dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing ('omics) approaches As earlier mentioned, metabarcoding, metagenomic, and metatranscriptomic sequencing, in addition to metaproteomics, are employed in microbiome studies. While metabarcoding involves sequencing of amplified gene targets to describe the composition of microbial communities, the other approaches provide the same insights, along with functional characterization of the microbial communities, but without the biases of targeted gene amplification. Well-curated and regularly updated reference databases based on commonly targeted microbial genetic markers (e. g. , the SILVA ribosomal RNA gene database project  [94] ) allow for high taxonomic resolution with metabarcoding. In contrast, it can be challenging to accurately infer taxonomic origin using other 'omics approaches, such as shotgun metagenomics and metatranscriptomics due to limited well-curated databases.
For example, the Galaxy platform  [122]  offers a variety of tools for analyzing microbiome data. For an example guide to statistical analysis in microbial ecology, see Buttigieg and Ramette  [123]. Like metadata, a good record of each data processing and analysis step, along with any codes/scripts used, should be kept. These along with the metadata and sequences (including those of controls) should be made publicly available upon study publication. Different public repositories, along with instructions for data deposition, are available; the most popular ones include the NCBI Sequence Read Archive  [124] , EMBL-EBI European Nucleotide Archive  [125] , and the NIG DNA Data Bank of Japan  [126]. If studies are properly conducted, "negative" or "unexpected" results should not preclude publication of the data, as several journals now specialize in publishing genomic data.
Radiographic analysis Preoperative assessment was based on an A/P view, a lateral view on plain X-rays, and a CT scan to evaluate the glenohumeral joint, rotator cuff muscle trophicity  35  and fatty infiltration,  10, 14  Tingart index,  36  and the type of glenoid wear. The modified Walch 1 classification was used to evaluate glenoid wear. Data are reported in Table  I. On the postoperative A/P view, the alpha angle described by Schnetzke et al  29  was used to measure the alignment and inclination of the stem by measuring the angle between the axis of the humeral shaft and the line passing through the center of the prosthetic head and the distal end of the stem (Fig. 1 ). The metaphyseal and diaphyseal filing ratios were also calculated (Fig. 1 ). At the last follow-up, on the A/P view, signs of glenoid loosening were analyzed according to the Mole classification  24  : the Mole score analyzes the radiolucent lines (RLL) around the glenoid implant in 6 areas: RLL less than 1 mm was rated 1 point, RLL between 1 and 2 mm was rated 2 points, and RLL greater than 2 mm was rated 3 points.
Data Analyses All analyses were conducted using the R software (R ver. 4. 01). All participants were included on the basis of the intention-to-treat principle. We imputed missing values (m (the number of multiple imputations) = 20) using all variables of cognitive function performance, age, and sex with "mice" in the mice package  [28]. We used predictive mean matching because using this method in multiple imputations can work even if the sample size is small  [29]. To check the baseline differences in demographic measures and cognitive function among the groups, we used a permutation analysis of variance with the "aovp" function in the lmPerm package (http://cran. r-project. org/web/packages/lmPerm/index. html, accessed on 8 September 2021). For cognitive function analysis, we calculated the change scores in cognitive functions and emotional states (post-intervention score minus pre-intervention score).
Training Data First, we checked the training adherence. There was no significant difference in training days among the groups (CT-NF (mean = 25. 43, SD = 0. 84), CT (mean = 25. 11, SD = 0. 98), and ACT (mean = 24. 55, SD = 1. 06)). In addition, we evaluated changes in training task performance and participants' satisfaction and enjoyment after the intervention using a 5-point scale. There was no significant difference in the average satisfaction scores among the CT-NF (mean = 3. 84, SD = 0. 52), CT (mean = 3. 81, SD = 0. 45), and ACT (mean = 3. 88, SD = 0. 57) groups and enjoyment scores among the CT-NF (mean = 3. 99, SD = 0. 37), CT (mean = 3. 95, SD = 0. 43), and ACT (mean = 3. 98, SD = 0. 32) groups.
Because of the 3D measurement, all other things being equal, the surgical-related distance of inaccuracy could have been longer than 2 mm. Practically, the Euclidian distance was measured on the postoperative CT scan where both the electrodes and the trajectories were displayed after coregistration of pre-and postoperative image datasets (iPlan 3. 0; Brainlab; Germany; geometric resolution = 0. 1 mm). The curve of each electrode was modeled from the skeleton using a third-order polynomial regression  (Lalys et al. , 2014) , leading to an acceptable approximation of the electrode with a median R 2 -value of 0. 98 (monodirectional electrodes, 0. 969; multidirectional electrodes, 0. 981) and a median root mean square value of 0. 29 mm (monodirectional electrodes, 0. 35 mm; multidirectional electrodes, 0.
22 mm). The aspects of the polynomial regressions represented fairly the observed deformation on X-rays and CT scans. Deformation parameters were computed for each electrode: the x, y, and z deformation (mm), i. e. , the distances between the curve and the straight line along the length of the skeleton (Figure  8 ), and the torsion (mm -1 ) and the curvature (mm -1 ) values; coarsely, the higher the deformations, torsions, and curvatures, the higher the values (Frenet-Serret formula; Python library). The electrode (skeleton, The positive values of the deformation, along the x-axis, y-axis, and z-axis, meant that the electrode bent, respectively, toward the midline, posteriorly, and inferiorly (Figure  4 ). We computed the regional values of the deformation parameters, i. e. , by tissue layers (gyration, WMS, and deep brain): the average value and the intraclass correlation coefficient (ICC) of the deformation parameters (x, y, z, torsion, and curvature).
These regional parameters were compared between pairs of tissue layers, gyration versus WMS, gyration versus deep brain, and WMS versus deep brain, and between the right and left hemispheres. We also compared the regional parameters of the two types of electrode in the deep brain, where the rigid distal part was positioned, and for the right and left hemispheres. The deformation parameters were also analyzed according to the type of electrodes, the hemisphere, the brain shift index, and the surgical-related inaccuracy. Statistical analyses were performed using Stata software, Version 15 (StataCorp, United States). Continuous data were expressed as mean and standard deviation according to statistical distribution. The assumption of normality was assessed using the Shapiro-Wilk test. The comparisons between independent groups (type of electrode) were carried out, for continuous variables, using Student's t-test or Mann-Whitney test when the requirements of t-test were not met.
Unfortunately, no data have reported on how to measure clinically meaningful change using these criteria. 8 In 2018, the European Union-North American Clinical Trials in Alzheimer's Disease (EU-US CTAD) Task Force on Agitation encouraged the evidencebased development of a single rating scale tailored to the IPA agitation criteria, using existing datasets, and using items from existing scales. 9 The desired instrument should reflect the syndromic agitation criteria developed by the IPA, incorporate information from both patient and caregiver, define clinically meaningful effects, demonstrate sensitivity to change, and form the basis for powering studies. In this study we respond to this EU-US CTAD Task Force recommendation by constructing novel outcome measures from existing instruments, with items mapped onto the IPA agitation criteria, and then choosing the best measure based on performance characteristics, including sensitivity to change and predictive validity, as assessed in the Agitation and Aggression AD Cohort (A3C) database.
Statistical analyses All analyses were performed using SAS version 9. 4 (SAS Institute Inc. ). Baseline characteristics are reported as frequencies and percentages for nominal variables and mean ± standard deviation (SD) for quantitative variables. To estimate MCID between mADCS-CGIC (very/much improved: 1 or 2 vs. others: ≥3) and the agitation measures (CMAI and NPI-C-A+A as original and CMAI-IPA and NPI-C-IPA as novel) at 1 and 3 months follow-up, receiver operating characteristic (ROC) analyses  16  were conducted. Youden's index (YI, calculated as sensitivity + specificity-1) was used to calculate the optimal cut-offs in the ROC curves. 17 Areas under ROC curves (AUC) were also calculated. To To check the accuracy of the novel scales, Cronbach's alpha was estimated to examine the internal consistency of items for each IPA domain (EMA, VA, PA).
To assess test-retest reliability (in terms of stability) we estimated intraclass correlation coefficients (ICC) and their 95% confidence intervals (CIs) at 1 and 3 months for stable participants (mADCS-CGIC = 4). In accordance with the guidelines of Koo and Li,  24  ICC estimates and their 95% CIs were calculated using the SAS ICC macro developed by Robert Hamer (https://support. sas. com/kb/25/031. html ) based on a single rater/measurement, absolute-agreement, two-way mixedeffects model. 25 ICC results can be interpreted is as follows: a Mean (± standard deviation). 5-0. 75 to 0. 9 good reliability 5->0. 90 excellent reliability. Finally, we examined the association between the novel scales and the inclusion criteria with binary responses for each IPA domain to study the internal validity of novel measures.
However, the internal coherence of CMAI is more questionable; Cronbach's alpha was good for EMA but it was low for VA and very low for PA. One explanation could be that when calculating the Cronbach's alpha, the number of items is taken into account. Indeed, in the EMA and VA domains The major strength of this study is that it uses data from A3C that mimics a clinical trial in terms of methodology and measurement in a naturalistic setting in both community and nursing home settings. However, there are notable limitations to this work. A3C was an observational, usual care study, whose population received close follow-up, and was frequently treated with psychotropics or non-pharmacological approaches for agitation. 14 However, we believe it is an appropriate dataset for the validation of outcome measures-because no trial can ethically eliminate other efforts at treating agitation, development of novel treatments for agitation will by necessity occur against this background.
a modified Delphi process, items from the CMAI and the NPI-C were mapped by an expert panel onto the IPA agitation criteria domains to generate derivative "novel" measures, CMAI-IPA and NPI-C-IPA. Original existing measures were the CMAI and the NPI-C-Agitation and Aggression domains (NPI-C-A+A). The original and novel instruments were examined in the A3C study database to assess their performance in terms of minimal clinically important difference (MCID), sensitivity to change, and predictive validity. Internal consistency and reliability were also assessed for the novel measures. For these purposes, the modified Alzheimer's Disease Cooperative Study-Clinical Global.
ACKNOWLEDGMENTS This work was supported in part by  Ethypharm , by grant  P30AG066507  to the  Johns Hopkins Alzheimer's Disease Research Center , the  Richman Family Precision Medicine Center of Excellence in Alzheimer's Disease at Johns Hopkins University , and grants  R01AG050515  and  R01AG054771  to  Dr. Rosenberg. Functional Neuromodulation ,  Bright Focus Foundation  and (2) payment as consultant or advisor from  Avanir, Astellas, Roche, Karuna, SVB Leerink, Maplight, Axsome, Global Institute on Addictions. Maria Soto  declares: payment as consultant or advisor from Avanir,  Acadia. Zahinoor Ismail declares: (1) payment as consultant or advisor from  Lundbeck/Otuska  outside the submitted work; (2) payment to his institution from  Acadia ,  Biogen ,  Roche , and  Sunovion  outside the submitted work.
Objectives To compare the benefits of a tightcontrol/treat-to-target strategy (TC/T2T) in axial spondyloarthritis (axSpA) with those of usual care (UC). Methods Pragmatic, prospective, cluster-randomised,  controlled, open, 1-year trial (NCT03043846). 18 centres were randomised (1:1). Patients met Axial Spondylo Arthritis International Society (ASAS) criteria for axSpA, had an Ankylosing Spondylitis Disease Activity Score (ASDAS) ≥2. 1, received non-optimal treatment by non-steroidal anti-inflammatory drugs and were biologic-naive. Interventions (1) TC/T2T: visits every 4 weeks and prespecified strategy based on treatment intensification until achieving target (ie, ASDAS <2. 1); (2) UC: visits every 12 weeks and treatment at the rheumatologist's discretion. Main outcome Percentage of patients with a ≥30% improvement on the ASAS-Health Index (ASAS-HI).
Study design This was a pragmatic, prospective, parallel, cluster-randomised (with the centre as the cluster), open, controlled (two arms) trial (TICOSPA-NCT03043846). This study was not considered interventional, as the treatment was either a standard of care approach according to the treating rheumatologist (UC arm) or a TC/T2T algorithm strictly following the current international scientific recommendations for axSpA management. The study duration of 1 year was considered sufficient to demonstrate the benefits of a particular strategy on symptoms. The study was conducted in agreement with local good clinical practice (GCP) and the Declaration of Helsinki.
Participant flow Eighteen centres (4 in Belgium, 10 in France and 4 in the Netherlands), all of them rheumatology departments with an expertise in SpA, were invited and agreed to participate. All centres signed an agreement to follow the treatment to which they were allocated. After randomisation, nine centres were allocated to the TC/T2T arm and nine to the UC arm. During the recruitment period (from February 2017 to June 2019; dataset locked on August 2019), centres from the TC/ T2T arm screened and included 80 patients, while centres from the UC arm screened 83 patients and included 80. The analysis population comprised 160 patients. After 1 year, one centre (UC arm) had not included any patient. At the patient level, in the TC/T2T arm, seven patients were lost to follow-up and one refused to continue the study; in the UC arm, three patients were lost to follow-up, four refused to continue and one was excluded because of a non-spondyloarthritis diagnosis during follow-up.
Gene expression of ACE2, BSG and NRP1 is shown as dot plots for all 30 clusters. UMAP plot and dot plot for marker genes is shown in Extended Data Fig. 4. d, Representative images of the human frontal cortex co-stained for ACE2, BSG and NRP1 with the endothelial marker CD34 confirmed the cell-type-specific localization of the receptors in the vascular unit. ACE2, BSG and NRP 1 were co-localized with the endothelial protein CD34. Images were obtained from a dataset of three patients (three sections per patient). Scale bars, 5 µm. e, Human brain endothelial hCMEC/D3 cells were transfected with human ACE2 and were incubated with SARS-CoV-2 (multiplicity of infection (MOI) of 1). Twenty-four hours after exposure to the virus, the spike glycoprotein was detected in several ACE2-positive cells indicating infection.
The experiment was performed three times. Scale bars, 5 µm. f, dsRNA was found in caveolin-1-positive endothelial cells in the cortex of two of four patients with COVID-19 but not in three uninfected controls. Scale bar, 20 µm and 2 µm (inset). To evaluate the expression of NRP1, BSG and ACE2 in human brain endothelial cells, we analyzed a published single-nuclei RNA-seq dataset that allowed the identification of endothelial and pericyte cell clusters  28  (Extended Data Fig. 4a, b ). In line with the mouse data, BSG and NRP1 were highly expressed in endothelial cells (Fig. 2c ). In this study, ACE2 was below the detection limit in.
Vectors were injected into the tail veins of mice (1. 8 or 3. 3 × 10 11 genomic particles per mouse, 100 µl). Except in the experiment using Ripk3 -/-mice (Fig. 7a, b ), for which we used male and female mice, only male C57BL/6 mice were used for vector injection. Mice were perfused under deep anesthesia with PBS and paraformaldehyde (PFA, 2% or 4%) 2 weeks after administering the vector. Total DNA of a sagittal brain section (50-µm thick) was extracted using the DNeasy tissue kit (Qiagen) according to the manufacturer's instructions. We quantified DNA with a spectral photometer (Nanodrop ND-2000C, Peqlab) as described previously  69. Cell culture and transfection. hCMEC/D3 and bEnd. 3 cells. The human brain endothelial cell line hCMEC/D3 (Merck SCC066, RRID: CVCL_U985) and the mouse brain endothelial cell line bEnd.
3 (American Type Culture Collection (ATCC), CRL-2299, RRID: CVCL_0170) were cultivated as described previously  36, 37. We used 24-well plates for luciferase assays, 48-well or 96-well plates or chamber slides for immunocytochemistry and 6-well or 12-well plates for immunoblotting. After withdrawing heparin (hCMEC/D3) or penicillin-streptomycin (bEnd. 3) from the medium, we transfected the cells using Lipofectamine 3000 (Thermo Fisher Scientific) and the following plasmids: pNF-κB-Luciferase (200 ng per well; Stratagene), pCAG-hACE2-TMPRSS2 (100 ng per well on 8-well chamber slides; 2,500 ng per well on 6-well plates, Invivogen), pCAG-GFP, pCAG-p. Cys145Ala-M pro -HA or pCAG-M pro -HA (400 ng per well on 24-well and 48-well plates; 1,000 ng per well on 12-well plates), pCAG-NEMO-2A-eGFP (1,000 ng per well on 12-well plates)  36  and pRL-SV40 (40 ng per well).
For precursor selection, ions of charge states of 1+ and >6+ were excluded from fragmentation. We fragmented precursor ions using higher-energy C-trap dissociation with a stepped normalized collision energy of 30. MS/MS spectra were acquired with a resolution of 17,500, AGC target was set to 10 5 and the maximum injection time to 50 ms. Data analysis was performed using the Xcalibur software (version 3. 0. 63, Thermo Scientific). To identify NEMO-derived peptides, a database search using the MaxQuant software (version 1. 6. 1. 0, Max Planck Society)  75  with the Andromeda search engine was performed. We searched raw data against a human NEMO database (https://www. uniprot. org/; July 2020). Digestion mode was set to unspecific and minimum peptide length to 3.
Eighteen cycles of PCR were used to amplify the adaptor-ligated cDNA products. Libraries were quantified using a High Sensitivity DNA chip (Agilent) on a Bioanalyzer 2100 and the Qubit High Sensitivity DNA assay (Thermo Fisher Scientific). Libraries were sequenced using High Output sequencing kits (75 × 2 bp; Illumina) by a commercial provider (Novogene). Bioinformatic analysis of single-cell RNA-sequencing data. Raw gene expression matrices were generated for each sample by a custom pipeline combining kallisto (v. 0. 46. 1) and bustools (v. 0. 46. 1) coupled with mouse reference version GRCm38. The output filtered gene expression matrices were analyzed by R software (v. 4. 2. 0) with the DropletUtils (v. 1. 8. 0) and Seurat (v. 3. 2. 0) packages. In brief, for each sample, cells were detected by ranking cell barcodes according to their number of unique molecular identifiers (UMIs) captured using the barcodeRanks function.
Low-ranked cells from this process were labeled as false positives and were discarded. Only genes expressed in >0. 5% of the dataset and cells with >200 genes assigned were selected for further analyses. Low-quality cells were removed if they included >20% UMIs derived from the mitochondrial genome. Gene expression matrices were normalized by the NormalizeData function and 2,000 features with high cell-to-cell variation were calculated using the FindVariableFeatures function. For both samples, we identified 'anchors' between individual datasets with the FindIntegrationAnchors function and fed these anchors into the IntegrateData function to create a batch-corrected expression matrix of all cells, which allowed cells from different datasets to be integrated and analyzed together. The dimensionality of the data was reduced by principal-component analysis followed by visualization with UMAP clustering using the Louvain algorithm.
Finally, we clustered cells by using the FindClusters and FindNeighbors functions. Cluster-specific markers were identified by the FindAllMarkers function and assigned to cell types. Clusters were then classified and annotated based on expressions of canonical markers of particular cell types. All details regarding the Seurat analyses performed in this work can be found in the website tutorial (https://satijalab. org/seurat/v3. 2/pbmc3k_tutorial. html). For analysis of single-nuclei RNA-seq data from human brain, preprocessed expression matrices were obtained from the Gene Expression Omnibus (GEO; GSE97942) consisting of >60,000 single nuclei from the human adult visual cortex, frontal cortex and cerebellum  28. The gene expression matrices were further processed as described above using the Seurat package (v.
Statistics and reproducibility. No statistical methods were used to predetermine sample sizes, but our sample sizes are similar to those reported in previous publications  37, 41, 80. Data were analyzed using Prism 8 (GraphPad) and SPSS 25 (IBM). Significance was considered when P < 0. 05. Depending on the dataset and experimental design, different statistical methods were used as indicated in Supplementary Table  5. Parametric statistics (for example, t-test and ANOVA) were only applied if assumptions were met, that is, datasets were examined for Gaussian distribution using the D' Agostino-Pearson test, aided by visual inspection of the data and homogeneity of variances by Brown-Forsythe, Levene's or F-test (depending on the statistical method used). If assumptions for parametric procedures were not met or could not be reliably assumed due to small sample size, non-parametric methods were used as indicated.
Two-tailed tests were applied if not indicated otherwise. Greenhouse-Geisser correction was used in ANOVA statistics if the sphericity assumption was violated (Mauchly test). No data points were excluded. Cell culture studies were performed at least three times in independent experiments with at least three replicates per condition and per experiment unless stated otherwise. Animal experiments were repeated as stated by the N number. Animals were randomly allocated to diet or treatment groups as long as age-matched, sex-matched and littermate conditions were fulfilled. All analyses were performed blinded without the knowledge of the genotype, treatment or infection status if not needed for subsequent processing. Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Image visualization and manual analysis were performed using Omero Server software (5. 6. 3) 76. Super-resolution microscopy. We used stimulated emission depletion (STED) imaging and expansion microscopy. For STED, 640-and 561-nm diode excitation lasers, a 775-nm STED laser, all pulsed at 40 MHz, and a ×100 1. 4-NA Olympus UPlanSApo were utilized. A spatial light modulator (Hamamatsu) was used to produce either a doughnut-shaped (two-dimensional (2D) STED) or a top-hat (three-dimensional (3D) STED) phase mask, shaping different depletion beams without changing the optical setup. To reduce photobleaching at an optimal signal-to-noise ratio, we used DyMIN adaptive illumination. For expansion microscopy, after immunofluorescence staining, gelation, digestion and expansion were performed as described previously  77.