dataesr/scientific-paragraphs-categorization huggingface dataset:
It is understandable that the misconception of the shape of the F-v relationship can lead to serious errors in the estimation of several parameters derived from linear equations, such as F0 and V0. However, despite this questionable choices, the shape of the STS F-v and P-v equation (Sfv and a_poly respectively) was found to be correlated (r = 0. 229-0. 335) while other studies found inconsistent results for them (p > 0. 05)  (Giroux et al. , 2014; Janicijevic et al. , 2019). Although results are similar to previous works, this method presents some limitations. First, participants were filmed in a side view bounding Openpose on one side to calculate kinematics. This could have increase the risk of losing data or abnormal recording if the subject was not well detected. When missing data appeared, it results in extrapolation when calculating F-v parameters that could partially explained the incongruent correlation obtained  (Lindberg et al.
3000-7000 years BP  (Zhang 2020). The river buffalo was introduced to Egypt from India via Mesopotamia during the nineth century  (Sidky 1951). Today, it is the most important domestic animal of Egypt, with approximately 3. 7 million heads (FAO 2017) used mainly for milk, but also for meat and as a draft animal. In this study, we sequenced the mitochondrial genome of 29 Egyptian river buffaloes and made a comparison with all sequences of Bubalus available in GenBank. As indicated in Figure  1 , blood samples were collected in two breeding stations: Kafr El Sheikh for the buffaloes from northern governorates, and Beni Suef for the buffaloes from southern governorates. Total DNA was extracted following the phenolchloroform protocol published in  Sambrook and Russel (2001). DNA extracts are stored in the ISYEB research collection at the Mus eum national d'Histoire naturelle (Paris, France).
Three overlapping PCR products were amplified using the primers published in  Hassanin et al. (2012 Hassanin et al. ( , 2020)) : (1) GluMA and LMet3-CH; IleU and Leu2LM1-CH; and (3) Ser2U and LPro-CH. PCR reactions were performed as detailed in  Hassanin et al. (2020). The amplicons were sequenced using the Ion Torrent Personal Genome Machine (Thermo Fisher Scientific). The NGS reads were assembled on GeneiousV R 10. 2. 2 (Biomatters Ltd. ) using the mitochondrial genome reference for Bubalus bubalis (accession number: NC_006295). The 29 new mitochondrial genomes generated for this study were annotated on Geneious and deposited in GenBank. The mitochondrial genome of the Egyptian buffalo is a circular double-stranded DNA sequence that is 16,357-16,359 base pairs in length. The 29 mitogenomes of Egyptian buffalo represent 24 haplotypes.
The overall base composition is A: 33. 1%, C: 26. 6%, G: 13. 9%, and T: 26. 4%. The genome contains the 37 genes found in a typical mammalian genome. All protein-coding genes of the mtDNA have a methionine start codon (ATR), except ND4L (GTG) and ND2 (ATT for members of haplogroup RB2, see below). With the exception of the CYTB gene, which has the stop codon AGA, all protein-coding genes appear to be terminated by TAA or TAG. However, this stop codon is incomplete in the COX3, ND3 and ND4 genes. The control region is 926-928 bp in length. The 24 mitochondrial haplotypes detected for the Egyptian buffalo were compared to the mitogenomes available in GenBank for the genus Bubalus, representing two Haplogroups showing more than 0. 5% of nucleotide divergence are highlighted by different colored rectangles, and those found in the swamp buffalo are named SBa to SBe (the name of the haplotype is followed by a dash and a number when the same haplotype was found in at least two individuals).
Among the 29 mitogenomes of Egyptian river buffalo, the 21 samples collected in Kafr El Sheikh are indicated in red, whereas the eight samples collected in Beni Suef are indicated in green. The four mitogenomes of river Buffalo assembled using SRA data available in GenBank are named with the SAMN accessions. For nodes supported by bootstrap percentage (BP) 90 in the RAxML analysis (see details in Material and Methods), the two values correspond to the posterior probability (PP calculated under MrBayes, left of the slash) and BP (right of the slash). An asterisk is used when both MrBayes and RAxML analyses provided maximal support values, i. e. PP ¼ 1 and BP ¼ 100, respectively. No information was provided for nodes supported by BP < 90. river buffaloes, 112 swamp buffaloes and the anoa (Bubalus depressicornis).
We also assembled four mitogenomes of Indian river buffalo using SRA (Sequence Read Archive) data (GenBank accession numbers are detailed in Figure  1 ). Syncerus caffer (accession number: NC_020617) was used as an outgroup. After exclusion of identical haplotypes and removal of ambiguous regions for primary homology, our final DNA alignment is 16,356 bp in length and contains 119 sequences, including 30 haplotypes for the river buffalo and 87 haplotypes for the swamp buffalo. The tree shown in Figure  1  was reconstructed with MrBayes 3. 2. 6  (Ronquist et al. , 2012)  and the GTR þ I þ G model. The Bayesian posterior probabilities (PP) were calculated using 10,000,000 Metropolis-coupled MCMC generations, tree sampling every 1000 generations, and a burn-in of 25%. Bootstrap percentages (BP) were calculated using RAxML version 8.
Figure1. Phylogeny of Bubalus based on complete mitochondrial genomes. The tree was reconstructed under MrBayes using the 118 mitogenomic haplotypes of Bubalus identified in our alignment of 16,356 nucleotides. The tree was rooted with Syncerus (not shown). Haplogroups showing more than 0. 5% of nucleotide divergence are highlighted by different colored rectangles, and those found in the swamp buffalo are named SBa to SBe (the name of the haplotype is followed by a dash and a number when the same haplotype was found in at least two individuals). Among the 29 mitogenomes of Egyptian river buffalo, the 21 samples collected in Kafr El Sheikh are indicated in red, whereas the eight samples collected in Beni Suef are indicated in green. The four mitogenomes of river Buffalo assembled using SRA data available in GenBank are named with the SAMN accessions.
Methods: This cross-sectional study focused on outpatients with colorectal cancer undergoing chemotherapy. A questionnaire including the Connor-Davidson Resilience Scale, Hospital Anxiety and Depression Scale, the MOS 12-item Short-Form Health Survey, and items based on a literature review was administered between August 2019 and August 2020. SPSS version 26 was used for data analysis. Correlation analysis and t-test were applied. In addition, Amos version 26 was used and structural equation modeling was applied to create a causal model. Results: A total of 121 participants (94. 5%) were included in the study. The goodness of fit (GFI) of the causal model was GFI = 0. 94, adjusted GFI = 0. 906, comparative fit index = 0. 997, and root mean square error of approximation = 0. 011. Resilience had a negative effect on anxiety and depression and a positive effect on QOL.
Sample size For calculating the sample size, GPower 3. 1 (Heinrich-Heine-Universitaet Duesseldorf, Duesseldorf, Germany) was used for the correlation coefficient, and previous studies for the covariance structure analysis. With the correlation coefficient, the calculation by a two-sided test, and effect size of 0. 3, α = 0. 05, 1-β = 0. 8, yielded a sample size of 82. [35]  Based on these sizes, it was decided to set the sample size to 120 or more. Data were collected until the number of valid responses reached 120.
Date management and analysis The data were analyzed using SPSS version 26 (International Business Machines Corporation, New York, USA). Correlation coefficients were calculated to identify the relationships between the resilience of patients with colorectal cancer undergoing chemotherapy and the reported scores of anxiety and depression and QOL. Furthermore, t-tests were utilized to interpret the relationship between patient resilience, anxiety and depression, and QOL as dependent variables and personal factors and disease-related factors as independent variables. In order to identify the relationship between patient resilience, anxiety and depression, QOL, and personal and disease-related factors that showed significant trends through t-test and ANOVA, a causal model using Amos version 26 (International Business Machines Corporation, New York, USA) was created; the analysis utilized structural equation modeling.
Acknowledgments I wish to express our appreciation to the patients and staff of participating hospitals for their cooperation in conducting this study. This study is a part of a doctoral dissertation written by the author at the  Graduate School of Nursing, Osaka Medical University, in Japan. I also wish to thank  Professor Kumi Suzuki  for valuable advice from research planning to writing the dissertation. I further wish to express my appreciation to Associate  Professors Yuri Ito  and  Emiko Kusano ,  Professor Norito Yasuda  for advice on the analysis, and  Professor Yuko Tomari  of  Kansai Medical University  for advice on the research planning.
A. RESULTS FOR UPWARD TRANSMISSION Figure  6  shows the marginalized diffusing patterns  12  for the Cl-B and Cl-S channels with the orange and maroon colors, respectively. The diffusing pattern is measured at the bottom of the sea ice, i. e. , ∆ z = 0, with DC gains of G u = 0. 26 and 0. 37 for Cl-B and Cl-S channels, respectively. These results were obtained by running the MCNRT using the ZeMax Opticstudio software  [85]  over 10 6 iterations. Note that we have verified that increasing the number of iterations to 10 7 resulted in almost identical results. Figures  6a  and 6b  show the marginalized diffusing patterns versus the polar and the azimuthal angles, I d↓θ and I d↓φ , respectively. As shown in these figures, the marginalized intensity is uniform with respect to (w. r. t. ) φ d , however, it is oriented w.
5, 0. 5] m. The intensities decay exponentially with peaks 1 × 10 -3 and 1. 8 × 10 -3 at the center, x d = 0 m, for the Cl-B and Cl-S SSCL channels, respectively, and almost zero value at |x d | = 0. 5 m. Due to the uniform value of the marginalized intensities w. r. t. φ d , the intensity profiles for x d and y d are similar and can be fitted 13 In this paper, the fitting is accomplished using the tool of curve fitting in Matlab  [86, CFTOOL]. The goodness of the fit R-square= {0. 8574, 0. 8714} and RMSE= {5. 95 × 10 -6 , 7. 606 × 10 -6 } for Cl-B and Cl-S channels, respectively. with the following two-dimensional functions for the Cl-B and Cl-S SSCL channels as 14   Figure  6d  shows the marginalized diffusing pattern I d↓t (i. e. , temporal dispersion patterns of the upward transmission) with t d =  [2, 24]  ns.
Data collection The collected data have been described elsewhere  [15]. Briefly, patients were included in participating ICUs between March 10, 2020 and April 15, 2020. We recorded demographics data and comorbidities using the Charlson comorbidity index  [18]. We collected duration since symptoms onset and presence of coinfection. We recorded after optimization the following mechanical ventilation (MV) settings after intubation in supine position and initial ventilation optimization: total PEEP, plateau pressure (Pplat), Vt. We recorded administration of advanced therapies for acute respiratory failure during the ICU stay (neuromuscular blocking agents, inhaled pulmonary vasodilators, prone-positioning, and ECMO). We calculated from measured variables the driving pressure as DP = Pplat-PEEP and the compliance of the respiratory system as Crs = Vt/DP in mL/cmH 2 O.
We included Pplat and Crs in the same model despite collinearity following published method  [22, 23]  assuming that if one of these two variables remained significantly correlated with the primary endpoint, this variable would be more informative than the other. After reviewing of the manuscript, we performed post hoc analyses. We analyzed the association between Crs and our primary endpoint in the subgroups of patients with severe and moderate ARDS (P/F ≤ 100 mmHg and P/F between 101 and 200 mmHg). We also analyzed the correlation between P/F ratio and Crs across three categories of PEEP (5-8, 9-12 and above 12 cmH 2 O) using Pearson correlation. To analyze the evolution of Crs at day-14, we measured day-14 Crs in patients under controlled MV as described above. We compared paired respiratory data (Vt, PEEP, DP, Crs) using the paired t-test or Wilcoxon ranking test according to distribution.
NR3C1, the gene encoding the glucocorticoid receptor, is polymorphic presenting numerous single nucleotide polymorphisms (SNPs) some of which are emerging as leading cause in the variability of manifestation and/or response to glucocorticoids in human diseases. Since 60-80% of patients with Diamond Blackfan anemia (DBA), an inherited pure red cell aplasia induced by mutations in ribosomal protein genes became transfusion independent upon treatment with glucocorticoids, we investigated whether clinically relevant NR3C1 SNPs are associated with disease manifestation in DBA. The eight SNPs rs10482605, rs10482616, rs7701443, rs6189/rs6190, rs860457, rs6198, rs6196, and rs33388/rs33389 were investigated in a cohort of 91 European DBA patients. Results were compared with those observed in healthy volunteers (n = 37) or present in public genome databases of Italian and European populations.
Fluorescence intensities were measured with FACS ARIA  (Becton Dickinson Biosciences; Falchi et al. , 2015; Federici et al. , 2019). The cells were then subjected to reverse-phase protein array (RPPA) analyses as described  (Federici et al. , 2019). Internal standardization was performed using the software package JMP v6 (SAS Institute, Cary, NC, United States), two-way hierarchical clustering was performed with the Wards method and significance was evaluated by two-group Wilcoxon test (significance cut off p ≤ 0. 05). All the data sets are available at http://capmm. gmu. edu/data. The data obtained with cells expanded without Dex are unpublished while those obtained with cells expanded with Dex have been the subject of a previous publication  (Federici et al. , 2019)  and are reported here for comparison.
DISCUSSION In this study, we used PCR single-strand conformation polymorphism analyses to determine the frequency of eight clinically relevant NR3C1 SNPs in a cohort of 91 DBA patients, 65 from the Italian Registry, and 26 from other European registries. A small cohort of healthy volunteers (n = 37) was analyzed in parallel as technical control. The study has strengths as well as intrinsic limitations. Although for a rare disease such DBA, our patient sample size is strength, in the context of robust analyses for genetic association, the sample size was small. Other limitations which may have weakened the power of the statistical analyses include incomplete patient demographic information for some patients, and some SNP genotypes were unavailable. However, the data presented here should be useful in improving our understanding the pathobiology of DBA and strongly support the need for further investigation on the effects of genetic variability in the manifestation of this disease.
Quantification and statistical analysis Images from the cell-based screen described in this protocol (see Figure  4A ) could be quantified by using software such as ImageJ or Photoshop. By determining the fluorescence intensity in the nucleus of transfected cells versus cells transfected with a negative control, a percentage of degradation could be obtained. We rather quantified the degradation percentage using flow cytometry after the cell-based screen, which allows the quantification of a large number of cells (more than 10, 000) in a very fast fashion. We normalized the intensity of the mCherry fluorescence from GFP positive cells (i. e. , green mitochondria) transfected with an antibody-based degrader to the intensity of the mCherry fluorescence from the GFP positive cells transfected with a non-degrader negative control antibody (see Figure  4B  as example).
Limitations A limitation of this protocol would be the level of the screen's throughput described here. It is a relatively low throughput screen and could be turn into a medium throughput screen using automation. Minipreps can be performed in 96 wells, followed by 96 well reverse transfection, and screening could be performed with a high content imager, such as Operetta or Arrayscan, or a flow cytometer, both in 96-well plate format. In these cases, quantification should be performed using the device dedicated software. Such screening methods would further provide a direct quantification of the degradation for each clone tested. The number of clones to screen to get a degrader depends on various parameters such as the amino acid sequence diversity of the clones obtained after the phage display (i.
Drift tube muon detector The luminosity measurement based on the DT muon detector  [20]  is based on an efficient trigger on a low-background physics object: muons produced in the CMS barrel. Muon track segments from barrel muon DT stations are sent every bunch crossing to track finder hardware, where tracks are built and later used to generate first level triggers. The number of tracks in time windows of approximately 23 seconds is read out and stored in a database. These data are used to estimate luminosity. The rate of muons in the DTs is significantly lower than the rate for most other observables from other luminometers. Thus, there are not enough muon tracks during the vdM scans to provide a precise measurement of σ vis , and so the system must be calibrated to the normalized PCC luminosity measurement.
6 Hz is the LHC revolution frequency during collisions, and σ is the total interaction cross section. At the LHC, L b is typically expressed in units of Hz/µb ≡ 10 30 cm -2 s -1. Two algorithms have been developed for extracting the instantaneous luminosity. One method is rate-scaling, where the raw rate of observables is scaled with calibration constants to the luminosity. Rearranging Eqs. (  2 ) and (3), one can estimate the instantaneous luminosity using the average number of observables at a given time: L b = N observables N observables/interaction ν r σ ≡ N observables ν r σ vis. (4) Luminosity is estimated from PCC, PVC, DT, and RAMSES data using the rate-scaling algorithm. The second method (zero counting) uses the average fraction of bunch crossings where no observables in a detector are produced.
Primary vertices are reconstructed, and their positions are then analyzed to perform a global fit to derive the transverse bunch density distributions of the beams (as discussed in Section 4. 4. 1). The BI scans are also analyzed as regular beam-separation scans. During both BI and regular vdM scans, the transverse bunch density distributions are also determined by simultaneously fitting the beam-separation dependence ("evolution") on the luminosity and the luminous region position, orientation, and spatial width, as reflected in the reconstructed primary vertices (as discussed in Section 4. 4. 2). The LHC conditions at IP 5 for the luminosity calibration fills discussed in this paper for 2015 and 2016 are summarized in Table  1. Pixel data are collected for PCC and for methods involving collision vertices using a zerobias trigger, which collects data from five BCIDs with a total rate of approximately 20 kHz.
The spurious charge is measured by the LHC LDM system, which provides a precise longitudinal distribution of the beam charge with a time resolution of 90 ps. The data from the LDMs for fills 4266 and 4954 indicate that both the ghost and satellite charges are small. The latter is estimated to be <0. 1% for each of the two beams and is neglected. No particular time dependence for either beam is observed, and the resulting overall spurious-charge correction in σ vis amounts to +0. 2 and +0. 3% in 2015 and 2016, respectively. This is applied as a correction to the beam currents in Eq. (  16 ). The ghost charge is also measured using the beam-gas imaging method  [12, 46, 47] , which compares the beam-gas rates in bunch crossings at IP 8 (the location of the LHCb detector) where only one beam contains protons, or where neither beam contains protons, leading to consistent results with the LDM measurement.
Therefore, the observed beam-beam deflection may be overestimated because of the inclusion of noncolliding, nondeflected bunches. In this analysis, a reduction factor of 0. 6 is thus applied on top of the geometric factor in both years, which is the approximate fraction of the total number of bunches in the vdM fills that collide at IP 5. The orbit drift, as described above, is also subtracted from the single-beam DOROS measurements before forming the actual beam separation. Finally, an additional length scale correction is made to DOROS data for each beam and in both of the two transverse directions. The calibration using vertices, both for DOROS and nominal LHC positions, determines only the average length scale for the two beams. The calibrations of each beam are also not necessarily the same for the two sets of data.
Therefore, a final, relative calibration of the DOROS data is made to align each beam in both transverse directions to the scale of the LHC beams. Figure  8  shows the residual difference in beam separation in all y scans in 2015 and 2016 as well as the residuals per beam in a single scan, which shows symmetric behavior. The resulting impact on σ vis is in the range -0. 6 to +0. 4 and -0. 5 to -0. 2%, with average values of -0. 1 and -0. 3%, in 2015 and 2016, respectively. Corrections are applied for each scan, and the uncertainty comes from the reduction factor in the beam-beam deflection correction at the location of the DOROS BPMs.
Models for the proton bunch density are built from Gaussian distributions parameterized with an additional correlation parameter : g j (x, y) = 1 2πσ jx σ jy √ 1 -2 j exp - 1 2(1 -2 j ) x 2 σ 2 jx + y 2 σ 2 jy - 2 j xy σ jx σ jy , ( 21 ) where j indicates the beam number (j = 1 or 2). More complicated models are constructed with sums of these individual correlated Gaussian distributions. Distributions with a wide tail are better described by adding a Gaussian component with a small weight and a large width. Distributions with a flattened central part can be modeled with an additional component with a small negative weight and a narrow width. Typically, both nonzero correlation parameters and different widths are required to describe the nonfactorizability observed in data. The best description of the BI data collected in 2015 and 2016 for the five bunch crossings used is achieved consistently with a sum of three Gaussian distributions, where the narrow component has a negative weight: ρ j (x, y) = -w j,1 g j,1 (x, y) + w j,2 g j,2 (x, y) + (1 + w j,1 -w j,2 )g j,3 (x, y).
The vertex resolution is determined from data as part of the fitting procedure. The bunch profiles ρ j (x, y, z), parameterized per beam j, are the sum of three individual Gaussian distributions g j,1. 3 (x, y, z) with common mean, but arbitrary width and orientation parameters (referred to as "bunch parameters" in the following): ρ j (x, y, z) = w j,1 g j,1 (x, y, z) + w j,2 g j,2 (x, y, z) + (1w j,1w j,2 )g j,3 (x, y, z). The overlap integral of Eq. (  23 ) is evaluated at each scan step to predict the true luminosity and the geometry of the luminous region for a given set of bunch parameters. In this calculation, we consider the impact of beam-beam effects, LSC, and orbit drifts. The bunch parameters are then adjusted according to a χ 2 minimization procedure to determine the best-fit centroid position, orientation, and the widths (corrected for the primary vertex resolution) of the luminous region measured at each step of a BI or vdM scan.