Elbow

phenix.model_vs_data makes extensive use of the CCTBX library (Grosse-Kunstleve et al., 2002 ▶ ). For example, input PDB files are processed with the comprehensive PDB library implemented in the CCTBX. The Monomer Library (Vagin & Mur­shudov, 2004 ▶ ; Vagin et al., 2004 ▶ ) is used to obtain geometry restraints (bond, angle, dihedral, chirality, planarity and nonbonded restraints). If an input model contains residues not defined in the Monomer Library, for example a novel ligand or nonstandard residue, phenix.ready_set (N. W. Moriarty, unpublished), which uses eLBOW (Moriarty et al., 2009 ▶ ) internally, is used to automatically generate suitable restraints.
The second part of the model-quality section contains summary statistics similar to those generated by the MolProbity web site (Davis et al., 2007 ▶ ; Chen et al., 2010 ▶ ), by using the tools integrated into PHENIX. phenix.ramalyze is used to compute the number of Ramachandran outliers, as well as favored and allowed residues (Lovell et al., 2003 ▶ ), and phenix.cbetadev is used to compute the number of residues with >0.25 Å deviation from ideal Cβ positions (Lovell et al., 2003 ▶ ). phenix.rotalyze calculates the percent sidechain rotamer outliers (Lovell et al., 2000 ▶ ). phenix.reduce and phenix.probe are used to add H atoms and calculate the all-atom clashscore (Word et al., 1999 ▶ ).
phenix.xtriage (Zwart et al., 2005 ▶ ) is used to detect possible twinning (see, for example, Parsons, 2003 ▶ ; Helliwell, 2008 ▶ ). In the presence of possible twin laws, the R factors are computed without any twin law and then by taking each twin law into account. The twin-related calculations can be relatively time consuming, but provide a more robust basis for deciding if twinning needs to be included.
If a model was previously refined using TLS parameters, the ATOM and ANISOU records in the coordinate section of the PDB file may contain either total or residual atomic displacement parameters, depending on the refinement program used. The nature of the atomic displacement parameters is often not clear from the TLS information stored as REMARK records in the PDB file header. Therefore two alternatives are tested: R factors are computed assuming (i) total atomic displacement parameter values and (ii) residual atomic displacement parameter values in the coordinate section of the PDB file. The outcome with the lowest R factor is taken to be correct. Typical R-factor differences are 2–10%. The phenix.tls (P. V. Afonine, unpublished) module in the CCTBX is used to extract the TLS information (selections, origins, matrices) from the PDB file header. Two commonly used formats are automatically distinguished: phenix.refine (Afonine et al., 2005a ▶ ) and REFMAC (Murshudov et al., 1997 ▶ ).
R factors are computed after performing bulk-solvent correction and anisotropic scaling as described by Afonine et al. (2005b ▶ ). The Wilson B factor shown in the output is computed using a likelihood procedure (Zwart et al., 2005 ▶ ). Reflection data outliers are automatically detected (Read, 1999 ▶ ) and removed from subsequent calculations. The number of outliers is reported in the output.
phenix.model_vs_data also supports PDB files with multiple models [see, for example, Burling & Brünger (1994 ▶ ), Levin et al. (2007 ▶ ), Terwilliger et al. (2007 ▶ ), and references therein]. In addition a list of PDB files can be given as input, facilitating the computation of statistics for very large structures that are currently typically split across multiple files in the PDB.

Studies were included if [1 ] participants were aged 65 years or older within well-defined samples, with a clear description of the inclusion and exclusion criteria; [2 (link)] sarcopenia and frailty were considered as outcomes, in which HGS was used to identify this condition; [3 (link)] a description of the protocol used to measure handgrip strength was provided; [4 (link)] the outcome measures described are: type of dynamometer for the assessment of HGS, individual’s position (including shoulder, elbow, arm and handle position and posture), hand dominance, number of repetitions, acquisition and rest time, encouragement and handgrip strength values.
Randomised control trials, cohort studies, case control studies and cross-sectional studies were included, and meta-analyses or review articles, case reports, case series, meetings’ proceedings, conference summaries and duplicate records were excluded. Articles were not included if information about either the posture of the individual, or concerning the arm position (shoulder, elbow or wrist) was absent. When the complete procedure was not described but a reference was made to another article, we searched for the missing parts of the procedure. If the article did not add more details regarding the procedure, it was still excluded. In case of disagreement about the inclusion of a study, the reviewers discussed their opinions to reach consensus. The studies were divided into two subgroups: [1 ] articles about sarcopenia and [2 (link)] articles about frailty. Final studies selected for inclusion in each category were independently compiled in data tables. Articles which presented the same data as an earlier study were still excluded.

Clinical evaluations were performed on emergency room patients with hypotension who were older than 18 years and met the inclusion criteria. After explaining the study to the patient or relative, written consent was obtained. The patient’s demographic details, vitals, clinical details, and diagnosis were entered in the data collection proforma. Patients who required ABG as a part of routine care per treating physician were sampled. An arterial sample (0.5–1 mL) was collected using a heparinized syringe from the radial artery at the wrist level. The venous blood sample was obtained from the cubital or dorsal hand veins. Both samples were collected with minimum delay (less than 10 min). Both samples were analyzed as soon as possible using a blood gas analyzer Nova Biomedicals Stat profile pHOX ultra.

Radiographic data consisted of full-length coronal and sagittal radiographs were obtained in free- standing posture with the upper limbs resting on a support, the shoulders at 30° forward flexion, and the elbows slightly flexed [19 (link)]. All of the radiographic parameters were measured with Surgimap Software (version: 2.3.2.1; Spine Software, New York, NY).
All of the radiographic parameters concerned in this current study were shown in the Fig. 1A-B, which included thoracic kyphosis (TK), lumbar lordosis (LL), sagittal vertical axis (SVA), sacral slope (SS), pelvic tilt (PT) and pelvic incidence (PI). All of those radiographic measurements were performed by a dedicated team independent from the operating surgeons.Fig. 1

A Sagittal radiologic parameters: Thoracic Kyphosis (TK) measured from the superior endplate of T4 to the inferior endplate of T12 by Cobb method; Lumbar Lordosis (LL) measured from the superior endplate of L1 to the inferior endplate of S1 by Cobb method. Sagittal vertical axis (SVA) defined as the horizontal offset from the posterosuperior corner of S1 to the plumb line going through the vertebral body of C7. B Pelvic parameters: Sacral slope (SS): the angle between the horizontal line and the sacarl endplate; Pelvic tilt (PT): the angle between the vertical and the line through the midpoint of the sacral endplate to the femoral heads axis; Pelvic Incidence (PI): the angle between the perpendicular to the sacral plate at its midpoint and the line connecting this point to the femoral heads axis

Kyphosis was recorded as positive value ( +), and lordosis as negative value (-). The spinopelvic index (SPI) was calculated by the equation: SPI = SS/PT.

In preparation for our cluster analyses, all sleep variables were mean-centred and scaled such that higher scores indicated better sleep. We then used K-means cluster analysis to characterize subgroups of sleep variables in WRAP participants (n = 619), conducted using ‘factoextra’ package in R.⁶⁴ The cluster assignment was based on the minimum distance (sum of the deviation of each variable) of a participant from the centroid of the cluster. The optimal number of clusters was identified using the elbow method by looking at the total within-cluster sum of square (WSS). To characterize the sleep group for each clustering-based subgroup of participants, the effect size ( ${\mathrm{\epsilon }}^2$ ) of the sleep problems used in cluster analysis was noted in the right column of Supplementary Table 3. The relative contributions of the different problems in the grouping of participants were large, medium and small when ${\mathrm{\epsilon }}^2$ ≥ 0.26, ${\mathrm{\epsilon }}^2$ ≥ 0.08 and ${\mathrm{\epsilon }}^2$ ≥ 0.01, respectively.⁶⁵ Given the high correlation among sleep variables, we conducted preliminary cluster analyses, sequentially excluding subsets of the scales and examining fit statistics and consistency across solutions. Based on the best WSS and Calinski–Harabasz Index values, the following subset of scales was selected in primary analyses: SPI1, SDS, ADQ, SOM, self-reported sleep duration, ESS and ISI.
To characterize how sleep groups differed across sleep characteristics, we used chi-square for categorical variables and Kruskal–Wallis tests for Likert-scale variables [median (Q1–Q3) reported]. Post hoc pairwise group differences at unadjusted P < 0.05 were reported.
Three sensitivity analyses were conducted to investigate the consistency of sleep group assignments and to examine whether between sleep group patterns in our outcomes were stable across different sample selection criteria. Alternative 1: we used LPA to characterize sleep subgroups (‘Mclust’ package in R). Briefly, LPA was a data-driven approach using continuous variables and indicators to identify subgroups of individuals. In this statistical approach, subgroup membership was determined by examining the pattern of interrelationships among indicator variables (maximizing homogeneity within each subgroup and heterogeneity between subgroups).⁶⁶ Alternative 2 (cognitively unimpaired subset only): we reduced the original set to include only those who were cognitively unimpaired (n = 21 with mild cognitive impairment were removed; leaving n = 598), and K-means cluster analysis was used in this subset. Alternative 3 (expanded set with imputed ISI): as previously noted, the primary cluster analysis was based on the first visit with MOS, ESS and ISI. Since the MOS and ESS questionnaires were added to the battery several years before the ISI, we opted to enlarge ‘baseline sleep’ in sensitivity analyses to include those who had not yet completed an ISI but had completed MOS and ESS at least once. The imputation method used the sleep data on a person both before and after the ‘missing value’. The next observation carried backward assigned the person’s next known sleep score after the ‘missing’ one to the ‘missing value’. If the person did not have the next value, the last observation carried forward, assigned the person’s last previous known sleep score to the ‘missing value’, was used.^{67 (link)} The resulting enlarged set included n = 1237 available.