dataesr/scientific-paragraphs-categorization huggingface dataset:
Outcomes Analysis The primary objectives of this meta-analysis study were to describe treatment-related complications (the safety of the treatment), the technical success rate, and occlusion during follow-up (efficacy of the treatment) after FD for nonsaccular aneurysms. The influence of aneurysm size, patient age, and treatment characteristics on the analyzed outcomes was also studied. This objective was achieved by performing a preplanned subgroups analysis as well as univariate and multivariate analyses on the available individual patient data. Individual patient data were extracted from each series (tables or on-line tables) when reported by the authors.
To verify the consistency of the meta-analysis outcome, we assessed the influence of each individual study by a sensitivity analysis ("leave-oneout" approach). Differences between subgroups of analyses were considered significant at P ,. 05. Meta-analysis was performed with ProMeta-2 (Internovi, Cesena, Italy) and OpenMeta [Analyst] (http://www. cebm. brown. edu/openmeta/). Univariate and Multivariate Analysis. From the individual patient data, we extracted the following dependent variables: aneurysm occlusion (adequate-versus-incomplete occlusion) and treatment-related complications (complications versus no complications). Individual patient data were selected from patients with nonsaccular aneurysms. The x 2 test was used to evaluate qualitative factors associated with occlusion and complications (aneurysm location and size, type of stent, patient age, type of aneurysm).
The independent variables significantly associated (P #. 1 in the univariate analysis) with aneurysm complete occlusion or complications were analyzed together in a binary logistic regression (multivariate analysis) to assess the independent contribution of each factor. The results of the regression model were calculated by the Wald test and expressed using P values and the related odds ratios. All statistical analyses were performed with SPSS, Version 24 (IBM, Armonk, New York).
METHODS The Covid-19 ICU patient turnover was calculated using a recurrence relation that amounts to the sum between the number of existing patients and the number of daily admissions from which was subtracted the number of patients discharged from the ICU, dead or alive. Number of daily admissions in the ICU: the number of daily admissions was calculated as the sum of a ratio k d of the new Covid-19 cases detected on a given day D0 and a ratio k w of the average of the ICU length of stay: The length of stay of the Covid-19 patients in the ICU depends on the severity of those patients' condition, which could be stratified as a function of the ventilation mode. Indeed, following internal data from Hospices Civils de Lyon (4), approximately 39. 7% of patients admitted to the ICU were non-intubated (nasal cannula O2, non-invasive ventilation e.
Methods Standard protocol approvals, registrations, and patient consents Details on the registry (BEYOND-SWIFT [Registry for Evaluating Outcome of Acute Ischemic Stroke Patients Treated With Mechanical Thrombectomy]) are registered at NCT03496064 and further details were published previously. 9 Patient consent was obtained according to Good Clinical Practice or institutional review board or local or institutional policies. Briefly, this multicenter, retrospective, international, nonrandomized observational registry was designed to investigate the safety and efficacy of a second-generation market-released neurothrombectomy device in borderline indications for treatment of AIS. In the 2007 period (center Munich), this included treatment with a Solitaire AB used as retrievable stent. Medtronic provided financial funding of the registry.
Inclusion criteria of the registry were treatment with a Medtronic market-released thrombectomy device for an intracranial large vessel occlusion with attributable neurologic symptoms. Current participation in another clinical trial was the only exclusion criterion. For this analysis, we included all patients from the registry with available information on initial imaging modality. Between 26% and 90% of all patients treated by MT at each center during the study timeframe were included in this registry. Choice of imaging modality was center-specific and not specified. Four centers used mainly CT, 2 centers mainly MRI, and the largest center used CT and MRI in equal parts (figure e-1, doi. org/ 10. 5061/dryad. f4qrfj6t1). Reasons why patients underwent CT or MRI were not available as a data item in the registry, but were provided by the local principal investigator for the study timeframe (table 1, Dryad, doi.
org/10. 5061/dryad. f4qrfj6t1). Local principal investigators also provided their approach for patient selection in the study timeframe (table 1, Dryad, doi. org/10. 5061/dryad. f4qrfj6t1) and all centers that had access to both modalities based their decisions on a tissue-based approach with somewhat similar indications to perform MT. Cerebral microbleeds were not an absolute contraindication for IV thrombolysis or (IVT) endovascular treatment (EVT) in the centers that used MRI as initial imaging modality. A clear-cut intracranial hemorrhage excluded both IVT and EVT in all centers. Indications for MRI as opposed to CT included favorable prognostic features (low NIHSS, no contraindications such as vomiting or pacemakers) as well as unfavorable prognostic features (posterior circulation large vessel occlusion including basilar artery occlusion, intubated patients, and unknown onset time).
Statistical analysis As patients not undergoing MT were not collected in BEYOND-SWIFT, we sought to address the issue of which parameters were associated with undergoing MT according to each imaging modality in the local database (2015-2018) from the largest participating center (Bern). For this purpose, patients who had a vessel occlusion in the suspected ischemic territory presenting between 0 and 48 hours after symptom onset were compared with univariable analysis as outlined above. A backward stepwise multiple logistic regression analysis was performed to identify factors associated with performing subsequent MT. Then, age, sex, and the imaging modality was added to the model to analyze whether the imaging modality itself was associated with MT decisions. In the BEYOND-SWIFT dataset, we compared the imaging groups (MRI vs CT) using χ 2 and Fisher exact tests for categorical variables, Mann-Whitney U test for non-normally continuous or ordinally scaled variables, and Welsch t test for independent normally distributed data.
The rationale for both models was the  combination of known predictors of FR following EVT  2, 5  and baseline and interventional differences of patients with and without FR factoring in the (multi)collinearity between variables (table 2, Dryad, doi. org/10. 5061/dryad. f4qrfj6t1). In a post hoc analysis, an ordinal shift analysis was applied using the same covariates and factors as stated above. We excluded patients with missing data items from the multivariate analysis. For the sensitivity analysis, the same model was used to assess the association of imaging modality with FR defined as mRS 5-6 at 90 days, despite successful recanalization. We used a level of significance of 0. 05. All analysis were performed with SPSS version 25.
Subgroup analyses Our main finding of an increased risk of FR in patients selected by CT were consistent throughout subgroups according to centers, anterior as compared to posterior circulation (p for interaction = 0. 862), occlusion site, and low and high ASPECTS score (figure  3  and table 3, Dryad, doi. org/10. 5061/dryad. f4qrfj6t1). When restricting the analysis to patients with known time of symptom onset, the increased risk of FR in patients selected by CT was robust also in patients presenting 0-6 hours after known symptom onset (aOR 1. 757, 95% CI 1. 168-2. 644) and the point estimate suggested an ever more pronounced association for patients presenting beyond 6 hours (aOR 24. 6, 95% CI 0. 557-1. 087), without reaching significance in this small subgroup. In patients with known symptom onset, there was no interaction of imaging type and presentation within 6 hours (p = 0.
The roughly 30-minute delay from admission to groin puncture in MRI-selected patients fits well with the reported data in real-world patients  18  and underscores the importance to shorten in-hospital delays for MRI patients. However, MRI patients were less severely affected and hence had less clear indications for MT, like more distal occlusions. In our dataset, there was a nonsignificant trend for sICH occurring less frequently in patients selected with MRI. A lower percentage of MRI-selected patients received IVT before MT, a finding that was nonsignificant in our registry, but significant in another recent observational study. 18 fter adjusting for possible confounders including the lower rate of IVT in MRI-selected patients, this association was nonsignificant in our registry, but remained significant in the study by Kim et al.
Hence, our results need to be replicated by other groups and verified by upcoming RCTs on this issue (IMAGECAT [NCT03745391]). Patient selection for MT by CT was associated with an increased risk of FR compared to MRI selection. Further research is needed to clarify the role of the initial imaging modality on FR occurrence and to develop a reliable FR prediction algorithm that could be included into shared decision-making and elucidation of patient preferences. Efforts are needed to shorten workflow delays in MRI patients. If confirmed in upcoming RCTs, cost-effectiveness analyses comparing CT with MRI as the best initial imaging modality for MT seem warranted due to the ambivalence between MRI-related costs and those associated with FR.
(IMAGECAT [Multimodal Neuroimaging in the Selection of Acute Ischemic Stroke (AIS) Patients to Endovascular Treatment (EVT)] [NCT03745391]). However, the main aim of this study was to sensitize stroke physicians that apparently the imaging modality influences their decisions regarding which patients to treat by MT. Whether this results in an overall better, worse, or equal outcome can only be judged by upcoming RCTs on this issue (IMAGECAT [NCT03745391]).
COVID-19 has become a global pandemic not long after its inception in late 2019. SARS-CoV-2 genomes are being sequenced and shared on public repositories at a fast pace. To keep up with these updates, scientists need to frequently refresh and reclean datasets, which is ad hoc and labor-intensive. Further, scientists with limited bioinformatics or programming knowledge may find it difficult to analyze SARS-CoV-2 genomes. In order to address these challenges, we developed CoV-Seq, a webserver to enable simple and rapid analysis of SARS-CoV-2 genomes. Given a new sequence, CoV-Seq automatically predicts gene boundaries and identifies genetic variants, which are presented in an interactive genome visualizer and are downloadable for further analysis. A command-line interface is also available for highthroughput processing.
Introduction The 2019 Novel Coronavirus (SARS-CoV-2) caused an outbreak of viral pneumonia since late 2019 and has become a global pandemic. Despite efforts to contain its spread, SARS-CoV-2 has infected more than 4 millions patients and caused nearly 300,000 deaths worldwide as of early May  (Dong et al. , 2020). To understand its evolution and genetics, scientists have sequenced SARS-CoV-2 genomes from patients across different age groups, genders, ethnicities, locations, and disease stages  (Hadfield et al. , 2018). These genomic sequences are being shared on public repositories at a rapid pace, with thousands of new sequences every week  (Elbe and Buckland-Merrett, 2017; Brister et al. , 2015; Kanz et al. , 2005; Wei, 2019). To keep up with the latest developments, scientists need to frequently download and clean new datasets, which is ad hoc and time-consuming.
Analysis and Visualization of Viral Genomes The collection of SARS-CoV-2 genomic sequences is rapidly expanding. An integrated pipeline is essential for keeping current with frequent updates. To understand the evolution and genetics of a new viral strain, it is necessary to identify its genetic mutations and gene boundaries. Existing software packages such as VAPiD  (Shean et al. , 2019)  and VIGOR  (Wang et al. , 2010)  focus on gene annotation. To our knowledge, a software package to identify, annotate, and visualize genetic variants for SARS-CoV-2 is lacking. CoV-Seq provides an intuitive web interface for analyzing and visualizing SARS-CoV-2 variants and ORFs. Upon receiving custom sequences, CoV-Seq identifies gene boundaries and genetic variants and displays them alongside a full-length genome (Figure  1B ).
Users interact with the genome by dragging the zoom bar to adjust the magnification and the position bar to pan along the genome. When the genome window contains less than 150 nucleotides, letters will appear to indicate both the nucleotide bases and amino acid residues. Hovering over gene bodies or variants will trigger pop-up windows for relevant information (Figure  1B  red box). Clicking on an ORF brings up three tables. A first table shows all variants and their positions, alleles, and ORFs in which they belong (Figure  1C ). A second table shows ORF annotations obtained by aligning input sequences against the reference sequence and transferring annotations from Genbank (Figure  1D ). A third table shows nucleotide and protein sequences for the selected ORF. All tables can be downloaded for further analysis.
SARS-CoV-2 genome processing pipeline Extraction of genetic variants from sequences involves the following steps (Figure  1A ): (1) preprocessing: removal of low-quality and duplicated sequences; (2) alignment: pairwise alignment of each sequence against a reference; (3) variant calling: identify differences between aligned sequences; and (4) postprocessing: remove low-quality sites and annotate variants. We aggregate SARS-CoV-2 sequences from GISAID, NCBI, EMBL and CNGB. Many sequences represent incomplete genomes, sometimes containing only a single gene. We filter these genomes using a lenient cutoff of 25,000 nucleotide because it removes distinctly incomplete genomes while retains complete genomes (Figure  S1 ). Both NCBI and EMBL are part of the International Nucleotide Sequence Database Collaboration (INSDC) and therefore contain duplicate submissions, which we remove by comparing the Accession IDs.
Further, dual submissions can appear in both GISAID and INSDC under different Accession IDs. We consider two submissions as suspect duplications if they have identical genomic sequences. These suspect duplications are marked in the metadata but not removed because it is possible for an identical strain to infect multiple patients. We perform pairwise alignment against the reference sequence NC_045512. 2 using MAFFT  (Katoh and Standley, 2013)  and use a custom Python script for variant calling. We left-normalize each variant with bcftools  (Li, 2011)  and remove samples with too many variants indicative of sequencing error. We use a lenient cutoff of 150 variants because it removes samples with extremely large numbers of variants while keeping most samples (Figure  S2 ). During postprocessing, we remove multi-allelic sites because these sites are more likely to occur in regions prone to sequencing error, such as the two ends of the genome (Figure  S3 ).
Further, we remove variants within the poly-A tail. Filtered variant callset is annotated with snpEff  (Cingolani et al. , 2012). Both raw and filtered VCF files are available for download on CoV-Seq (http: //covseq. baidu. com/browse) and the pipeline is open source at https://github. com/boxiangliu/covseq. We are committed to update VCF files and associated metadata weekly on Mondays.
Methods Further information can be found in the Supplemental Methods. μ-CT. Following euthanasia, crania of WT and T58I mice at 8 months were imaged using the Scanco μCT 50 scanner (Scanco Medical AG) with 17. 2 μm voxel size and X-ray energies of 55 kVp and 109 μA. Images were reconstructed and converted to 3D volumes (μCT Evaluation Program V6. 5-3) and reference landmarks according to Vora et al. (49)  were placed manually on the rendered data using the Avizo-lite 3D analysis software. All landmarks were digitized by the same investigator for a total of 18 landmarks (see Supplemental Figure  3 ). Pathologic examination, tissue processing, and staining. Mice were either euthanized at a specific age or observed for signs of disease and were killed when moribund. Cardiac blood was obtained in Microvet EDTA tubes (BD Biosciences) for complete blood cell counts using a Hemavet 850FS (Drew Scientific), and BM cytospins were prepared using a Shandon Cytospin 3 Cytocentrifuge (Thermo Fisher Scientific).
Blood smears and BM cytospins were stained with Wright-Giemsa Stain (Fisher Healthcare PROTOCOL) following standard protocols. Images were taken on a Nikon Eclipse 80i microscope with a Nikon Digital Sight camera using NIS-Elements F2. 30 software at a resolution of 2560 × 1920 pixels. Using Adobe Photoshop CS2, image quality was improved with unsharp mask, and images were resized and set at a resolution of 300 pixels/inch. Organs were collected in Z-fix (Anatech Ltd). Mouse sternums were decalcified using Cal-Rite (Richard-Allan Scientific). Tissue sections of fixed solid organs were embedded in paraffin and sectioned at 4 μm thickness and processed following standard procedures for the sectioning of paraffin blocks. H&E stainings were processed on the Leica Autostainer XL according to manufacturer's instructions.
The rest of the whole cell lysates (at least 250 μg) was used to detect Raf1 binding levels using the Active Ras Pull-Down and Detection Kit (Thermo Scientific) according to the manufacturer's protocols. Quantitation was performed using the ImageLab (Bio-Rad) software. Heart valve surface area quantitation. Leaflet area was obtained from H&E-stained sections. Area of freehand outline valve leaflets from at least 8 sections of 3 fetuses was measured with Measurements tool (Fiji Software). Mice. The mice described in this report are available through the Jackson Laboratory Repository. The relevant identifiers are as follows: JAX035731 (LSL-T58I) and JAX035732 (LSL-P34R). Statistics. Statistical analyses were carried out using Prism 7 (GraphPad). Data are presented as mean ± SEM unless stated otherwise.
Acknowledgments We thank members of the  Shannon lab  for helpful discussions and for providing advice. We thank  X. Zou  for assistance with blastocyst injection to generate the Kras LSL-P34R/+ and Kras LSL-T58I/+ strains. We also thank  T. Huynh  from the  MicroPET/CT, MicroSPECT/CT, Optical Imaging Core Facility  and  Nicholas Szeto  from the  Skeletal Biology and Biomechanics Core  for their expertise in and technical support of μ-CT analysis. We thank  Jennifer Bolen  from the  Biorepository  and  Tissue Biomarker Technology Core  for helpful advice on and technical support of tissue processing and histological detection. This work was supported by  NIH/National Cancer Institute  grants  R37 CA72614  and  U54CA196519  (to KS),  R50 CA211452  (to JCW),  R35-DE026602  (to ODK), and  K08-DE026219  (to AS) and  NIH/National Cancer Center Support  Grant  P30 CA082103  to the  Helen Diller Family Comprehensive Cancer Center  at the  University of California, San Francisco.
KS is an  American Cancer Society Research Professor. JGB is funded by  Programa de Atracción de Talento from Comunidad de Madrid. PC is a fellow of the  Jane Coffin Childs Memorial Fund. KH is a fellow of the  Japan Society for the Promotion of Science Overseas Research Fellowships. Address correspondence to:  Kevin Shannon ,  Department of Pediatrics, University of California, San Francisco ,  Helen Diller Family Cancer Research Building , 1450 3rd Street,  Room 263 ,  San Francisco , California 94158,  USA. Phone: 415. 476. 7932;Email: kevin. shannon@ucsf. edu. AS's present address is:  Institute of Dental Sciences, Faculty of Dental Medicine, Hebrew University , Jerusalem, Israel. insight. jci. orghttps://doi. org/10. 1172/jci. insight. 140495.
Giraud et al. [2002]  developed the computer program Crocus_PC, which simulates the snowpack evolution initialized by a profile fully described in terms of the Crocus state variables and a time series of meteorological forcing. Snow depth data can be used to drive the model Snowpack  [Bartelt and Lehning, 2002]. Charrois et al. [2016]  and  Larue et al. [2018]  showed the potential of assimilating snow surface reflectance into Crocus. Magnusson et al. [2014]  introduced a method to assimilate measured water equivalent of snow cover or bulk snow density. Piazzi et al. [2018]  and  Smyth et al. [2019]  assimilate other sources of data, such as snow surface temperature or surface albedo. Even if obvious deviations between simulated and observed profiles are noticed, there is currently no method to correct the snow simulation during the winter season from detailed observed stratigraphy.
Results The result of our method are first illustrated on one season and one site, before being applied to all the dataset for quantitative evaluation. In particular, we investigate improvements permitted by the direct insertion, their time persistence, the interest of direct insertion depending on model error and the influence of the set of variables chosen for insertion. For illustration, we provide results using our method to a single site, Col de Porte, during the winter season 2003-2004 (Figure  4 ). Here we show only the grain shape and density profiles. Figures  4a  and 4d  show all snowpack observations available at this site on the considered period (LOBS). An observation is reported approximately every week between mid-December and early April. The observations are here extended up to the next observation as they are the only data available about the snowpack during this period of time.
Discussion and conclusion Detailed snow cover models are commonly evaluated on surface or bulk variables, whereas they are designed to represent the detailed stratigraphy. Moreover, models used in support to operational avalanche hazard forecasting are commonly driven with meteorological data without use of snowpack observations during a whole season, so errors accumulate and the simulation increasingly deviates from observations. We contribute in this paper to reduce these two limitations. A method for evaluating detailed snow cover models with respect to snow pit observations is introduced and direct insertion of observations into snowpack modelling is implemented and used to analyze some key features of snow cover dynamics.
Direct insertion We showed that direct insertion of snow observations enables to correct snow cover simulations. The improvements provided by the insertion depended on the time between the insertion and the evaluation. Its typical time persistence was around one week. A perfect agreement between the model and the observation could not be reached immediately after insertion (Figure  7 ). This discrepancy was partly due to the absence of some grain shapes in Crocus code and the fact that some observed profiles are not numerically stable according to Crocus empirical parameterizations. On the considered sites and time period, the median error of the simulation decreased to 6. 8 cm for snow depth, 39 kg m -3 for density and 0. 25 for grain shape, one week after the direct insertion of a full manual profile (Figure  7 ).
The variables used for the direct insertion were chosen on the basis of available information from conventional snowpack observations. In general, we used all data available from these observations to re-initialize the model. We showed that this could be adapted to data available, even with more simple observations which are easier to collect. We evaluated the impact of the choice of the inserted property on the simulation improvements (Figure  9 ). For instance, using only snow depth with adjusted layer boundaries (thickness) enabled to reduce the simulation error one week after the insertion, but mainly on the snow depth, with a limited cross impact on the other properties (Figure  9 ).