dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Erik Berndtsson  for providing continuous advice about the design and assistance in the implementation of the protocol in Sweden. We also thank  Veronika Lyckow ,  Katarzyna Trok ,  Marika Augutis , the  Board  and the peer mentors of  RG Active Rehabilitation  for being valuable partners in this initiative. We thank professor  Thórarinn Sveinsson  and  Magdalena Lewandowska  for assistance in determining the statistical methods for analysing the data. We also thank  Kenneth Chance-Larsen  for language editing and proofreading assistance. We would also like to acknowledge people involved in the translation of outcome measures. In Sweden, this group consisted of  Professor Richard Levi (MD, PhD ),  Emelie Butler  (PT, PhD),  Gunilla Lyckow Windahl  (professional English translator),  Ulrica Lundström  (OT, PhD),  Erika Nilsson  (employee at the Spinalis Foundation, person with tetraplegia) and  Erik Berndtsson  (peer mentor, worker at RG Active Rehabilitation, person with paraplegia).
Generating the initial structure for MD simulations Native TIA-1 has three RNA recognition motifs, RRM1, RRM2, and RRM3, connected by flexible linkers and followed by a C-terminal unstructured Q-rich domain of ~100 residues. In this study, we investigated a truncated construct of TIA-1 without the Q-rich domain  [24] , and will in the following refer to this truncated construct simply as TIA-1. As starting point for our models, we used previously determined high-resolution structures of the three folded domains. The structure of RRM1 was determined by solution state NMR spectroscopy  [24]  (PDB 5O2V), the structure of RRM2 was determined by X-ray crystallography  [24]  (PDB: 5O3J), and the structure of the RRM2-RRM3 complex (RRM23) was determined by solution state NMR spectroscopy  [25]  (PDB: 2MJN).
Setting up the MD simulations We used the coarse-grained force field Martini version 3. 0. beta. 4. 17  [21, 22]  in combination with GROMACS 5. 1. 4 or 2016. 5  [27]. First, the structure was coarse-grained using the Martinize2 python script  [28]  (Fig  1A ). An elastic network  [29]  was then added to the folded domains, to make them semi-rigid. Specifically, a harmonic potential with a force constant of 500 kJ mol -1 nm -2 was applied to all backbone (BB) bead pairs with relative distance less than 0. 9 nm. No elastic network was added to the flexible linkers or to contacts between the three folded domains, so that we only applied restraints within RRM1 (residues 7-80), RRM2 (residues 95-170) and RRM3 (residues 204-270). The coarse-grained structure with the elastic network was then relaxed for 3 ps, with a time step of 30 fs, using the Berendsen thermostat and barostat, and Verlet cutoff scheme.
Therefore, for each ensemble (set of conformations and associated weights) we estimated a single set of global values for these parameters using the following algorithm: 1. Each frame was fitted individually, with the four parameters free. 2. Trajectories were reweighted using BME (see details below) using a range of θ-values from 1 to 500. This resulted in a set of weights for each value of θ: {w θ } = {w θ,1 ,w θ,2 ,. ,w θ,N ), where N is the number of frames. 3. Weighted averaged parameter values were calculated using: hpi w ¼ X i w y;i � p i ; where p is either I(0), B, r 0 , or Δρ, and i runs over all frames. 4. The scattering was calculated again for each frame using Pepsi-SAXS/SANS, with the parameters fixed to the weighted average. This resulted in a reduced χ 2 for each θ: w 2 r;y ¼ 1 M À 2 X j S � hI y ðq j Þi þ B À I exp ðq j Þ s j ! 2 ; where M is the number of data points, and I θ (q j ) is the weighted average of the intensities: hI y ðq j Þi ¼ X i w y;i � I calc;i ðq j Þ: The scale parameter, S, and the constant background, B, were refitted for each θ to minimize w 2 r;y.
Tuning protein-water interaction strength in the Martini model As described in more detail in the results section, we find that simulations using the unperturbed Martini force field yielded structures that were too compact and thus did not fit the experimental SAXS and SANS data. Several atomistic force fields have likewise failed to describe flexible and disordered proteins, but increasing the protein-water interaction strength has in several cases been shown to improve the fit to experimental data  [36] [37] [38]. Inspired by this work and a previous modification to Martini force field v. 2. 2  [39]  we examined whether a similar solution could be applied here, and thus varied (increased) the protein-water interaction strength. Specifically, we adjusted the interactions between protein and water by multiplying the � parameter in the Lennard-Jones potential between water beads and protein beads by a factor λ  [21]  that we varied between 1.
SAXS and SANS data We used one SAXS dataset recorded on non-deuterated TIA-1 and three different SANS datasets with RRM1 fully deuterated (dRRM1) and RRM23 non-deuterated (hRRM23), with SANS obtained in 0%, 42% and 70% D 2 O leading to different contrasts of TIA-1 in solution (Fig  1B ). All data were collected by Sonntag et al. [24]  and obtained from the authors. Varying the deuteration of the solvent gives unique contrast situations in SANS: In 0% D 2 O both dRRM1 and hRRM23 have positive contrast but with an inhomogeneous internal contrast due to the higher excess scattering length density, Δρ, of dRRM1; in 42% D 2 O dRRM1 has positive contrast and hRRM23 has approximately zero contrast; and in 70% D 2 O hRRM1 has a negative contrast, and dRRM23 has a positive contrast of similar magnitude.
Pair distance distribution functions We determined the pair distance distribution functions, p(r) (Fig 1C ), using Bayesian Indirect Fourier Transformation (BIFT) as implemented in BayesApp  [40, 41]  (available via GenApp  [42] ). A constant background parameter was used in the transformation, and the distributions were allowed to take negative values. This was necessary in particular for the SANS dataset measured in 70% D 2 O, as the deuterated domains have positive excess scattering length density (contrast) and the hydrogenated domains have negative excess scattering length density. Such alternating contrasts result in negative values for the p(r) at distances typical for the distance between these domains.
Combining MD simulations and SAS data by Bayesian reweighting As mentioned in the introduction, there are several ways to combine MD simulations with SAS data  [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] , but we here used the Bayesian/Maximum Entropy (BME) method  [3]  and the above-calculated SAXS and SANS intensities to reweight the trajectories. For details of BME see  [3]  as well as code and examples online https://github. com/KULL-Centre/BME. We refer to recent reviews for an discussion on alternative methods  [5, 6, 43]. The first step in any reweighting approach is to run the MD simulation and compare the average calculated intensity from all frames to the experimental data without any further change of the MD simulations. Discrepancies between calculated scattering and data may arise from insufficient sampling or approximate "forward models" used to calculate scattering data from conformations.
g. the structure of RRM2 is essentially the same whether in the complex of RNA (PDB ID 5O3J) or in the context of RRM2-RRM3 (PDB: 2MJN). We performed a 10 μs long MD simulation of TIA1, and examined the consistency between simulation and experiments by comparing the calculated scattering intensity (averaged over all structures in the MD trajectory) with the experimental SAXS data. From this, we observed clear discrepancies, as evident from visual inspection of fit and residuals (Fig  2A ). We found that most of the simulated structures had R g values below the experimentally determined value (Fig 2C ) and, that the simulation predicted that TIA-1 is mostly in a collapsed state with R g of around 20 Å, but with occasional expansions of the three-domain structure, resulting in spikes in the plot of R g values.
Such a compact ensemble is clearly in disagreement with the SAXS data. This observation indicates that the current parameterization of the Martini force field causes too compact structures for the flexible protein TIA-1. We speculate that this may be ascribed to the protein-protein interactions between domains of TIA-1 being too attractive, as such protein "stickiness" has previously been observed for simulations in Martini v2. 2  [39, [49] [50] [51] , and Martini v. 3. 0. beta. 3. 2  [3]. We considered other reasons for the poor fits, including the fact that we keep domains fixed with elastic networks, and limited accuracy of the calculated SAXS data, but none of these could easily explain the large discrepancy between data and calculated scattering from the unperturbed ensemble. To improve agreement between simulation and experiment we used the BME reweighting protocol in which the weight of each conformation in the ensemble is modified to improve agreement.
Fitting the data twice The above protocol included two fitting steps: First, we fitted λ by a grid-search to find the value that best matched SAXS data. Second, we reweighed the trajectory weights to obtain even better consistency with data. That is, the prior in the BME protocol  [3] , was not a true prior, as the initial weights, which were input in the reweighting protocol, had already been adjusted against data. Optimally, in a Bayesian framework, only one fitting step should be applied to obtain consistency between simulations and a given experimental dataset. However, the two-step fitting protocol here provided the most reliable results. We suggest that such a two-step fitting protocol is necessary when the force fields leads to a poor initial consistency with data, i. e. that all relevant states has not been sampled.
Inclusion of SANS data had only limited effect on reweighted distributions We proceeded to examine the information in the SANS data by reweighting the simulations. We reweighted from the simulations with the unaltered force field (λ = 1. 00), to monitor the largest effect of the reweighting protocol. In particular, we reweighted with either the SAXS data, with each of the SANS datasets, or with all data simultaneously and calculated the distributions of R g and D 13 (Fig  7 ). The distributions after reweighting were almost identical when using, respectively, SAXS alone and SANS at 0% D 2 O alone. Due to the contrast match-out of the hydrogenated RRM2 and RRM3 domains, the SANS data at 42% D 2 O had only limited information about the overall structure of TIA-1, and thus reweighting with this data alone only shifted the distributions marginally.
The SANS dataset at 70% will in principle contain information about the whole complex, but due to the low signal-to-noise ratio, this dataset alone was not sufficient to shift the distribution as much as the SAXS data or the SANS data obtained at 0% D 2 O. When including all data, the final distributions reflected a mix of the distributions obtained by using each of the datasets separately. The distribution obtained after including all data was, however, qualitatively similar to the distribution obtained after reweighting with SAXS data alone, albeit slightly closer to the initial distribution. Those results indicate that, for this specific system and data, the SANS data add only limited extra information about the distributions of R g and D 13 when high-quality SAXS data is already available.
06), however, gave a more subtle picture. The SANS at 42% D 2 O was again fitted equally well for all values of θ, and this time that was also the case for SANS data at 70% D 2 O. However, the agreement with the SANS data at 0% D 2 O worsened slightly as the fit to SAXS data improved. This result illustrates that the SANS data contain some structural information not captured fully by the SAXS data, though additional experiments (such as SAXS measurements of the deuterated samples and in D 2 O) would be useful to determine whether these small differences come from differences in sample conditions. The additional structural information was also reflected in the different optimal values of λ found in SANS and SAXS (Fig  3 ). For λ = 1. 04, the agreement with the SANS datasets improved as θ decreased until around θ = 1000, where the fit to SANS at 0% D 2 O and at 70% D 2 O slowly worsened (S4 Fig).
Conclusion We found that the latest Martini coarse-grained force field (version 3. 0. beta. 4. 17) resulted in structures of the flexible TIA-1 that, as judged by comparison with high-quality SAXS data, were on average too compact. However, by increasing the protein-water interaction strength of the force field by about 6%, we achieved a very good agreement with the SAXS data. Reweighting the data with a Bayesian maximum entropy method further improved the fit. In general, it cannot be expected that good agreement with data can be obtained by tuning a single parameter in a force field. Therefore, we also investigated "suboptimal versions" of the force fields, with 4% to 10% increase of the protein-water interaction strength. We stress that the term "suboptimal" here and elsewhere refers to the description of the SAXS and SANS data on TIA-1, and not the more complex problem of optimizing a transferable force field.
Moreover, the effective fraction of structures kept in the reweighted ensemble, ϕ eff , should not be too small. For this particular protein system, and these particular data, we found that the reweighted distributions were similar after reweighting when ϕ eff was 36% or above. However, more systems need to be investigated to reach a general rule of thumb for when a force field is good enough. Despite adding some additional information, we have shown that the structural information gain of including SANS data in the reweighting process was limited for this system and the available SAXS and SANS data. Inclusion of SANS data did not alter significantly the obtained distributions of the central parameters such as R g and D 13. It might be possible to increase the signal-to-noise ratio by decreasing the H 2 O content in the solvent, and thus gain more structural information from SANS data.
It is essential to know whether COVID-19 patients have a history of cerebrovascular disease, as it may be predictive of prognosis and useful for allocation of limited medical resources. This meta-analysis was performed to assess the incidence of cerebrovascular disease as a comorbidity in COVID-19 patients. The PubMed, Cochrane Library, Embase, CNKI, WFSD, and VIP databases were systematically searched. The pooled analysis of relevant data was conducted using RevMan 5. 3 software. The primary outcome was incidence of cerebrovascular disease as a comorbidity. Forty-seven studies involving 16,143 COVID-19 patients were included in this analysis. The incidences of a history of cerebrovascular disease and hypertension in COVID-19 patients were estimated to be 3. 0% (95% CI, 2. 0%-4. 0%; P<0. 00001) and 23.
It was recently reported, however, that blood pressure variability or threshold hypertension was associated with a poor prognosis in patients with cerebrovascular disease  [59]. This prompted us to ask the question, do COVID-19 patients with cerebrovascular disease comorbidity have a poorer prognosis than those without it? To answer that question, we systematically searched the PubMed database and found that a meta-analysis demonstrated that patients with a history of cerebrovascular disease have a 2. 5-fold greater likelihood of severe COVID-19 illness  [6]. On the other hand, the relationship between blood pressure variability and the prognosis of comorbid patients.
Search strategy To identify eligible studies, the main search was conducted in the PubMed, Cochrane Library, Embase, CNKI, WFSD, and VIP electronic databases for studies published between December and features/characteristics. Specific retrieval strategies were adjusted to accommodate the different databases. The procedure was concluded by: 1) the perusal of the reference sections of all relevant studies; 2) a manual search of key journals and abstracts from the major databases in the field of COVID-19; and 3) contacting experts to try to acquire unpublished data. The main search was completed independently by investigators. Any discrepancy was solved by consultation with an investigator not involved in the initial procedure.
Quality assessment Using an assessment tool, two independent reviewers assessed the quality of the selected studies according to the recommendations of the United Kingdom National Institute for Health and Care Excellence (NICE)  [62]. We analyzed the following eight domains: 1) cases are preferably come from medical institutions of different levels and perform multicenter research; 2) There are clear research hypothesis or purposes; 3) There are clear inclusion and exclusion criteria; 4) There are clear outcome indicators; 5) The data collected reach the expected goal; 6) patients are recruited continuously; 7) The authors clearly describe the study's primary findings; and 8) subgroup analysis and reporting of outcome indicators are performed. Studies are assigned one AGING point for each criterion met, making the maximum score 8 points.
Data extraction Two reviewers extracted data independently using a predefined data extraction form. Disagreements were resolved by discussion or consensus with a third reviewer. The data extracted included the first author; study characteristics (i. e. , year and publishing language); participant characteristics (i. e. , age, country, sample size); and outcomes (i. e. , incidence of cerebrovascular diseases or hypertension as comorbidity or dizziness/headache as the first symptom in patients with COVID-19). Cerebrovascular diseases (ischemic and hemorrhagic cerebrovascular diseases) refers to transient or permanent neurological dysfunction caused by one or more cerebrovascular diseases arising for various reasons  [63]. In addition, diagnoses of hypertension complied with the Japanese Society of Hypertension Guidelines  [64].
Population Patients who underwent a pre-therapeutic 18-FDG PET/CT in our nuclear medicine department for newly diagnosed histologically proven HNSCC between 2004 and 2014 were retrospectively included. All patients (≥ 18 years old) had a complete CWU (clinical examination, panendoscopy, head and neck CT, and/or MRI depending on primary tumor location and thoracic CT) within 4 weeks after diagnosis. Exclusion criteria were previous history of head and neck cancer, treatment started before 18-FDG PET/CT completion, non-squamous cell carcinoma histology, and cervical lymph node metastases of unknown primary tumor. The study was approved by the Institutional Ethics Committee (29BRC18. 0012) (RENOVATE ClinicalTrials. gov NCT03841175) and all patients provided written informed consent. The study followed the French General Data Protection Regulation (GDPR).
PET/CT Imaging The examinations were performed on several successive PET/CT hybrid machines: Gemini GXLi (Philips © Healthcare, Netherlands) and Biograph-mCT (Siemens © , Erlangen, Germany). Patients were required to be fasting for at least 6 h, and the average blood glucose measured before injection of the tracer was 5. 79 ± 0. 12 mmol/l. After an intravenous injection of 3-5 MBq/kg of 18-FDG (IBA Molecular Imaging © , Saclay, France), patients remained calm and at rest (bedbound for about 1 h). CT was initially performed in the cranio-caudal direction with a whole-body protocol and injection of iodized contrast material (1. 5 mL/kg) (after confirmation of no contraindication to contrast material). Whole-body PET/CT data were acquired in 3D mode and included both emission images (2 to 3 min per step), and transmission images required for attenuation correction.
Statistical Analysis The Kaplan-Meier method was used to estimate OS and PFS. The log-rank test was used to compare survival rates according to stage and management impact  (19). The Chisquared 2 test was used to compare the incidence of therapeutic impacts by location and stage, and the TOST equivalence test to compare therapeutic impacts between groups. The level of significance of the p-value was 0. 05. Statistical analyses were performed using SPSS v25 software (IBM Corp © , Armonk, NY).
This study is a systematic review and meta-analysis. The routine use of anticoagulants for the prevention of venothromboembolism in spinal patients is controversial and the risk benefit analysis is poorly described. The objective of this study is to collate the current evidence and quantify the risk of bleeding associated with anticoagulant thrombophylaxis in the spinal patient, both in the elective and trauma settings. Medline, Embase, Cochrane Trial Register databases and the grey literature were searched. Data were pooled via a meta-analysis from randomized trials and cohort studies comparing the rates of spinal epidural haematoma and wound haemorrhagic complications in spinal surgery patients receiving low molecular weight and low dose unfractionated heparin (LMWH/UFH) thromboprophylaxis and those not anticoagulated.
Data sources and collection: The search strategy included the following electronic databases; the Cochrane Central Register of Controlled Trials (Central), Medline via PubMed and Embase via Ovid. Additional studies were identified by hand searching of relevant bibliographies and journal archives and a search of the grey literature. Eligibility criteria: Studies were eligible if they reported the number of SEH and wound haemorrhage complications in patients who received pharmacologic thromboprophylaxis (intervention group) versus those who did not (control group). Study designs included were randomised controlled trials (RCTs), quasi-RCTs, cohort studies and case control studies. Eligible patient populations were adult patients (>16 years) undergoing any spinal surgeries via any surgical approach or patients with acute spinal injuries (with or without spinal cord injuries (SCI)) that were treated operatively or nonoperatively.
Data synthesis and analysis: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two authors (A Nguyen and S Tran) performed all phases of this systematic review and metaanalysis in an independent and blinded manner. Disagreements were resolved either by consensus or consultation with the other authors who are a statistician and spinal surgery experts. Data extracted included study and participant characteristics, details of intervention and comparison methods, intra-and extraspinal bleeding (including wound and non-wound related) outcomes and details about any associated re-operation or treatment and mortality rates. The odd ratios (OR) of the number of patients with bleeding complications versus the number of patients without, between intervention and control groups, were pooled via a random-effect-model meta-analysis using the Review Manager (RevMan 5.
Risk of bias within and across studies: Assessments of individual study quality were performed using the Cochrane Risk of Bias tool for randomised trials and the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I) tool for observational studies  23. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) pro software was used to perform an assessment of the quality of the body of evidence across all studies. This assessment was based on the GRADE approach in which the quality of the evidence was judged to range between "no concern" to "very serious concern", according to five criteria: risk of bias, inconsistency, indirectedness, imprecision and publication bias  24, 25.