dataesr/scientific-paragraphs-categorization huggingface dataset:
On average, PCR-HRM tests have been calculated to be five times cheaper and three times faster than other types of analysis such as multilocus sequence typing (MLST) and RFLP  [48]. In the present study, we investigated Leishmania infection in a range of field samples taken from three hedgehogs, seven Meriones and 12 dogs captured in the same governorate of El Kef, situated in North West Tunisia, and constituting an active focus of SCL due to L. infantum. We used HRM PCR, which is a real-time PCR based on SYBR Green dye, with the Roche LightCycler ® 480 machine equipped with gene scanning software (version 1. 5. 0). Development and optimization of PCR-HRM targeting the 7SL RNA and HSP70 genes required modification of several reaction parameters, including a touchdown protocol, which ultimately improved amplification of the 7SL RNA gene targets.
Cores and Sampling Two marine sediment cores (core SW104_ND2: length = 116 cm, and core ND2_2013: length = 452 cm) have been used to obtain the high-resolution composite pollen record SW104_ND2-ND2 described in this paper, hereinafter named ND2. The two records were collected in the same site from the Sicilian continental shelf, about 19 km from the coast in the Malta Channel, at a water depth of 89 m b. s. l. (36°33′52″ N, 14°52′59″ E). The cores were recovered during the NEXTDATA 2013 oceanic cruise onboard of the R/V CNR-Urania using the Kullenberg gravity corer (core ND2_2013) and the SW104 gravity corer system (core SW104_ND2), preserving the water-sediments interface. The sediment cores were wrapped and regularly sub-sampled on-site for palynological analysis. The analyzed composite core ND2 was constructed using the distribution pattern of planktonic foraminifera  [28].
Cores and Sampling Two marine sediment cores (core SW104_ND2: length = 116 cm, and core ND2_2013: length = 452 cm) have been used to obtain the high-resolution composite pollen record SW104_ND2-ND2 described in this paper, hereinafter named ND2. The two records were collected in the same site from the Sicilian continental shelf, about 19 km from the coast in the Malta Channel, at a water depth of 89 m b. s. l. (36 • 33 52 N, 14 • 52 59 E). The cores were recovered during the NEXTDATA 2013 oceanic cruise onboard of the R/V CNR-Urania using the Kullenberg gravity corer (core ND2_2013) and the SW104 gravity corer system (core SW104_ND2), preserving the water-sediments interface. The sediment cores were wrapped and regularly sub-sampled on-site for palynological analysis. The analyzed composite core ND2 was constructed using the distribution pattern of planktonic foraminifera  [28].
Pollen Analysis Chemical treatment was performed on 94 samples using HCl (37%), HF (40%), and NaOH (20%), according to the standard procedure  [33]. Pollen concentration values were estimated by adding Lycopodium tablets to a known weight of sediment in each sample. No heavy liquids were used during sample preparation. The identification of pollen grains and non-pollen palynomorphs (NPPs) was carried out by means of light microscopy (400 to 640 magnifications) using pollen morphology atlases  [34, 35]  and online databases  [36] [37] [38]. Pollen counts were elaborated to obtain percentage and concentration values for each pollen type detected. The main percentage sum was based on terrestrial pollen, excluding aquatics, fern spores, and NPPs. A pollen diagram was generated representing pollen percentages in relation to the age-depth model obtained from radiocarbon chronology and short-lived radionuclides analysis on the stratigraphic sedimentary sequence (Figure  3a, b ).
The pollen diagram was plotted and sub-divided into four statistically significant pollen-assemblage zones, numbered from the base upward and prefixed by the site abbreviation ND2, using the computer software Psimpoll 4. 27  [39]. Constrained Cluster Analysis by Sum Squares (CONISS) was used to identify the number of statistically significant pollen assemblage zones (hereinafter "zones"), with a dissimilarity matrix of Euclidean distances, involving all the pollen taxa exceeding 2%. This agglomerative algorithm searches for the most similar stratigraphically adjacent pair of samples using the dissimilarity matrix of all pairwise combinations of samples. A sum of squares is calculated from each cluster after merging a pair of samples into one cluster, and it is recalculated as clusters are merged.
Bioluminescence imaging Immediately after cell injection, the animals received a SC injection of D-Luciferin (10 μL/g [body weight] of a 47 mM stock solution). 20 min later, the animals were imaged with an IVIS Spectrum instrument (Perkin Elmer). All data is displayed in radiance (photons/second/centimeter 2 /steradian), where the signal intensity scale is normalised to the acquisition conditions.
Mice were imaged at 34°C. In mice receiving subcutaneous hUC-MSC injection, scans were acquired at the site of injection at 61 wavelengths (680 nm to 980 nm in 5 nm steps) in 1mm slices. 10 frames per wavelength were measured and averaged. In mice receiving intravenous hUC-MSC injection, scans were acquired at the lungs at 61 wavelengths (680 nm to 980 nm in 5nm steps) in 1 mm slices. Additionally, images were acquired through the full volume of all animals at 8 wavelengths (660, 700, 730, 750, 760, 800, 850, 900 nm) in 1 mm slices. For image processing, the ViewMSOT 4. 0. 1. 34 (iThera Medical, Germany) was used. Data were reconstructed with the back-projection algorithm. Multispectral unmixing was performed using the linear regression algorithm. Images were unmixed for haemoglobin, oxyhaemoglobin, melanin, and the GNR MSOT spectrum.
Computed Tomography Agar phantoms with inserts, as used for MSOT, were imaged using an aluminium filter 0. 5 mm thick or a 0. 06 mm copper filter with an applied X-ray tube voltage of 90 kV in a Quantum GX micro CT (Rigaku Corporation). Images were acquired with a field of view (FOV) of 25 mm giving a voxel size of 50 µm. After MSOT imaging, the mice were culled and their carcasses were imaged using an aluminium filter 0. 5 mm thick and an applied X-ray tube voltage of 90 kV with the same instrument. Images were acquired with a FOV of 25 mm giving a voxel size of 50 µm. Surface-rendered 3D models were constructed for 3D viewing of the analysed mice. Volume rendered 3D images were generated using the Quantum GX software version 3. 0. 39. 5100.
Acknowledgements and funding statement In vivo imaging data in this article were obtained in the  Centre for Preclinical Imaging (CPI) of the University of Liverpool. The CPI has been funded by a  Medical Research Council (MRC)  grant ( MR/L012707/1 ). We thank  Ms Alison Beckett  ( Biomedical EM Unit, University of Liverpool ) for assistance with electron microscopy imaging,  Dr Tim Devling  and  Dr Neal Burton  (iThera Medical) for expert advice on MSOT imaging. MSC transduction and sorting was carried out by  Mr Francesco Amadeo ,  NHSBT. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 813839  and from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No.
Severe neurological morbidity at 2 years was de ned as developmental delay requiring speci c multidisciplinary care in rehabilitation centers or in institutions for children with special needs. Non-severe morbidity was graded as moderate or minor as de ned in the Supplementary Methods. Demographic, surgical, neonatal and follow-up data were collected retrospectively from each infant's medical records. Gestational age was de ned using ultrasound data from the rst trimester of pregnancy or, if not available, the date of the last menstrual period. The diagnosis of intrauterine growth retardation was retained for a birth weight lower than the 10th percentile for gestational age according to the Fenton curves  [9]. Weight z-scores were de ned according to Fenton  [9]  charts in preterm infants at hospital discharge, and according to the World Health Organization charts  [10]  in term infants and at 2 years.
Ethics The processing of personal data was carried out in accordance with the EU Regulation 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard the processing of personal data and on the free circulation of such data. The study was declared to the National Data Protection Authority (Commission Nationale de l'Informatique et des Libertés) as meeting the reference methodology (MR-004) and registered in the institutional register of data processing of the Assistance Publique-Hopitaux de Paris under the number 2021-0312112531. Data were anonymised for processing. Patients and their families were informed by a note at the end of all reports that their child's clinical data could be collected for research purposes and of their right to decline such research.
The second objective was to test the algorithm for proven and young (APY) in PEC computations. With large datasets, it is impossible to handle the full PEC matrix, thus the third objective was to examine the minimum number of genotyped animals needed in PEC computations to achieve IP accuracies that are equivalent to GEBV accuracies. Results: Correlations between GEBV and IP for the validation animals using SNP effects from ssGBLUP evaluations were ≥ 0. 99. When all available genotyped animals were used for PEC computations, correlations between GEBV and IP accuracy were ≥ 0. 99. In addition, IP accuracies were compatible with GEBV accuracies either with direct inversion of the genomic relationship matrix (G) or using the algorithm for proven and young (APY) to obtain the inverse of G. As the number of genotyped animals included in the PEC computations decreased from around 55,000 to 15,000, correlations were still ≥ 0.
Data and model Data for this study were provided by the American Angus Association (Saint Joseph, MO) and included 230,639 animals in the pedigree and 38,000 post-weaning gain (PWG) phenotypes. Genotypes for 39,774 SNPs after quality control, were available for 60,000 animals born up to 2018. To mimic a real situation, genotyped animals were split into "old" (N = 54,533) and "validation" (N = 5467), i. e. , young, genotyped animals predicted through IP. Validation animals were genotyped animals that were born in 2018 and for which their pedigree, progeny, and own records were omitted from the data. Validation animals were first excluded from ssGBLUP, and IP and their accuracies obtained; then, these IP accuracies were compared to accuracies when these same validation animals were included in ssGBLUP.
Benchmark GEBV and accuracy A ssGBLUP evaluation using the complete data (i. e. , including validation animals) was run to obtain benchmark GEBV accuracy ( ACC GEBV ) for validation animals. The ACC GEBV for animal j was calculated based on PEV from the inverse of the LHS of MME (8) as follows: Let the inverse of the coefficient matrix of the MME (8) be: where PEV j is the diagonal element j in the prediction error variance matrix C uu.
Indirect predictions and accuracy Before calculating IP, SNP effects from ssGBLUP were obtained as described in Wang et al. [8] , using the POSTGSF90 program  [27]. Recently, Legarra et al. [28]  showed that under ssGBLUP, blending and tuning parameters need to be taken into account when backsolving GEBV into SNP effects ( a): where u is a vector of GEBV from an ssGBLUP evalua- tion with the reduced data that does not include data for  (7) m nn,i = g ii -G ic G -1 cc G ci. ( 8 ) X ′ R -1 X X ′ R -1 W W ′ R -1 X W ′ R -1 W + H -1 σ -2 u β û = X ′ R -1 y W ′ R -1 y , (9) C -1 = C ββ C βu C βu C uu. ( 10 ) Then, ACC GEBV j = 1 - PEV j σ 2 u , ( 11 ) a| u = (1 -α)b 1 2 p i 1 -p i Z ′ G -1 u, validation animals. Once SNP effects are available, IP can be calculated as IP = Z validation a , which reflects the marker-based predictions  [28].
Once SNP PEC is available, accuracy for IP for animal j ( ACC IP j ) can be computed as: (12) C -1 g = C ββ C βg C βg C gg. ( 13 ) Then, ACC IP j = 1 - PEV j σ 2 u , (14) var a = PEC = var (1 -α)b 1 2 p i 1 -p i Z ′ G -1 u (15) = (1 -α)b 1 2�p i (1 -p i ) Z ′ G -1 var u (1 -α)b 1 2�p i (1 -p i ) Z ′ G -1 , (16) = (1 -α)b 1 2 p i (1 -p i ) Z ′ G -1 var(u) -var u -u G -1 Z 1 2 p i (1 -p i ) b(1 -α). ( 17 ) var a = PEC = (1 -α)b 1 2 p i 1 -p i Z ′ G -1 Gσ 2 u -C u 2 u 2 G -1 Z 1 2 p i 1 -p i b(1 -α). ( 18 ) var a = PEC = (1 -α)b 1 2 p i 1 -p i (Z ′ G -1 Zσ 2 u -Z ′ G -1 C u 2 u 2 G -1 Z) 1 2 p i 1 -p i b(1 -α). While the accuracy of IP can be easily obtained with small datasets, obtaining C u 2 u 2 becomes impractical in large-scale evaluations because of the number of genotyped animals. To overcome this limitation, the dimensionality of genomic information was exploited by using the APY algorithm to compute a sparser G -1.
Implementation The implementation required changes in three existing programs from the BLUPF90 software suite  [27]. Most of the changes followed those for the computation of p-values for SNP effects as described in  [19]. In a nutshell, the modifications in BLUPF90 allows storing the inverse of the LHS of the ssGBLUP MME in binary format. For that, OPTION snp_var is required. When this option is also used in POSTGSF90, the  (19 ) ACC IP j = 1 - (1 -α)b z j var a z ′ j σ 2 u. program reads the binary file, extracts the coefficients for genotyped animals ( C u 2 u 2 ), and applies Eq. (  18 ). The main difference compared to the calculation of p-values is that POSTGSF90 saves all the elements of the SNP PEC matrix in binary format for further computations of ACC IP by PREDF90, whereas only SNP PEV (i.
5) hacc_prog: genotypes and phenotypes for high accuracy animals plus their progeny phenotypes. The scenarios S1 and S2 (direct and apy) used all the data available, and they reflected the case when all animals in the evaluation are used to calculate SNP PEC. These scenarios also served as a test to compare the direct or APY G -1 in the PEC computations. The other scenar- ios (under S3) represented a situation when only a subset of the animals is used. In scenario S3. 3 (hacc), 15,000 animals with the highest accuracy based on the benchmark ( ACC GEBV ) were selected. The number of ani- mals with genotypes, phenotypes, and pedigree for each scenario and dataset is in Table  1. Once SNP PEC were available, ACC IP was calculated, in PREDF90, for valida- tion animals in each scenario and dataset.
99, the regression coef- ficient ( b 1 ) decreased, being as low as 0. 86 when blend- ing was up to 30% of A 22 (Table  3 ). Ben Zaabza et al. [29]  pointed out the importance of accounting for the residual polygenic effect when its proportion exceeds 20%, and our results show that while our current formulas and implementation are robust for smaller blending proportions, fine tuning is needed to account for higher proportions of blending or when a residual polygenic effect is explicitly included in the model. The computing requirements for BLUPF90, POSTGSF90, and PREDF90 for the direct and apy Table  2  Descriptive statistics for ACC GEBV and ACC IP for all scenarios and datasets a ABS difference: absolute difference between ACC GEBV and ACC IP direct: all genotyped animals (N = 54,533) and phenotypes with direct G -1 ; apy: all genotyped animals (N = 54,533) and phenotypes with APY G -1 ; 50K-2K: all phenotypes and decreasing the number of genotyped animals from 50K to 2K; core: genotypes for core animals only (N = 15K) and all phenotypes; hacc: genotypes for high accuracy animals only (N = 15K) and all phenotypes; core_prog: genotypes and phenotypes for core animals plus their progeny phenotypes; hacc_prog: genotypes and phenotypes for high accuracy animals plus their progeny phenotypes    19 )).
If there is no dispersion, b 1 equals 1, and deviations from one indicate either under or overestimation of ACC IP. Regression coefficient and intercept for each scenario are presented in Figs. 2  and 3. No bias or scale differences were found when all genotyped animals (except for the validation) were used to calculate PEC in the scenarios direct and apy. However, reducing the number of genotyped animals to 20K increased inflation, as b 1 dropped from 0. 98 to 0. 74 (Fig. 2 ). At the same time, bias increased from 0. 02 to 0. 25 (Fig. 3 ), with a shift towards underestimation. Although an ad-hoc scaling factor based on tests with smaller datasets can correct the overall underestimation or overestimation, it is more difficult to reduce the inflation.
Furthermore, using only own and progeny records, did not result in increased dispersion compared to using complete data and pedigree information (core vs core_prog and hacc vs hacc_prog scenarios in Fig. 3 ). It is worth noting that the number of records and animals in the pedigree was nearly halved between the core and core_prog scenarios. This indicates that including a sufficient number of genotyped animals with own phenotypes, and adding their phenotyped progeny are enough to account for the contributions due to phenotypes and pedigrees as well as G -1 -A -1 22 and to obtain reasonable SNP PEC for IP accuracy. For future research with larger datasets, groups of genotyped animals with many phenotyped progeny could be a good target when trying to reduce computation costs in obtaining SNP PEC.
[12]  for a SNP-BLUP model, obtaining SNP PEC from ssGBLUP may be challenging because it depends on the number of animals rather than the number of SNPs included in the system of equations, therefore reducing the number of genotyped animals for PEC computations is critical. Methods to approximate PEC could be likewise helpful. More research is needed to investigate whether SNP PEC computed from a smaller subset of genotyped animals can be used to approximate ACC IP with a larger number of genotyped animals and to account for large proportions of blending or the residual polygenic effect. Such tests could be hard to accomplish because obtaining ACC GEBV based on PEV as a benchmark is not fea- sible for large datasets, although approximations could be used. In addition, to be able to use smaller subsets of animals, fine tuning is still needed to refine the methods and to define which animals should be used in PEC computations to avoid biases on IP accuracy.
Patient dataset A retrospective dataset of 222 patients with ASD was used, the same cohort described in the previous clinical study in which the Global Alignment and Proportion (GAP) score was developed and validated  [9]. The data were collected within the multicenter study of the European Spine Study Group (ESSG), a collaboration of 6 clinical sites across Europe. Enrollment criteria were: age of ≥ 18 years and at least 1 of the following: coronal Cobb angle ≥ 20°, sagittal vertical axis of ≥ 5 cm, pelvic tilt of ≥ 25°, or thoracic kyphosis of ≥ 60°. Out of this ESSG database, only cases of operative treatment with posterior fusion at ≥ 4 levels and follow-up data available at ≥ 2 years postoperatively were included in the GAP study  [9]  and in the current work. Further exclusion criteria, related to the simulation nature of the study were: upper instrumented level at T1 or more superior (unsuitable to be simulated with the model established for investigating loads at T1/T2-L5/S1), missing values of postural measures needed to create a patient posture model within the defined work frame, or model construction and simulation errors.
The dataset included patient demographic parameters, surgical treatment details, prior fusion, occurrence and type of mechanical complications, as well as sagittal alignment measures obtained from preoperative and 6 weeks postoperative long lateral radiographs. The sagittal alignment parameters included: pelvic incidence (PI), sacral slope (SS), global tilt (GT), lumbar lordosis L1-S1 and thoracic kyphosis T2-T12 angles, and angles for estimating lumbar and thoracic curve distribution: L4-S1, T10-L2, T5-T12, and T2-T5 (Fig. 1 ). Based on the sagittal parameters, GAP score and its parameters (relative pelvic version, RPV; relative lumbar lordosis, RLL; lordosis distribution index, LDI; and relative spinopelvic alignment, RSA) were calculated for each participant (Fig. 2 ). GAP score can be used to categorize sagittal alignment as: postoperatively proportioned, GAP ≤ 2; moderately disproportioned, 3 ≤ GAP ≤ 6; and severely disproportioned, GAP ≥ 7  [9].
The spine model A musculoskeletal model of the spine, including articulated thorax with ribcage, previously developed and validated in the AnyBody Modeling System software was used  [35, 36]. In this model, the pelvis is constrained to the ground and rigidly connected to the sacrum. The articulations between thoracic and lumbar vertebrae are modeled as spherical joints, and articulations between ribs and vertebrae are approximated as revolute joints. The elastic effects of intervertebral disc, paraspinal ligaments and ribcage structures are modeled with passive stiffness elements. This model has been validated by comparing predicted compressive forces to reported in vivo intradiscal pressure measurements at various postures in healthy volunteers, as described in more detail in previous works  [35] [36] [37] [38].
Statistical analyses Descriptive data are presented as means ± standard deviations (SD). Wilcoxon signed-rank tests were performed to examine differences between preop and postop measures. Postoperative changes in model-predicted compressive and shear forces estimated for the proximal segment (with respect to the corresponding segment in the preoperative condition) were calculated. Their associations with sagittal alignment measures and the associations between sagittal alignment measures and spinal loads were analyzed with Pearson's r correlation coefficients. The significance level was set at p < 0. 05, and the Benjamini-Hochberg procedure was applied to adjust p-values for multiple hypothesis testing and control the false discovery rate at the 5% level. Analyses were performed using MATLAB (MATLAB R2018a, v.
Patient sample Out of the sample of 222 patients, 17 were excluded. Eight had an upper instrumented level at T1 or more superior. In five cases, the dataset of postural measures was incomplete. In four other cases, data were available, but posture could not be simulated (due to model limitations: assumed dimensions of vertebral bodies and intervertebral spaces causing conflict between provided sagittal alignment measures, or simulation failure to solve extreme deformity cases). This left N = 205 patients (156 female, 49 male) to be analyzed in the study. Their mean age (and standard deviation) was 52. The sample included both primary (72%) and revision (28%) surgery cases, representing a variety of fusion lengths (median number of fused motion segments = 9, range 3-16), and upper (T2-L3) and lower (T12-pelvis) instrumented levels, with T10-pelvis fusion being the most common (13%, Fig.
001). Conclusions: Our data indicates a central role for SHIP1 in OC development and activity in vitro. The low bone mass phenotype in SHIP styx/styx mice, however, may be caused by reduced bone formation or by the wasting disease and systemic inflammatory condition characteristic of SHIP1-deficient mice. This led us to investigate whether the gain-of-Cxcr4-function mutation modulates the OCL differentiation and functions. Bone and bone marrow cells were obtained from long bones of wt and mutant mice. In vitro multinucleated TRAP+ OCLs differentiation was performed from total bone marrow cells treated with M-Csf/ Rank-L. Matrix dissolution activity evaluated using osteo-assay plates led to resorbed areas quantified using Fiji/ImageJ software. For mouse gene expression, total RNAs were isolated using Trizol, reverse transcribed and tested in SYBRGreen qRT-PCR reactions (StepOne Applied-Biosystems).