dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Microscopy is frequently hindered by the rate and accuracy of image analysis. Here, we developed a novel strategy that leverages physiological biomarkers to train computer vision models and automate image analysis. Using signal from GEDI, a sensitive detector of Ca++ levels presaging cell death (ref), we trained a CNN that detects cell death with superhuman accuracy and performs across a variety of tissues and imaging configurations. Importantly, we demonstrate that the GEDI-CNN learned to detect cell death by locating fundamental morphological features known to be related to cell death, despite receiving no explicit supervision to focus on those features. Additionally, we demonstrate that in combination with automated microscopy, we can accelerate the analysis of large datasets to rates faster than the rate of image acquisition, enabling highly sensitive single-cell analyses at large scale for the first time.
As GEDI-CNN significantly reduces the time and resources required to do high-power studies and eliminates human bias and intraobserver variability in live/dead classification, GEDI-CNN live/dead classifications should become a useful tool in future studies attempting to link iMN phenotypes to their genotypes. While GEDI-CNN model prediction accuracy was always above chance, the accuracy level was reduced in some datasets, suggesting differences in imaging parameters and/or biology may limit its ability to generalize from one data set to the next (Supplemental Figure  3 ). To offset reduced accuracy, new GEDI-CNN models can be transferred, or "primed", with small datasets that better represent the qualities of a particular experiment or microscope setup. These datasets can be generated by imaging the GEDI biosensor on a particular microscope platform and training a model according to the biomarker optimized (BO-CNN) pipeline we outline here.
Nevertheless, on some datasets we observed that both human and GEDI-CNN classification accuracy and GEDI-CNN accuracy were reduced, suggesting that in these cases morphology alone may be insufficient to accurately classify a cell as live or dead. Morphology of iMN datasets proved particularly difficult to interpret for both human curators and GEDI-CNN (Figure  6C ). Interestingly, while classification accuracy for live neurons was consistently high across different cell types, accuracy of classification for dead neurons often reduced overall accuracy (Figure  2B , 6D), suggesting death classification by morphology is especially difficult. Because of this variability in how different biological systems reflect death, the use of the GEDI biosensor as a ground truth to evaluate GEDI-CNN model predictions as well as for training new models will continue as the gold standard for detecting cell death.
Automated Imaging and Image Processing Pipeline Quantification of GEDI ratios was performed as previously described  31. In short, RGEDI and EGFP channel fluorescence images obtained by automated imaging were processed using custom scripts running within a custom-built image processing Galaxy bioinformatics cluster  63, 64. The Galaxy cluster links together workflows of modules including background subtraction of median intensity of each image, montaging of imaging panels, fine-tuned alignment across time points of imaging, segmentation of individual neurons, and cropping image patches where the centroid of individual neurons is positioned at the center. Single-cell tracking was performed using a custom Voronoi tracking algorithm that extracts intensity and feature information from each neuron  31.
Survival analysis was performed by defining the last timepoint alive as the timepoint before the GEDI ratio of a longitudinally imaged neuron exceeds the empirically calculated GEDI threshold for death, the last timepoint a human curator tracking cells finds a neuron alive, or the timepoint before the GEDI-CNN classifies a neuron as dead. KM survival curves and CPH were calculated using custom scripts written in R, and survival functions were fit to these curves to derive cumulative survival and risk-of-death curves that describe the instantaneous risk of death for individual neurons as previously described  65. Cumulative risk of death analysis was performed by quantification of cumulative mean of dead cells/total cells per well across timepoints. Because fluorescent neuronal debris can remain in culture for up to 48 hours past the time point of death  31  , timepoints other than at 48 hour intervals were removed from the analysis to avoid double counting dead cells within the culture.
Models were trained on Nvidia Titan X GPUs for 20 epochs using the Adam optimizer  67  to minimize class-weighted cross entropy between live/dead prediction and GEDI-derived labels. To improve model robustness, neuron images were augmented with random up/down/left right flips, cropping from 256x256 pixels to 224x224 pixels using randomly placed bounding boxes, and random rotations of 0°, 90°, 180, or 270°. These images, which were natively 1-channel intensity images were replicated into 3-channel images to match the number of channels expected by the VGG16. We selected the best performing weights according to validation loss for the experiments reported here. Experiment code can be found at https://github. com/finkbeiner-lab/GEDI-ORDER.
Image Ablations and Curation Tools Curation was performed using a custom Fiji script that runs a graphical interface with a curator, displaying a blinded batch of cropped EGFP morphology images one at a time while prompting the curator to indicate whether the displayed neuron is live or dead with a keystroke (ImageCurator. ijm). Automated subcellular segmentations were generated and saved as regions of interest (ROIs) and measured within a custom Fiji Script (measurecell8. ijm). ROIs were used to generate a panel of ablations for each image using another custom Fiji script (cellAblations16bit. ijm). Pearson co-localization was measured using JACoP with Costes background randomization 49 within a custom Fiji script (PearsonColocalization. ijm).
CODE AVAILABILITY Code is available at https://github. com/finkbeiner-lab/GEDI-ORDER. Code for statistics and data preprocessing, is available upon request to the corresponding author. Access to and use of the code is subject to a non-exclusive, revocable, non-transferable, and limited right for the exclusive purpose of undertaking academic, governmental, or notfor-profit research. Use of the code or any part thereof for commercial or clinical purposes is strictly prohibited in the absence of a Commercial License Agreement from the J. David Gladstone Institutes.
7-fold greater risk of global mortality than non-transplant CKDps and a similar level of renal function. However, KTRs appeared to have more severe infections and there was no difference between the two groups in the incidence of cardiovascular events  [24]. Data for a direct comparison of cardiovascular risk between KTRs and non-transplanted CKDp are lacking. We therefore decided to compare aortic stiffness and cardiovascular risk between KTRs and CKDps with similar renal function. To this end, we measured CF-PWV, a recognized intermediate endpoint, in KTRs and CKDps in a propensity score matching analysis including measured glomerular filtration rate (mGFR).
Study design and participants CKDps were recruited from two previously described observational cohort studies: the TransplanTest and the NephroTest  [15, 25]. The TransplanTest cohort is a monocentric hospital-based cohort that included consecutive adult kidney graft recipients who attended the Division of Renal Physiology (Tenon Hospital, Paris, France) between June 2008 and January 2017 and were able and willing to be evaluated at 3 months and/or 1 year after transplantation. Eligible patients were 18-70 years of age and were recipients of a first living or cadaveric single kidney transplant. Non-inclusion criteria were cardiac arrhythmia, symptomatic lower limb arteriopathy and dual-kidney or combined organ transplantation. All participants signed a written informed consent and data collection was approved by the Commission nationale de l'informatique et des libertes according to French legislation (n 2065902v0).
Data and measures The NephroTest cohort CKDps were referred in a 1-day visit at the Renal Physiology Division of Tenon Hospital. The TransplanTest cohort patients (KTRs) were referred at 3 and 12 months after kidney transplantation for clinical and laboratory examinations to the same Renal Physiology Division. Data were retrospectively collected for KTRs and prospectively collected for CKDps. They included demographics, medical history, diagnosis of primary renal disease, body height and weight, resting blood pressure (BP) and medications. In the two cohorts and at each visit, mGFR was measured by chromium-51 ethylenediaminetetraacetic acid ( 51 Cr-EDTA) renal clearance, as reported previously  [26]. In brief, 1. 8-3. 0 MBq of 51 Cr-EDTA (GE Healthcare, Velizy, France) were injected intravenously as a single bolus.
Statistical analysis We first created a study database by pooling the TransplanTest and NephroTest (Tenon Hospital) data. We then calculated propensity scores including the following variables: mGFR, age, sex, MBP, BMI and heart rate  [28]. For KTRs, these variables were evaluated 12 months after transplantation. The 168 KTRs were matched at a 2:1 ratio with the 609 CKDps and the closest propensity score. This provided propensity score-matched sets of 137 KTRs and 226 non-transplant CKDps (Figure  1 ). Propensity score matching was performed with a caliper, which enabled us to have no residual differences between the two groups for the variables used. Patient characteristics were summarized as medians (interquartile ranges) or numbers (%) and the two groups were compared using Student's t test or Wilcoxon test for quantitative data and a chi-squared test or Mann-Whitney test for qualitative data, as appropriate.
Logistic regression models were used to estimate crude and adjusted odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) for CF-PWV higher than the median. For multivariable analysis, we included in the models all clinically relevant vascular stiffness-associated factors selected based on literature and identified in univariate analysis (P < 0. 05). Concerning covariates already used for propensity score matching, we included only those with standardized mean differences (SMDs) <0. 10 [29]. Models were compared using log-likelihood statistics. For sensitivity analyses, we repeated the matching, limited to only one mGFR matching, and repeated the analyses. Missing data were managed by applying multivariate imputation with chained equations (MICE) before matching. The percentage of missing data among the patients before matching (n ¼ 777) is given in Supplementary data, Table  S1.
ACKNOWLEDGEMENTS The NephroTest study group:  Bichat University Hospital (Paris): Francois Vrtovsnik, Eric Daugas (Nephrology)  and  Martin Flamant  and  Emmanuelle Vidal-Petiot (Physiology) ;  European Georges Pompidou University Hospital (Paris) :  Christian Jacquot ,  Alexandre Karras ,  Eric Thervet ,  Christian d'Auzac (Nephrology)  and  P. Houillier ,  M. Courbebaisse ,  D. Eladari  and  G. Maruani  (Physiology);  Tenon University Hospital (Paris ):  Jean-Jacques Boffa ,  Pierre Ronco ,  H. Fessi  and  Eric Rondeau  (Nephrology) and  Emmanuel Letavernier ,  Nahid Tabibzadeh ,  Marine Livrozet ,  Camille Saint-Jacques  and  Jean Philippe Haymann  (Physiology); and  INSERM (Villejuif) :  M. Metzger  and  B. Stengel. We thank nurses  Martine Dapiedade ,  Catherine Fremy  and  Nathalie Dieu  for their expert technical assistance.
Follow-Up of PH Patients PH patients were prospectively followed-up until 1 January 2019. All patients were prescribed with specific PAH medications and/or treated with balloon pulmonary angioplasty or pulmonary endarterectomy (CTEPH patients) when indicated according to the guidelines  [19, 23]. The primary endpoint was defined as all-cause mortality. Survival status of all patients was obtained from patients and checked in the Municipal Personal Records database. Patients who did not reach the primary endpoint were censored at the 1 January 2019.
Logistic regression was conducted to determine whether plasma melatonin was an independent risk factor that distinguishes between PH patients and healthy controls. Univariate and multivariate Cox proportional hazard regression were used to assess associations between plasma levels of melatonin and mortality in PAH patients, one PAH patient with a very high melatonin level of 4471 pM was defined as an outlier (>100 times the IQR), and was excluded to avoid interference. Comparisons of long-term survival curves between groups in PAH patients were performed using Kaplan-Meier analysis with log-rank (for trend) test. Statistical analysis was performed using IBM SPSS software (version 21. 0. 0. 1), figures were made using GraphPad Prism (version 8. 0. 2). A two-sided p-value < 0. 05 was considered statistically significant.
Acknowledgments: This work was supported by the  China Scholarship Council  ( 201606230252 ) as well as the  Netherlands CardioVascular Research Initiative : an initiative with support of the  Dutch Heart Foundation  ( CVON2014-11 , RECONNECT), and  German Center for Cardiovascular Research  ( DZHK81Z0600207 ). Instrumentation support was received from  AB Sciex, ltd. for LC-MS/MS analyses performed in this study.
Reynolds model The model using the solution of the Reynolds equation to describe flow in the air gap of an NFL problem, which was applied in this study, was presented in detail in work  [8]. The equations are solved in a home-made code written in Fortran 2003 and based on the Finite Volume Method. As discussed in the introduction, the Reynolds equation is a simplification of the Navier-Stokes equations due to the thin film configuration where only a part of the viscous contributions are preserved  [24]. Therefore, the inertia terms are neglected and the pressure is constant in the through film direction. The fluid velocity profiles are thus parabolic and proportional to the radial pressure gradient. The Reynolds model, similar to the CFD model, used the axial symmetry of the analyzed system (for simplifying calculations) and the same set of boundary conditions and assumptions for air behavior in the gap (ideal gas).
Acknowledgments This work was partially carried out as a part of research grant no. 2016/23/B/ST8/00210  entitled "Study on the use of Poisson's acoustic levitation effect for non-contact transportation of light objects" financed by the  Polish National Science Centre. CFD calculations were carried out at the  Academic Computer Centre (TASK)  in Gdańsk. This work pertains to the  French Government program  " Investissements d'Avenir" (LABEX INTERACTIFS , reference  ANR-11-LABX-0017-01 ).
Background: Humeral stem lucencies are uncommon after uncemented anatomic total shoulder arthroplasty (aTSA), and their clinical significance is unknown. This study compares clinical outcomes of aTSA with and without humeral stem lucencies. Methods: Two-hundred eighty aTSAs using an uncemented grit-blasted metaphyseal-fit humeral stem between 2005 and 2013 were retrospectively evaluated for radiographic humeral stem lucencies. All shoulders were evaluated at a minimum 5-year follow-up from a multicenter database. Clinical outcomes included range of motion (ROM) and American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form (ASES) score, Constant score, University of CaliforniaeLos Angeles Shoulder Score (UCLA), Simple Shoulder Test (SST), and Shoulder Pain and Disability Index (SPADI) scores.
Materials and methods A retrospective review of all primary anatomic total shoulder arthroplasties between March 2005 and May 2013 was performed using a prospectively collected multicenter research database. All shoulders with a press-fit uncemented humeral stem, adequate radiographic data, preoperative and postoperative ROM, and PROMs with a minimum 5-year follow-up were included. All arthroplasties were performed using a single shoulder arthroplasty system (Equinoxe; Exactech, Inc. , Gainesville, FL, USA). This system uses a platform press-fit, metaphyseal, grit-blasted humeral stem that is between 100 and 125 mm long depending on stem diameter. All operations were performed by fellowshiptrained surgeons from 14 different centers across the world. The deltopectoral approach was used in all cases.
Exclusion criteria included revision shoulder arthroplasty, rheumatoid arthritis, and post-traumatic arthritis. All shoulders were evaluated by the operating surgeon using a standardized follow-up protocol. Radiographic follow-up included a minimum of 2 views (Grashey and axillary lateral views). Humeral stem lines were evaluated according to their presence or absence, location, and thickness using a standardized multicenter protocol. Location was categorized according to the Sanchez-Sotelo radiographic zones. 20, 21 Glenoid periprosthetic lines were graded according to the Lazarus Scale. 14 Shoulders were then separated based on the presence or absence of peri-implant humeral component lucencies, and their data were compared. Demographic data were evaluated, including age, sex, and body mass index.
Data were collected on the history of administration of a prior corticosteroid injection to and prior surgery of the operative shoulder. The performing surgeons and/or their research assistants examined patients independently both prior to and following primary shoulder arthroplasty using a standardized protocol. Shoulders were evaluated clinically for active ROM, including abduction, forward elevation, external rotation with the arm at the side, and internal rotation. Abduction, forward elevation, and external rotation were measured in degrees. Internal rotation was measured according to the level reached by the thumb. This value was categorized as described by Flurin et al. 10  At the time of clinical followup, outcome scores were obtained, including American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form (ASES) score, Simple Shoulder Test (SST), Constant score, University of CaliforniaeLos Angeles Shoulder Score, and Shoulder Pain and Disability Index (SPADI).
The minimal clinically important difference (MCID) for ROM and outcome scores was used as defined by Simovitch et al  26  for aTSA. Postoperative follow-up was performed at regular intervals, with ROM and outcome scores obtained at each visit. Complications and reoperations were recorded. Shoulders undergoing revision surgery were included in clinical analysis using the most recent data before revision surgery. Subanalysis was also performed to evaluate the effect of stress shielding where the patients with humeral lines in zone 8 were compared to the other patients with humeral lucent lines, excluding patients revised for humeral loosening. In this subanalysis, patients were included in the stress shielding group if they had lines in zone 8 (isolated) or lines in zones 8 and 7 (within the proximal grit-blasted portion of the stem) but were excluded if they had lines in any other zone to try to distinguish between stress shielding (loss of bone proximal to the area of ongrowth) and osteolysis (which should theoretically have lucent lines in other zones).
Statistical analysis Continuous variables were evaluated using Welch 2-sample t test or 1-way analysis of variance with Tukey honestly significant difference post hoc test for multiple comparisons of means. Categorical variables were analyzed using a c 2 test or Fisher exact test, based on sample size. The alpha level for significance was set at P <. 05. SPSS, version 17. 0 (SPSS, Chicago, IL, USA), and R, version 3. 5. 1 (R Foundation for Statistical Computing, Vienna, Austria), were used for statistical analyses. The patients with humeral lucent lines present were directly compared to those without any lines.
Results Two hundred eighty aTSAs (137 males, 143 females) were evaluated at a mean follow-up of 6. 6 years (range, 5. 0-12. 0 years). This group was from a total of 559 aTSAs performed during the study period, with 279 being excluded (50%) because of missing preoperative or postoperative data points at a minimum 5-year follow-up. Follow-up for the humeral line group (7. 4 years) was longer than shoulders without humeral lines (6. 5 years; P ¼. 003). At the most recent follow-up, 243 (86. 8%) showed no humeral lucent lines and 37 (13. 2%) showed 1 or more peri-implant humeral lucencies. Humeral lucent lines were more prevalent in zone 8 (21/ 37, 57%), zone 4 (15/37, 41%), zone 7 (12/37, 32%), and zone 3 (11/37, 30%). See Figure  1  for complete details. Both groups were similar in age, sex, body mass index, history of prior surgery, estimated blood loss, and comorbidities.
Complications Gross humeral loosening was noted in 4 of the 37 patients in the humeral lucent line group (11%). One patient experienced humeral lucent lines with an impending pathologic fracture and underwent revision surgery. One patient sustained rotator cuff failure that led to particulate debris and ultimately to humeral and glenoid loosening and revision surgery. Another patient had significant glenoid wear, and eventually humeral loosening, also requiring revision surgery. The last patient had humeral loosening confirmed by computed tomographic scan, and revision was planned at the time of data collection. Other complications besides humeral loosening were more common in the humeral lucent line group (21% vs. 8%; Table  III ). The need for any revision excluding the patients revised for humeral loosening was higher in the humeral lucent line group (18% vs.
To our knowledge, no other study has associated an increased complication rate with patients with humeral stem lucencies in aTSA. This study has multiple limitations. The main limitation is its retrospective design; however, a prospectively collected database was used to provide the data. The operations were performed by multiple surgeons at different centers. This contributed to inherent variation in surgical techniques and postoperative protocols. This also, however, increases the generalizability of the study findings toward a diverse population and allows us to have a large number of patients with adequate follow-up. Radiographs were analyzed by the surgeon performing the procedure, introducing possible selfassessment bias. Additionally, although radiographs were taken using a standardized protocol, variability in x-ray technicians and positioning was likely present and could have affected the grading of humeral lucencies.
2 ). To nd the protein-interaction networks affected by the presence of diabetes on each tissue we performed an in silico bioinformatics analysis with the Ingenuity Pathway Analysis (IPA) software. For this, we introduce all those proteins with a signi cant change or a ratio DM versus non DM lower than 0. 5 or higher than 1. 7. Regarding SAT, the top affected network with a score of 56 and 25 proteins involved was the network "Endocrine System Disorders. Free radical Scavenging" (Supplemental Fig. 1 ). On the other hand, when we studied VAT the top affected network was associated with "Metabolic disease" with a score of 40 and the contribution of 16 proteins (Supplemental Fig. 2 ).
Host range The following laboratory collection strains were used to test their ability to replicate viruses MRV1, ARV3 and SSRV1: A. convivator  [9] , A. hospitalis W1  [10] , Saccharolobus solfataricus strains P1 (GenBank accession no. NZ_LT549890) and P2  [11] , Sulfolobus islandicus strains REN2H1  [2] , HVE10/4  [12] , LAL14/1  [13] , REY15A  [12] , ΔC1C2  [14] , and Sulfolobus acidocaldarius strain DSM639  [15]. CsCl gradient-purified virus particles were added directly to the growing cultures of Acidianus species and virus propagation was evaluated by TEM. For Sulfolobus and Saccharolobus species, spot tests were performed and the replication of the virus was evidenced by the presence of an inhibition zone on the lawn of the tested strains. The host ranges of PFV2 and PSV2 were examined by adding purified virions to the growing cultures of P.