dynamical characteristics and the vertical structure of the robust coastal anticyclones which formed at the southeast tip of Crete during summer and fall 2015. Throughout comparisons are carried out between the regional model and remote sensing or in-situ observations. We then discuss, in Section 4, the impact of various forcing on the vertical extend of these coastal anticyclones. Finally, we sum up our results and conclude in Section 5.
Regional Wind forcing We used the ARPEGE data-set to provide the most realistic wind-forcing for our regional simulations of the Mediterranean Sea. This data-set is based on 4-D variational assimilation of wind observations into the Meteo-France system of Forecast and Analysis ARPEGE. This reanalysis provides the atmospheric fields at high spatial (1/10) • and temporal (hourly 1 h) resolution. To test the accuracy of the ARPEGE data-set in the Crete area and especially in the Kasos strait we collected regional wind speed data from three Meteorological stations of the Hellenic National Meteorological Service (HNMS) located on the islands of Crete, Kasos and Karpathos at heights 15 m, 17 m and 114 m respectively. We first build the time series of the mean wind speed in the Kasos strait (inside the black circle of Figure  1(a) ) and compare the temporal variability with the in-situ data of (HNMS).
We found that the synoptic variability of the ARPEGE data in 2015 and 2016 is in good agreement with the local observations (see Figure  1  for summer 2015). However, if we compare the wind intensities we could find some local discrepancies. There is a correct agreement with Sitia weather station, which is located at the southeast of Crete, but a slight overestimation is found with the Karpathos station and an underestimation with the Kasos station. Hence, if the main components of the synoptic wind variability in the Kasos area are accurate in the ARPEGE data-set the local intensities of the surface winds, which are strongly impacted by the complex orography of Crete, should always be taken with care. Nevertheless, as far as we know, this is the best wind data-set available at high resolution for this specific area in 2015 and 2016.
Eddy database DYNEDAtlas In order to compare the mesoscale eddies formed in the southeast of Crete in the regional simulation CROCO-MED60v40-2015 with both remote sensing and in-situ observations, we used the dynamical eddy data-base DYNED-Atlas (https://www. lmd. polytechnique. fr/dyned/). This recent database provides 17 years  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017)  of eddy detection and tracking in the Mediterranean Sea along with the co-localisation of Argo floats for each detected eddy (https://doi. org/10. 14768/2019130201. 2). The dynamical characteristics of the eddies contained in the DYNED-Atlas database were computed by the AMEDA eddy detection algorithm (Le  Vu et al. , 2018)  applied on daily surface velocity fields.
It can therefore be expected that the long-lived Ierapetra anticyclone (IE2015) will form in July or early August this year. However, this was not the case. Indeed, a long-lived anticyclone that survives more than six months was formed in late September in the numerical simulation CROCO-MED60v40-2015 while a similar eddy was detected in early October in the DYNED-Atlas database. Such long-lived and robust anticyclone, which is formed in the Southeast of Crete is usually called a Ierapetra anticyclone and will be labeled IE15 in what follows. Nevertheless, according to CROCO-MED60v40-2015, several other anticyclones were formed in the same area during summer 2015. These coastal anticyclones (labeled AE1, AE2 and AE3) were formed the 9 of June, the 14 of August and the 12 of September respectively (Figure  4 ).
Dynamical characteristics and trajectories The Figure  5  compares the temporal evolution of the characteristic radius (R max ) and the intensity (V max ) of the long-lived IE15 formed in CROCO-MED60v40-2015 with the IE2015 detected in DYNED-Atlas. These two mesoscale anticyclones were formed mid-fall at the end of September or early October, respectively. Since the spatial resolution of the numerical model (1/60 • ) is seven times greater than that of merged altimetry products (1/8 • ), it makes sense that the initial formation of such coastal eddy is better detected in the regional simulation CROCO-MED60v40-2015. If we assume that the model simulates correctly the IE formation, the AMEDA algorithm will detect it earlier in the regional model than in the coarse AVISO/CMEMS data set. In both cases, the radius of the IE15 and the IE2015 exceeds the local deformation radius (R d = 10 -12 km) by at least a factor three (Figure  5 B ).
Such large radius is in good agreement with previous observations of Ierapetra Eddies  (Hamad et al. , 2006; Matteoda and Glenn, 1996; Taupier-Letage, 2008; Mkhinini et al. , 2014; Ioannou et al. , 2017 Ioannou et al. , , 2019)). However, the eddy intensity seems to reach higher values in the numerical model than in the eddy database. The maximal azimuthal velocity V max could reach up to 70 cm/s in the CROCO-MED60v40-2015 while it never exceeds 40 cm/s in the DYNED-Atlas data base (Figure  5A ). The underestimation of the IE's intensity in AVISO/CMEMS products, in comparison with in-situ measurements, was previously documented in  Ioannou et al. (2017)  and typical velocity values of 60 cm/s were confirmed by VMADCP measurements for IE eddies  (Ioannou et al. , 2017 (Ioannou et al. , , 2019)).
The trajectory of the simulated IE15 and the observed one also differs even if both of them quickly propagate offshore 60 km south of the Kasos strait (Figure  5C ). As in the numerical model, a shorter-lived coastal anticyclone, that remained close to the shore in the southeast of Crete, was also detected the 29 of July according to the DYNED Atlas data-base. Such coastal anticyclone was detected from altimetry despite its decreased accuracy near the coast. The formation and the location of the short-lived anticyclone was also confirmed by a careful analysis of SST images. Hence, both remote sensing data sets, visible images and altimetry maps, show that coastal anticyclones could be formed in this area earlier during the summer months. We compare in the Figure  6 , the dynamical characteristics of this coastal anticyclone detected in the DYNED-Atlas with the three structures formed by the regional simulation in June, August and early September.
SUMMARY AND CONCLUSIONS Using high-resolution (1/60 • ) regional model CROCO-MED60v40, we analyzed the formation of a coastal eddy in the southeastern wake of Crete island. It is in this area that an intense, large -scale and long-lived anticyclone is formed almost every year, commonly known as the Ierapetra eddy  (Hamad et al. , 2005 (Hamad et al. , , 2006;; Taupier-Letage, 2008; Amitai et al. , 2010; Menna et al. , 2012; Mkhinini et al. , 2014; Ioannou et al. , 2017). Our previous studies  (Mkhinini et al. , 2014; Ioannou et al. , 2020)  have confirmed that the intensity of the summer wind jets (i. e. Etesian winds) blowing through the Kasos strait is one of the main reasons for the formation of robust anticyclones in this area. Motivated by the fact that the regional model CROCO-MED60v40 was driven by the ARPEGE hourly wind reanalysis (at 1/10 • ), which are the most accurate wind dataset in this region, we study the relation between the wind forcing and the dynamical characteristics of the wind-induced eddies.
More specifically, we focus on the year 2015 where an Ierapetra eddy was formed both in the observational as well as in the numerical fields. During that year, Argo profilers were trapped for several months (October 2015 -April 2016) in the core of the Ierapetra anticyclone, allowing us to compare the evolution of the vertical structure of the anticyclone from the in-situ data with the numerical outputs. Even without in-situ data assimilation, the numerical model was able to reproduce the formation and the dynamical evolution of a long-lived and robust anticyclone (IE15) similar to the Ierapetra eddy (IE2015) that was detected this specific year. The IE15 was formed at the end of September while the IE2015 was detected in early November according to the DYNED-Atlas eddy data-base. The temporal evolution of the characteristic radius and the vertical extend of the IE15 are also very close to the observations.
However, the trajectories of these two eddies diverge after a few weeks and the eddy intensity reach higher values in the numerical model than in the eddy database. The latter could be due to a systematic under-evaluation of the eddy amplitude when we use the sea surface height or the surface velocity field derived from altimetry data-set. Indeed, similar under-evaluation in comparison with local VMADCP measurements were found by  (Ioannou et al. , 2017 (Ioannou et al. , , 2019)). More surprisingly, according to CROCO-MED60v40-2015, several other anticyclones were formed in the same area during summer 2015 when the Etesian winds reach strong values. Three of them survive more than two months but their trajectories differ from the IE15. These coastal anticyclones travel along the Crete shelf while the Ierapetra eddy propagates offshore after its formation.
A careful analysis of the DYNED-Atlas eddy data base reveals that a similar coastal anticyclone was also detected, during August and September 2015, on the standard AVISO/CMEMS Mediterranean altimetry data. Hence, such coastal anticyclones that propagate along the south coast of Crete are both present in the model and the observations. Thus, even if it does not exactly reproduce the observed ocean circulation (since there is no data assimilation), the regional simulation CROCO-MED60v40-2015 seems to provide a realistic description of the formation and the evolution of coastal eddies in the south of Crete island. Therefore, we can rely on this high-resolution model to study the impact of the orographic wind forcing on the formation and the subsequent evolution of realistic coastal anticyclones.