dataesr/scientific-paragraphs-categorization huggingface dataset:
Activities of daily living  (23)  and Instrumental Activities of Daily Living  (24)  scores were used to evaluate the functional status of patients 3 months before (retrospective), during and 3 months after admission to hospital. These items were scored numerically, with higher numbers representing better functioning. Factors that could impact clinical presentation, i. e. , non-steroidal anti-inflammatory drug, steroid or antibiotic prescription, and use of beta blockers within 7 days before bacteremia onset, were also recorded. In addition, microbiological data (pathogens isolated from blood cultures, susceptibility patterns) and data on antimicrobial treatment (appropriateness, interval between symptom onset and treatment initiation) were collected. Patients' vital status, living place and functional status were evaluated at D7, 1 month (D30) and 3 months (D90), by telephone after discharge.
PBMC-HepaRG cell interactions decrease TNFα but not IL-10, IL-1β, IL-17 and IFNγ secretion. Because hepatocytes can act on T cell polarization, mRNA and/or supernatant levels of the pro-inflammatory cytokines IL-1β, IL-17, IFNγ and TNFα and of the anti-inflammatory cytokine IL-10 were quantified in PBMC cultures in presence or not of HepaRG cells. mRNA levels of IL-10 and IL-1β increased with PBMC-HepaRG interactions with a higher induction in PHA-stimulated versus non-activated conditions (p < 0. 05) (Fig. 5a, b ). IL-17 mRNA level was strongly up-regulated in PHA-activated co-cultures compared to other conditions (p < 0. 05) (Fig. 5c ). Cytokine supernatant levels were then quantified in PBMC monocultures and PBMC-HepaRG cell co-cultures. In HepaRG cell cultures, cytokine production was too low compared to other culture conditions or not detectable (data not shown).
PBMCs were isolated by Ficoll-Hypaque (Eurobio) density gradient centrifugation. Cells were maintained in RPMI 1640 medium (Eurobio) supplemented with 10% human AB serum (Etablissement Français du Sang, La Plaine Saint-Denis, France), 2 mM L-glutamine (Eurobio). PBMCs were activated or not with 5 µg/ml phytohemagglutinin (PHA) (Sigma-Aldrich) and added on HepaRG cells or skin fibroblasts at a ratio of 5 PBMCs for 1 HepaRG cell or 1 skin fibroblast. This ratio was based on data from the literature  8, 27. For cell culture insert assays, HepaRG cells were cultured at the bottom of a culture plate well and PBMCs were placed in Falcon ® cell-culture inserts (Corning, NY, USA) with a small-pored membrane (0. 4 µm) preventing direct cell-cell contacts but allowing the circulation of soluble factors.
PE labeled anti-HLA-DR (clone immu357, IM1639, Beckman Coulter) was used to stain HLA Class II. Corresponding isotypic antibodies labeled with the same fluorophores and from the same suppliers were used as controls. To track the PBMC proliferation, PBMCs were stained with CFSE as previously described by Quah B et al. 28  Briefly, PBMCs (10. 10 6 cells/mL) were labelled with 1 μM CFSE for 5 min at room temperature, PBMCs were then washed and cultured for 3 days in presence or not of PHA. To investigate cell viability the fluorescein isothiocyanate (FITC) annexin V apoptosis detection kit (556547, BD, Franklin Lakes, NJ, USA) were performed according to the manufacturer's instructions. Data were analyzed using Kaluza software (version 1. 2, Beckman Coulter).
Statistical Analysis All data were analysed using SAS statistical software version 9. 4 (SAS Institute Inc, Cary, NC). Graphs representation has been achieved using R 3. 0. 1 software (the R foundation, Vienna, Austria) and Graphpad Prism v7. 0 (Austria). Quantitative variables are expressed as mean ± SD in case of normal distribution or median (interquartile range) otherwise, and categorical variables are expressed as number (percentage). Normality of distributions was checked using the Shapiro-Wilk test. The Cox proportional hazard model was used to depict any correlation between preoperative (demographics / anatomic) and procedural variables of patients and a composite criterion that includes 30-day malperfusion-related complications and 30-day death. Variables having a significant test in univariate analysis (<0.
Purpose This study aimed to evaluate the accuracy and diagnostic test performance of the U-net-based segmentation method in neuromelanin magnetic resonance imaging (NM-MRI) compared to the established manual segmentation method for Parkinson's disease (PD) diagnosis. Methods NM-MRI datasets from two different 3T-scanners were used: a "principal dataset" with 122 participants and an "external validation dataset" with 24 participants, including 62 and 12 PD patients, respectively. Two radiologists performed SNpc manual segmentation. Inter-reader precision was determined using Dice coefficients. The U-net was trained with manual segmentation as ground truth and Dice coefficients used to measure accuracy. Training and validation steps were performed on the principal dataset using a 4-fold cross-validation method.
We tested the U-net on the external validation dataset. SNpc hyperintense areas were estimated from U-net and manual segmentation masks, replicating a previously validated thresholding method, and their diagnostic test performances for PD determined. Results For SNpc segmentation, U-net accuracy was comparable to inter-reader precision in the principal dataset (Dice coefficient: U-net, 0. 83 ± 0. 04; inter-reader, 0. 83 ± 0. 04), but lower in external validation dataset (Dice coefficient: U-net, 079 ± 0. 04; inter-reader, 0. 85 ± 0. 03). Diagnostic test performances for PD were comparable between U-net and manual segmentation methods in both principal (area under the receiver operating characteristic curve: U-net, 0. 950; manual, 0. 948) and external (U-net, 0. 944; manual, 0. 931) datasets.
Despite attractive diagnostic performances, the clinical applicability of this method is impeded by these timeconsuming steps, first and foremost MS; in this regard, automatized segmentation would be a significant improvement. In this context, deep learning segmentation appears as an appealing option. It uses neural networks trained to perform tasks using examples and to grasp intricate structures in datasets  [22]. Specifically, convolutional neural networks (CNNs) have significantly advanced computerized image recognition performance. They have successfully been applied to the neuroradiology field to segment various structures such as brain tumors  [23] , white matter hyperintensities  [24] , or organs-at-risks prior to radiation therapy  [25]. Among CNNs, the U-net  [26]  is the most commonly used model in biomedical image segmentation.
Study design and participants This retrospective case-control study used two NM-MRI datasets. A principal dataset from 60 patients with PD and 62 age-and gender-matched healthy controls (HC) was obtained by a 3T scanner (MAGNETOM Prisma, Siemens Healthcare) from October 2017 to July 2018 and was used to train and validate the U-net model. An external validation dataset, including 12 patients with PD and 12 HC, was obtained using a different 3T scanner (Achieva, Philips Medical Systems) from April 2014 to April 2015 and used to test the U-net. All patients were from the Neurology Department of Juntendo University Hospital and satisfied the Movement Disorder Society diagnostic criteria for clinically established PD  [27]. These patients responded to antiparkinsonian therapy and remained free of atypical parkinsonism for 18 months or longer after being scanned.
The HC group had no history of neurologic or psychiatric disorders. All the participants provided informed consent before examination and the Ethics Committee of the Juntendo University School of Medicine approved the study. Using two 3T MR scanners, we obtained modified NMsensitive T1-weighted fast-spin echo sequences with additional spectral presaturation inversion-recovery pulses, similar to that proposed by Schwarz et al. [15]. General scan parameters for the principal data set were as follows: 600/12 ms repetition time/echo time; echo train length of 14; 2. 5 mm slice thickness; 0. 5 mm slice gap; 3. 0 mm spacing between slices; 512 × 359 acquisition matrix; 220 × 220 mm field of view (0. 43 × 0. 43 mm pixel size); 175 Hz/pixel bandwidth, three-averages; 7:15 min of total scan time, whereas those for the external validation dataset were as follows: 688/15 ms repetition time/echo time; echo train length of 14; 3 mm slice thickness; 1.
Thresholding method based on manual segmentation We used a similar, slightly modified version of the method reported by Schwarz et al. [15]  to measure the hyperintense area in the SNpc. Image analyses were performed by two radiologists blinded to clinical information [reader 1 (KK) and reader 2 (AL)] on an offline Windows computer using the MRIcron software v2010. In this study, we used the masks delineated by the most experienced radiologist (reader 1) as criterion standard for SNpc and midbrain segmentation. First, masks were generated by manually delineating the SNpc and midbrain in two consecutive axial slices that included the midbrain (Fig. 1 ). Then, we measured the average background signal and standard derivation (SD) for each patient, where the background was defined as the midbrain subtracted by the SNpc.
The hyperintense areas of the SNpc were calculated by multiplying the number of pixels within the SNpc masks exhibiting signals above a chosen threshold by the image resolution. The optimal threshold was determined in the principal dataset by performing ROC analyses for the SNpc hyperintense areas using several thresholding values with intervals of the same order of magnitude as previous authors  [18, 19] : MSI + 1, 1. 5, or 2 SD. The highest diagnostic accuracy was yielded using the MSI + 1. 5 SD threshold. We employed the same + 1. 5 SD threshold in the external validation dataset, on the principle of externalizing both the segmentation and thresholding processes as a whole. This manual process took approximately 5 to 10 min for each subject.
U-net architecture CNN-based segmentation Deep learning segmentation was performed using a U-net architecture CNN  [26]  according to the maximum probability of candidate classes of each voxel. First, we augmented the principal dataset by changing the signal intensity, rotation degree, and scale of the original images to obtain a model that is robust against deviations, without prior signal intensity normalization. For signal intensity deviation, we converted the original signal intensity as A orig þ B ð Þ ; where A = 0. 7/1. 0/1. 3, B = -100/0/100, and orig denotes the signal intensity of the original image. For rotational deviation, we processed the original image by rotating it by -30°/0°/+ 30°. Finally, for scale deviation, we resized the original images to 90%/100%/110%. We used these augmented datasets as the final training data.
The Adam  [28]  gradient descent algorithm was used to optimize the model. Here, the Adam update rule was applied with α = 0. 0001, β1 = 0. 5, and β2 = 0. 999. Adam optimization was performed for 10 epochs. Training of the U-net was performed according to a 4-fold cross-validation based on four balanced groups mixing HC and PD patients with a 1:1 ratio. Validation and training Dice coefficients were similar in all folds; therefore, the model trained on the first fold was arbitrarily chosen to perform U-net segmentation (US). We did not employ early stopping or hyper parameter search. As an inference phase, the trained U-net was applied to the external validation dataset to segment the unlabelled Achieva MR images. NM-rich areas of the SNpc were calculated using the optimal threshold obtained with the principal dataset (signal intensity of the midbrain +1.
5SD). A computer with 64 GB of CPU memory, a Xeon E5-2670 v3 CPU (Intel, Santa Clara, CA), and a TITAN Xp graphics processing unit (NVIDIA, Santa Clara, CA) was used to perform the model training. Python 3. 6 and the DL framework of Chainer 3. 2. 0 (http://chainer. org/) was used to code the computer program. Each fold took 150 min to process. The time to predict (U-net segmentation and hyperintense voxels count) for each patient was less than 0. 5 s.
Statistical analyses Statistical analyses were performed using the XLSTAT v2018. 7 software. Age and gender distributions were compared between patients with PD and HC using Student's t test and chi-squared test. The relative variation of the background signal was calculated by dividing the mean background signal with the standard deviation. The mean relative variations in the HC and PD groups were compared using the Student's t test to assess the image quality. To evaluate the segmentation accuracy of the U-net, we used the Dice similarity coefficient (DSC) defined as Dice MS; US ð Þ¼ 2jMS∩USj MS j jþUS j j , where MS denotes manual segmentation and US denotes U-net segmentation. DSC values range from 0 to 1, where 0 and 1 indicate no and perfect overlapping, respectively. We compared the segmentation outputs of the U-net to the masks delineated manually by reader 1 (KK), considered the criterion standard.
We also compared the two readers' segmentation masks. The inter-reader precision and segmentation accuracy of the Unet was rated as follows: 0. 0-0. 39, "low"; 0. 40-0. 79, "moderate"; and 0. 80-1. 0, "high. " In terms of DSC, using Mann-Whitney U test and Student's t tests with the principal and external validation datasets, respectively, we compared the U-net accuracy or the inter-reader precision for SNpc or midbrain segmentation between the HC and PD groups. In addition, we compared the U-net accuracy to inter-reader precision for SNpc segmentation in both the HC and PD groups. The Student's t test was used to compare the NM-rich areas of the SNpc between the PD and HC groups. The relations between the hyperintense areas and disease duration or UPDRS-III scores were determined using the Spearman's rank correlation test.
The strength of the correlation was determined using the following criteria for correlation coefficient r: 0. 00-0. 19, "very weak;" 0. 20-0. 39, "weak;" 0. 40-0. 59, "moderate;" 0. 60-0. 79, "strong;" 0. 80-1. 0, "very strong. " Finally, to evaluate the diagnostic performance of the thresholding method as a diagnostic test for PD using either manual or U-net segmentation, receiver operating characteristic (ROC) analyses of hyperintense SNpc areas were performed and areas under the curve (AUC) calculated. ROC curves form the same datasets were compared using the Delong method  [29].
Principal dataset Table  2  shows the inter-reader precision and US accuracy in terms of the DSC obtained for the SNpc and midbrain. The US accuracy of SNpc and midbrain (MB) was as high as the interreader precision, with similar DSCs for each subgroup and structure. The DSCs were consistently lower for the SNpc than for the midbrain (DSCs for all subjects: midbrain, 0. 97 ± 0. 01; SNpc, 0. 83 ± 0. 04). Further, regarding the SNpc, the DSCs were lower for patients with PD than HC. The calculated hyperintense areas within the SNpc were significantly lower in the patients with PD than in the HC [MS: PD, 48. 6 ± 19. 1 mm 3 (mean ± SD); HC, 84. 9 ± 14. 4 mm 3 ; US: PD, 45 ± 18. 5 mm 3 ; HC, 83. 9 ± 14. 5 mm 3 ; all p < 0. 0001 using Student's t test] (Fig. 2a ). Analysis of the correlation between the hyperintense area and duration a significantly weak negative correlation with both methods (MS: r = -0.
External validation dataset Visual assessment of the output segmentation maps did not reveal any large segmentation errors. The US accuracy for SNpc was high for HC and moderate for patients with PD but was significantly lesser than the inter-reader precision in both cases (mean DSCs: patients with PD, 0. 77 versus 0. 83; HC, 0. 80 versus 0. 86, all p < 0. 0001). As with the principal dataset, the SNpc segmentation was less accurate than the midbrain segmentation in each case (Table  2 ). Hyperintense areas were significantly lower in the patients with PD than HC, and the differences between these groups were similar with both methods [MS: PD, 43. 9 ± 18. 2 mm 3 ; HC, 87. 6 ± 23. 1 mm 3 ; US: PD, 36. 1 ± 17. 4 mm 3 ; HC, 80 ± 23. 7 mm 3 ; all p < 0. 0001 using Student's t test] (Fig. 2b ).
Discussion Here, we developed a U-net model to segment the SNpc and midbrain in NM-MRI and showed that our model could achieve equivalent diagnostic performance to that of manual segmentation using a validated thresholding method for the hyperintense area of the SNpc, despite a moderate segmentation accuracy of the SNpc by our model. U-net segmentation of the midbrain was highly accurate both datasets; however, the U-net not achieve a segmentation of the in the same range as the inter-reader precision in the external validation dataset. The lower accuracy of the US for SNpc in the external dataset implies that different imaging parameters and signal intensity variations challenge the U-net inference capabilities. Also, applying the optimal threshold for the principal dataset to the external dataset could have affected the diagnostic test accuracy, because the should be adapted to the neuromelaninsensitivity level of the pulse sequence.
To address this specific issue, Schwarz et al. [18]  proposed a normalization procedure based on the theoretical volume of the SNpc hyperintense area in healthy controls. Because we wanted to test independently the accuracy of the U-net in the external dataset, we did not try to normalize the signal intensity level. Another finding is that the accuracy of the SNpc segmentation was consistently lower than that of the midbrain, denoting the difficulty in determining the boundary of the SNpc regardless of the segmentation method. Unlike the boundaries between the midbrain and surrounding cisterns, the boundaries between the SNpc and the background are difficult to delineate precisely because hyperintense pixels depict only neuromelanin content and not the entire SNpc. The relative subjectivity inherent in the manual segmentation of the SNpc seems to have affected both manual and U-net segmentation accuracies.
Further, the DSCs were lower in the patient group compared to the healthy group probably because reducedhyperintense areas result in an even more challenging segmentation task. Despite the relative lack of precision of the SNpc segmentation in the external dataset, the calculated hyperintense areas were significantly reduced in patients with PD compared to HC in both datasets, consistent with the results of previous studies  [13, 15, 19, 20]. The diagnostic test accuracy for PD of the thresholding method was not affected: AUC were similar using U-net or manual segmentation in both datasets, with a slight comparative advantage for the U-net method, and as high to that of the previously described manual techniques, where it ranged from 0. 82 to 0. 93  [13, 15, 20]. These results suggest that an extremely precise segmentation of the SNpc is not required to provide useful size estimates of the hyperintense area.
Our U-net model is sufficient to obtain a satisfying diagnostic accuracy. The hyperintense areas were correlated to motor severity (reflected by UPDRS-III scores) in the external validation dataset but not in the larger principal dataset. We do not have a clear explanation for this finding, as disease severity was similar between the two groups. Due to its small size (12 PD patients), the correlation analyses performed on the external validation dataset should be viewed cautiously. As previous studies on smaller samples also found weak  [18]  or no correlation  [13, 17]  with UPDRS-III scores, the utility of NM-MRI as a monitoring tool for patients with PD could not be proved. This study had several limitations. First, the sample size was relatively small for a case-control study, particularly of the external validation dataset.
A drawback of this approach is the loss of information, such as the magnitude of the signal intensity above the threshold or its spatial distribution  [30]. Several studies have found sub-regional patterns of neuromelanin loss within the SNpc using manually placed regions of interest  [16, 18]  or voxel-wise analysis  [30] , with differences between HC and patients with PD preferentially involving the posterior and lateral parts of the SNpc. Studying the whole SNpc could have contributed to the lack of correlation with the clinical status in our study, which remains an important focus for further improvement of NM-MRI. Additional studies focusing on this region of the SNpc could help achieve this goal. Finally, mean disease duration was longer in the external validation dataset and it may have influenced positively the diagnostic accuracy of the method in the external dataset.
Additionally, the mean age differed between the datasets; thus, the potential influence from these factors cannot be ignored because the midbrain is subject to agerelated changes  [31]. However, despite these limitations, because the U-net saves times and does not affect the diagnostic accuracy of the thresholding method, it may be useful to promote the clinical application of NM-MRI for PD diagnosis. In conclusion, U-net segmentation provided relatively high accuracy in the evaluation of the SNpc in NM-MRI and yielded diagnostic performance comparable to that of the established manual method, but its segmentation accuracy should be further improved to be able to fully replace manual segmentation.
This study was funded by the Brain/MINDS Beyond program from the Japan Agency for Medical Research and Development (AMED) under Grant Number JP19dm0307024; JSPS KAKENHI (JP19K17244); a High Technology Research Center Grant from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (MEXT); and the MEXT-Supported Program for the Strategic Research Foundation at Private Universities, 2014-2018.
Funding This study was funded by the Brain/MINDS Beyond program from the Japan Agency for Medical Research and Development (AMED) under Grant Number JP19dm0307024; JSPS KAKENHI (JP19K17244); a High Technology Research Center Grant from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (MEXT); and the MEXT-Supported Program for the Strategic Research Foundation at Private Universities, 2014-2018.
Overview of the Protocol Four hESC and 11 hiPSC lines were analyzed in this study (Table  1 ). Human PSCs were assayed after an average of 33 passages and differentiated into hematopoietic progenitors from EBs, using established hematopoietic permissive culture conditions. Their hematopoietic potential was evaluated by flow cytometry, colony formation, and whole transcriptome analysis in day-16 EBs. Two sub-groups of hPSCs were thereby identified according to their hematopoietic competence. Hematopoiesis-related miRNA expression in EBs was followed during the differentiation process by quantitative RT-PCR at day 3 and 16. TLDA (TaqMan low-density array) miRNome analysis was then performed at the pluripotent undifferentiated stage in both subgroups (Figure  1 ). These data were crossed in an integrative bioinformatic analysis with day-16 EB transcriptomic data.
Global microRNA Expression Profiling in Human PSCs To demonstrate a predictive value of miRNAs as markers of hematopoietic potential, the expression of 754 individual miRNAs was analyzed in our 15 hPSC lines at the pluripotent stage. Clustering gene expression patterns were determined using hierarchical algorithms of StatMiner software applying Euclidean distance and Ward's linkage method. This unsupervised method did not segregate hPSCs according to their hematopoietic potential (Figure  S4 ). An initial supervised study comparing miRNA expression between ESCs and iPSCs was performed to ensure the homogeneity of our series of hPSCs for further analyses. In such a case, no significant variation in miRNA expression was observed using the LiMMa (Linear models for microarrays) method and Bonferroni correction.