dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure1. Dose-response of antibody activity in the passive protection model. 6-to 8-week-old female na€ ıve CBA/N mice (n D 5/group) received an intraperitoneal injection of 200 ml test sample or PBS. Control mice received PBS. After 1 h, mice were challenged intravenously with a lethal dose of S. pneumoniae strain A66. 1 (serotype 3), and survival was monitored for 14 days. Data were analyzed using logistic regression with probit link under PROC GLIMMIX in SAS version 8. 2 to determine the ED50. In each plot, circles indicate survival data for individual samples, and the best fit regression is shown as a green line, with the upper and lower limits of the 95% confidence interval shown as yellow and red lines, respectively. The horizontal line indicates a median response of 50% survival. In (A), mice were injected with 2.
8-27. 5 EU (1-10 mg) of purified anti-PhtD antibody or PBS. Survival data were from 4 passive protection experiments, which included 13 survival points. In five of the 13 cases (206, 275, 687, 1375, 2063, and 2750 EU/ml), the passive protection experiment was performed twice, and for 2 of these (275 and 2750 EU/ml), the points overlap and appear as a single data point. Protection experiments for 3 cases (43, 435, and 2178 EU/ml) were not repeated and were included to provide a more appropriate range of antibody concentrations. In (B), mice were injected with 1:20 to 1:60 postimmune sera. To account for the baseline levels of protection seen in the pre-immune sera, the analysis was corrected for over-dispersion using PROC GENMOD with the SCALE D option in SAS version 9. 13 and based on survival data from mice that received pre-and post-immune sera.
In (A), mice were injected with 2. 8-27. 5 EU (1-10 mg) of purified anti-PhtD antibody or PBS. Survival data were from 4 passive protection experiments, which included 13 survival points. In five of the 13 cases (206, 275, 687, 1375, 2063, and 2750 EU/ml), the passive protection experiment was performed twice, and for 2 of these (275 and 2750 EU/ml), the points overlap and appear as a single data point. Protection experiments for 3 cases (43, 435, and 2178 EU/ml) were not repeated and were included to provide a more appropriate range of antibody concentrations. In (B), mice were injected with 1:20 to 1:60 postimmune sera. To account for the baseline levels of protection seen in the pre-immune sera, the analysis was corrected for over-dispersion using PROC GENMOD with the SCALE D option in SAS version 9.
Bioinformatic identification of candidate barcode SNPs We identified candidate SNPs for our barcode by performing SNP calling with the Genome Analysis Toolkit (GATK) Unified Genotyper  [29]  using available sequence data (with quality scores of > 30) on isolates from Central America (Salvador 1  [16] ), South America (Brazil I  [20]  and Acre4 (E. Winzeler, University of California San Diego, personal communication)), Peru (IQ07  [30] , PQSJ62 (E. Winzeler, University of California San Diego, personal communication), and PQSJ96 (E. Winzeler, University of California San Diego, personal communication) and Asia (North Korea, and India VII)  [20]. Additionally, we used available SNP annotation data from sequenced isolates from Cambodia (C08, C15 and C127) and Madagascar (M08 and M19)  [19].
Primer design and assay selection We designed primer pairs using the LightScanner primer design software version 2. 0 (BioFire Defense, U. S. A. ). The software uses HRM design parameters including: primer length of 18-28 nucleotides, melting temperature (T m ) of 58-60°C, GC content of 40-60%, and amplicon size of < 60 base pairs (bp). We then checked the primer pairs for the potential to form primer-dimers or alternative amplicons using Genious software version 6. 1 (Biomatters Ltd, New Zealand). If the reaction specificity was acceptable, we screened the primers for species-specificity to P. vivax using a BLAST genomic database search (National Library of Medicine (NLM) U. S. A).
To improve the sensitivity as well as the signal-to-noise ratio of the assays in clinical samples, we identified the optimal primer pair concentration for each assay by performing an optimization matrix. We evaluated five different primer concentrations (0. 1 μM, 0. 2 μM, 0. 3 μM, 0. 4 μM, and 0. 5 μM) and their combinations, leaving all other reaction conditions unchanged. Following amplification, we evaluated the amplicons for the designed product size as described above. Next, we verified genotyping calls for each assay by melt curve analysis using the Eco Real-Time PCR System software version 4. 1 (PCRmax, Staffordshire, UK) or the Applied Biosystems ViiA 7 Real-Time PCR System (Life Technologies, Grand Island, NY, USA). We considered a primer concentration to be optimal when the amplification: (1) resulted in an amplicon of the correct size, (2) performed at a cycle threshold (C t ) less than 30 (as anything greater than 30 could generate unreliable melting profiles  [33] ), and (3) produced a single peak in the melt profile with no primer-dimer artifacts identified by Eco Real-Time PCR System software version 4.
We placed a seal on top of the PCR plate with a roller and then centrifuged at 1000 RPM for 1 min. We optimized this protocol to be performed in either a 48-well plate in the Eco Real-Time PCR System or in a 384-well plate in the Applied Biosystems ViiA 7 Real-Time PCR System. We used HRM software on the Eco Real-Time PCR System or the Applied Biosystems ViiA 7 Real-Time PCR System for HRM genotyping analyses. See supplemental materials S1 File for the full barcode assay protocol.
Principal component analysis (PCA) We performed PCA with the program SmartPCA in the Eigensoft package  [36]  on the four populations (Brazil, Sri Lanka, Ethiopia, and French Guiana) together and in pairs. In addition, we performed PCA on the four populations using only the data from a reduced set of SNP assays (either 14 or 28), where we populated the barcode subsets with the assays with the highest AMAF values. We assessed the significance of PCA results using the percent of variance (POV) explained, which is calculated as the sum of the eigenvalues corresponding to the first and second principal components over the sum of all eigenvalues.
All genotype calls (504 out of 504 SNP calls) were concordant across duplicates and on both genotyping platforms. The variability of the T m values for each assay in duplicate was ±0. 038°C on the Eco, ±0. 046°C on the ViiA 7, and ±0. 038°C between platforms (S5 Table ). We then compared manual genotyping to the automatic genotyping feature of the ViiA 7. The ViiA 7 genotyping software automatically genotyped 98% (494 out of 504 SNP calls) of the SNP calls accurately. We successfully genotyped the 10 missed calls by manual inspection. We evaluated the overall reproducibility of the 42 assays in our pilot screen by evaluating the variability of the T m values across duplicates for all monogenomic calls from the 87 clinical samples and control samples. The variability of the T m values across duplicates for monogenomic calls was ±0.
Introduction The extensive use of imaging techniques to investigate brain diseases and the need to outline specific region of interests (ROIs) for quantitative analysis emphasize the importance of accurate and robust segmentation methods. Accurate tracing of deep brain structures, such as the thalamus and hippocampus, requires a high degree of expertise and preferably standardized outlining protocols. Even though an acceptable intra-and inter-rater reliability can be achieved using standardized protocols  [1] , manual segmentation is very time consuming. In large datasets, segmentation can become a bottleneck in post-processing and data analysis. Moreover, manual region outlining is prone to inconsistencies. Automatic or semi-automatic segmentation methods have the potential to solve these issues.
Several software solutions for automatic segmentation are publicly available. Functional MRI of the Brain (FMRIB) Software Library (FSL) and Freesurfer are tools frequently used for segmentation and appear to be reasonably reliable  [2, 3]. However, there are still a potential to improve the automatic segmentation methods, especially in longitudinal studies  [4]  and for diseases that cause small structural changes. Novel segmentation methods utilize redundancy in images to exploit a representative image library with corresponding validated structure labels  [5] [6] [7]. These methods are called non-local means patch-based segmentation (NLM-PBS), since similar image patches are searched for in a non-local fashion, i. e. spatially located in a neighborhood around the target structure. NLM-PBS has been shown to be superior to conventional atlas-based techniques and even to other library-based methods  [5, 6].
ACKNOWLEDGMENTS We thank  Eric Marino  for maintaining the Imaging Resource used in this study,  Patrick Reeves  for generating the expression plasmid for LifeAct, and members of our laboratory for helpful discussions. C. K. was a recipient of a  Helen Hay Whitney Foundation  Fellowship. This work was supported in part by  National Institutes of Health  grants  GM-075252  (to T. K. ) and  U54 AI057159  ( New England Regional Center of Excellence in Biodefense and Emerging Infectious Disease, Core Imaging Facility ). B. -C. C. , W. R. L, and E. B. were funded by the  Howard Hughes Medical Institute. T. K. gratefully acknowledges support from the  Janelia Visitor Program.
Patients and Methods We have retrospectively analyzed data of all patients receiving an allogeneic HSCT in our institution between 1986 and 2006 for hematologic malignancies, alive without relapse at 1 year after HSCT. This inclusion criterion was selected to eliminate early posttransplant MC reflecting early relapse. Furthermore, some reports have already demonstrated the role of posttransplant MC at 3 months  [10]  or at 6 months  [11]  after HSCT as a predictor of relapse in hematopoietic malignancies. T-cell depletion was done in all patients with low risk disease defined as first complete remission (CR1), or first chronic phase (CP1) for chronic myeloid leukemia, and for most patients with intermediate and advanced risk disease but not for patients with active leukemia at the time of transplantation.
In Section 3 a detailed description of the current approach is presented. Section 4 evaluates the proposed approach on both synthetic and real data. Finally, concluding remarks are given in Section 5. Related to single-depth-image pose estimation, Ye et al. (Ye et al. , 2011)  proposed a pipeline to combine pose detection with pose refinement. To do so, the depth map is used to find a similar pose within a database of prior full-body surface mesh models. Lehment et al. (Lehment et al. , 2010)  considered 3-D point clouds extracted from depth maps to fit a mesh of a cylinder-based stickman model using Annealing Particle Filters (APF). However, the aforementioned methods require a GPU-based implementation. Shotton et al. (Shotton et al. , 2013)  introduced two superreal-time approaches to predict the positions of body joints using a large and varied synthetic set of training images.
The equation of the 3-D line in which the base point A lies is thus defined from the centroid of P , i. e. , p = 1 k • ∑ k i=1 p i , ∀ p i ∈ P and the normalized vector v. The third coordinate of A, i. e. , A z , is retrieved from the human body proportions denoted in Fig. 1  (b), whereas the two missing coordinates result from: A x = v x • t + px , A y = v y • t + py , (1) with t = (A zpz )/(v zpz ). Note that the initialization of the skeleton model and hence, the body clustering, are strongly dependent on these two parameters. Indeed, a wrong direction of the torso entails to a wrong initialization of the model and thus to an erroneous pose in space. To increase the accuracy and robustness of the torso direction, only those 3-D points that belong to the torso are considered. To do so, the torso 3-D points are classified by fitting a cylinder, a simplified geometric model that can be quickly fitted to the dataset using Random Sample Consensus (RANSAC)  (Fischler and Bolles, 1981).
EXPERIMENTAL RESULTS The proposed human pose estimation approach is evaluated on both real and synthetic data. All reported results have been obtained using a Mobile Intel R QM67 Express Chipset with an integrated graphic card Intel R HD Graphics 3000. The proposed approach has been implemented in C++ language using the OpenCV  (Bradski and Kaehler, 2008)  and PCL  (PCL, 2014)  libraries. Real data has been recorded using a multi-view sensing system composed by 2 consumer-accessible RGB-D cameras, i. e. , the Asus Xtion Pro Live camera, with opposed fieldof-views, i. e. , with no data overlapping. Nevertheless, the relationship between the two cameras was determined by a calibration step, using the stereo calibration implementation available in OpenCV  (Bradski and Kaehler, 2008). Better registration approaches based on ICP, bundle adjustment or the combination of both can also be considered.
However, the current approach perfectly estimates the human pose on such a coarse registered point clouds, handling large portions of missing data as well as registration inaccuracies. Synthetic data has been generated using V-Rep (VRE, 2014), a very versatile robot simulator tool in which the user can replicate real scenarios. A simulated scene has been created to generate the test cases with four virtual Kinect cameras installed in the top corners of the virtual scene. It can be observed that the data is perfectly registered since a full knowledge of the relationship between the cameras and their calibration parameters is known. Consequently, synthetic data has been considered as ground truth data in the next evaluations. All datasets have been voxelized to account for point redundancy after data registration.
Voxelization stands for a discrete approximation of 3-D objects into a volumetric representation  (Garcia and Ottersten, 2014a). Some visual results on both synthetic and real body-scanned datasets are shown in the Fig. 4. The first row presents the considered datasets highlighting the estimated orientation of the torso (green line) and the extracted base point (purple dot). Second row shows the initialization of the predefined skeleton model. The clustering of all body parts is shown in the third row whereas the fitted skeleton model is shown in last row, from which results the human pose. In Fig. 5 , the estimated poses of the synthetic Bill model and a real dataset on some selected point-cloud video frames are shown. These results show that the method is able to accurately estimate the body pose of different body configurations of an upright person.
Robustness to Noise Next, the robustness of the current approach against noise is evaluated. To do so, the synthetic dataset of Bill has been considered, to which it has been applied a zero-mean Gaussian noise with standard deviation σ ∈ [0. 5, 5] cm, i. e. , N (0, σ 2 ). In order to increase the reliability of this evaluation, the noise has been added to the depth maps acquired by each virtual Kinect camera, i. e. , before being transformed to point clouds. Fig. 6  depicts the error between the location of the resulting skeleton nodes from the noisefree 3-D model (considered as ground truth), and their respective ones from the noisy models. The labels of the nodes correspond to those presented in Fig. 1. As shown in Fig. 7 , the proposed approach is able to estimate the Bill pose for all σ values.
Runtime and Performance Analysis Table  1  reports the time consumption to estimate the human pose for each of the datasets presented in Fig. 4. Note that most of the time is dedicated to cluster the 3-D points to estimate the direction of the torso. However, it is important to recall that this operation might be done only once in the first video frame, as discussed in Section 3. If a better performance is required, one can increase the voxel size to represent the body-scanned point cloud. Thus, a voxel size of 1 cm 3 has been considered when evaluating the approach. However, the performance is significantly increased by increasing the voxel size, whereas the human pose remains accurately estimated, as shown in Table  2. The only constraint of this implementation is that there has to be a minimum distance of 5 cm between two 3-D points to be clustered within the same object.
CONCLUDING REMARKS A scheme to estimate the human pose from bodyscanned point cloud datasets has been described. Us-ing PCA and prior knowledge on human body proportions, a predefined skeleton model is initialized and then fitted to the given point cloud by an iterative process based on the theory of Expectation Maximization. From the experiments, it is shown that a good estimate is achieved in both synthetic and real datasets, and even in the presence of high noise within the depth measurements. As future work, the current approach will be extended to alternative body configurations other than upright. Alternative techniques to segment the 3-D points belonging to the torso (used to estimate its direction) will be further investigated in order to address the limitations given by the cylindrical model, such as the tuning of the model parameters.
Time consumption analysis for human pose estimation on the datasets presented in Fig. 4 (units are in ms). Reported are the mean values taken over 100 iterations. 1 st row, Nilin Combat dataset (12655 points). 2 nd row, Iron Man dataset (21975 points). 3 rd row, Jilliam dataset (14855 points). 4 th row, Frederic dataset (19675 points).
9×150 mm 2. Vitamin E was determined at 290 nm, cryptoxanthin, β-carotene and lycopene were determined at 460 nm; and lutein/zeaxanthin and astaxanthin were determined at 450 nm. The flow rate was fixed at 1 mL min -1. Identification of peaks was based on the comparison of their retention times and diode array spectra, taken during analysis, with the corresponding data obtained by analysing standard compounds.
Results ICA detects a PD-specific pattern of deformation DBM was used as the measure of local brain atrophy. It is a measure of the change in the shape of each voxel that results from applying non-linear spatial normalization of the entire brain to a standard template  (Aubert-Broche et al. , 2013). For each subject, we obtained one parametric image of MRIdetermined voxel-wise difference in volume, compared to the template brain. ICA was conducted on these DBM images using FSL MELODIC software  (Beckmann and Smith, 2004). No constraint was imposed on the number of components, and probabilistic ICA estimated 30 independent components of deformation in the PPMI data set (PD patients and controls combined). Each ICA component consists of a spatial map and the average deformation value in that map for each subject.
One theory suggests that Parkinson's disease is caused by a toxic protein that is able to spread from neuron to neuron. Recent advances in brain imaging have made it possible to map networks in the living human brain-the so-called brain connectome. These networks could form the 'highways' through which a disease-causing agent might spread. The Parkinson's Progression Markers Initiative (PPMI) is a large study that collects data from hundreds of people in an effort to identify the causes of Parkinson's disease. Zeighami et al. have now analyzed MRI scans that were collected as part of this initiative, which show the structure of the brains of 230 people in the early stages of Parkinson's disease. Comparing these scans to those from age-matched healthy individuals allowed Zeighami et al. to identify the set of brain regions that show atrophy in the early stages of Parkinson's disease.
Discussion Pattern of atrophy in PD The combination of DBM and ICA of an unprecedentedly large set of MRI data at a magnetic field strength of 3T allowed us to map out the brain regions affected in de novo PD. Patients included in this study were diagnosed on average 7 months prior to the investigation and had no evidence of dementia (Table  4 ). Most MRI studies to date using T1-weighted images in PD have reported normal The brain was parcellated into 112 regions (Figure  4 -figure supplement 1). SN was chosen a priori as the region of interest, and the functional and structural connectivities between each given region and SN were calculated. The statistical difference (t-score) between the average deformation in PD and controls in each region was used as an atrophy measure. Using correlation, the relationship between regional atrophy and both regional functional connectivity with SN using resting-state fMRI (rsfMRI) (left) and regional anatomical distance using diffusionweighted imaging (DW-MRI) (right) was examined.
, 2010; Silbert and Kaye, 2010; Pyatigorskaya et al. , 2014). Similarly, hippocampus and amygdala atrophy are typically linked to cognitive impairment (for review see  Silbert and Kaye, 2010; Ibarretxe-Bilbao et al. , 2011). With regard to cortical areas, studies using voxel-based morphometry (VBM), DBM, or cortical thickness measurements have reported significant differences between PD and controls in parahippocampal gyrus, inferior, middle and frontal temporal gyrus, parietal lobe and occipital lobe, but, once again, typically in advanced patients with cognitive impairment  (Silbert and Kaye, 2010; Ibarretxe-Bilbao et al. , 2011; Camicioli, 2013; Pyatigorskaya et al. , 2014). Our finding of an atrophy network including brainstem and subcortical regions in these de novo patients is likely due to the larger number of participants available through the PPMI database compared to previous relatively underpowered investigations.
PPMI data set rsfMRI We acquired rsfMRI in 51 healthy, right-handed volunteers (mean age: 23. 6 ± 5. 9, range: 18-47, 32 men, 63%). None of the subjects reported a history of drug abuse, neurological or psychiatric disorder. The experimental protocol was reviewed and approved by Research Ethics Board of MNI. All subjects gave informed consent. Scans were acquired using a Siemens MAGNETOM Trio 3T MRI system at the MNI. High-resolution, T1-weighted, three-dimensional volume acquisition for anatomic localization (1-mm 3 voxel size) and resting-state echoplanar T2*-weighted images with blood oxygenation level-dependent (BOLD) contrast (3. 5-mm isotropic voxels, TE 30 ms, TR 2 s, flip angle 90˚) were acquired from all participants. Each resting-state scan was 5-min long (150 vol). Subjects were instructed to rest quietly with eyes open.
, 1994). The resulting transformation was applied to the MR image to resample it onto a 1-mm 3 voxel grid and bring it into MNI space. (2) A hierarchical non-linear registration was performed on the linearly resampled scan to align it with the MNI152-2009c template  (Collins and Evans, 1997). The resulting non-linear transformation field was inverted to generate a map of the deformations in template (MNI) space for each subject. Quality control was performed on each individual data set: the brain mask, and linear and nonlinear registrations were visually inspected, and data sets with faulty registration were discarded. After the registration procedure, for each MRI and for each position in the brain x = x1, x2, x3, we obtain a displacement value in each direction to generate a field of vectors: U(x) = (u1(x), u2(x), u3(x)); that is, during the registration procedure position, x is displaced to the new position x + U(x) in the template space.