dataesr/scientific-paragraphs-categorization huggingface dataset:
Escort search Given a next move location l of a state, EscortSearch ( Algorithm 3 ) searches for a set of escorts and then builds a clearance path for each escort from its current location to the next move location of the state. In order to facilitate the reference and save the computation time of the clearance cost, we code each escort location in the neighborhood (see Fig. 5  ) by two numbers separated by a comma, where the number before the comma is the clearance cost τ , and the number after the comma is the escort location index λ, and the locations in the neighborhood are referred as l τ,λ ∈ L N , where L N is the set of locations in the neighborhood of the requested item. Observation 1. Under the assumption of no-backward-move, for any escort in the backward zone of the neighborhood of a state, its clearance cost will increase as the state evolves into any future state.
According to the grid size or/and the number of escorts of the instance, the small-size and large-size instances are further classified into several groups. See Appendices A3 for the detailed way to generate all instances in our numerical experiments. Our heuristic algorithm was coded in MATLAB 2016b academic setting. Because we could not obtain the code of the exact and heuristic algorithms of  Yalcin et al. (2019a)  , we recoded them in MATLAB to make our experimental results comparable with theirs. The experiments were conducted on a HP desktop with Windows 10 of home setting, 3. 20 GHz Intel Core i5-6500 U processor and 8 GB of RAM.
With the aid of BeamSearchP BS, a variety of SBS/RS systems may also have a chance to be re-designed as PBS systems and gain the benefits of both space and retrieval efficiency, as well as the flexibility of system design and operation. Possible obstacles to implement a PBS system are the system-specific requirements of its scheduling algorithm in real-world scenarios, which needs a further application-oriented research. Future research on the application of our algorithm in the operation/optimization of PBS systems may include: 1) improving the setting of solution pool parameters r and p, 2) finding a better state appraisal function through machine learning, 3) relaxing the constraint of no-backward-move, 4) considering multiple-items joint retrieval, 5) finding a better trade-off between retrieval time and energy consumption by allowing block movements, 6) considering the cost of changing the direction of item move, 7) Considering the influence of acceleration and deceleration of shuttles/AGVs, 8) considering the scenario with multiple I/O locations, 9) comparing the performance of PBS systems with other SBS/RS systems, 10) Analyzing the performance of different layout of aisles and PBS modules of different sizes in terms of throughput, 11) designing layout and control policy based on our algorithm.
, 2016). They are defined by four flow rates: propane (mL/min), nitrogen (L/min), oxidation air (L/min) and dilution air (L/min). The flow rates for the operating conditions studied in the present investigation are presented in Table  1. Aerosol from the miniCAST is delivered to the exposure system by the distribution/dilution column. The Vitrocell® exposure system comprises two stainless steel modules (exposure module and air control module). Aerosols are delivered to the modules in parallel: the first module receiving the aerosol and the second module receiving filtered clean air as negative control. The modules are designed for simultaneous exposure of three independent replicates. The exposure module is equipped with electrodes for high voltage charging to increase particle deposition, and in addition one well contains a quartz crystal microbalance sensor system (QCM) to determine particle dose by the Vitrocell® software.
Particle characterization: size, mass, and morphology The number and particle size distributions were measured with the SMPS in the range of 14. 46-679. 25 nm immediately downstream of the Vitrocell® system after going through the VKL dilutors. This enabled characterization of the particle number concentration at the time of deposition. The SMPS was operated at a sample flow of 0. 3 L/min. SMPS data were corrected for the PALAS VKL dilutors (100). The mass concentration of the aerosol was measured with the Tapered Element Oscillating Microbalance (TEOM) (1105 Rupprecht & Patashnick Co. , Inc. , USA) and controlled over time with the PPS directly after the VKL dilutors. The soot particles were deposited onto a grid (200-mesh copper grid with a Formvar/carbon film, Agar Scientific) through impaction/interception  (Ouf et al.
Three analytical replicates were acquired for each condition, in random order to avoid systematic bias from the measurement devices. External calibration was performed using a polycyclic aromatic hydrocarbon (PAH) mixture (Supelco EPA 610, Sigma Aldrich, France). Generally, errors below 250 ppb were found. After calibration, each individual scan of the time-resolved data was peak-picked (S/N 9) and exported with Bruker Data Analysis (5. 0) visual basic scripts. The exported mass spectra were processed by self-written MATLAB routines, and molecular assignment was performed using the following restrictions: CcHhNnOo; n≤1 and o≤4, with a maximum error of 2 ppm, DBE≤80, 0 ≤ H/C ratio ≤ 3. The molecular formula attribution lists of the six data sets were merged into one list. All attributions were considered, and attributions not found within a sample were set to zero intensity.
Optimization of cell culture conditions Fig. 6  shows the gene expression of four differentiation markers on NHBE cultured at the ALI for 0, 7, 14 and 21 days. Data are expressed as ΔCt. The lower the ΔCt the more the expression of this gene was induced. Results showed the highest expression of the MUC5AC gene, which is a marker of goblet cells, at 14 and 21 days in ALI (Fig. 6a ). Expression of FOX-J1 (Fig. 6b ), a transcription factor that controls expression of ciliary genes, was present from 0 days to 21 days at ALI. Expression of FOX-J1 remained at the same level at 14 and 21 days at ALI. Expression of MUC5AC and FOX-J1 at 14 and 21 days means the reconstitution of a pseudostratified epithelium. Regarding membrane integrity, two junction markers were studied by quantifying the expression of OCLN and ZO1 (tight junctions).
Acknowledgments This work was supported by  ANSES (French Agency for Food, Environmental and Occupational Health and Safety ;  PUFBIO  project, Grant number  EST-2017-190 ) and cosupported by the  Regional Council of Normandy  and the  European Union  in the framework of the  ERDF-ESF  ( CellSTEM  project). Ana Teresa Juarez Facio  received a PhD fellowship funded by  ADEME (Agency for Ecological Transition). The authors would like to thank  Cathy Logie ,  Violaine Martin de Lagarde  and  Marion Janona  for their technical support. The  COBRA laboratory  is financed by the  Labex SynOrg  ( ANR-11-LABX-0029 ) and the  European Regional Development Fund  ( ERDF HN0001343 ). Financial support from the.
Methods The software only allows clinicians registered with the authors to have access to medical data, in order to avoid misuse of the data. The software runs autonomously on a computer or on a mobile phone to protect patient confidentiality. If requested, it may be possible to store this data on the hospital database in connection with the patient's computerized medical record. The patient information is also anonymised with a medical identifier. Once connected, the clinician can either diagnose a new patient or access the diagnosis of a registered patient by indicating their medical identifier. In Table  1  are referenced the questions allowing to diagnose the patient via the software form. The clinician calculates the Total Burn Surface Area (TBSA). In accordance with current clinical standards, these areas are the head and neck, the lower limbs, the upper limbs, the external genitalia, the posterior surface of the trunk and the anterior surface of the trunk.
The software considers these different scenarios to select the one that is the most suitable. In Fig. 3 , there is an example of the data collection of a fictitious patient carried out by a clinician as described in the methods section. These data are processed and saved in the database to allow the clinician to find them later if necessary. In Fig. 4 , there is an example of a fictitious patient diagnosis by the software as described in the methods section. The fictitious patient is therefore in the situation corresponding to the second scenario in Table  4. His probability of mortality is 66% but it takes around 21 days to produce the graft from a 4 cm 2 biopsy. The size of the graft to be produced corresponds to the full thickness burned area, which is 15,363 cm 2 here. The clinician will not leave the patient untreated for 14 or even 21 days, but partially covers the burn using split thickness grafts.
Discussion Few quantitative methods are reported in the literature to adjust the cell quantities necessary in view of skin substitute production according to specific patient characteristics. No automation and quantification allowing diagnosis are proposed. One rare quantitative approach using Matlab computing language is tensor decomposition for color image segmentation of burn wounds  [24]. The aim is to use a new automated segmentation of the images in order to determinate the burn area. An issue is to investigate the segmentation of burn areas on 3D images to include curves and depth to further improve the segmentation accuracy  [25]. The software developed here is complementary to this "Burn Case 3D" work, because it allows to calculate the number of cells and the duration necessary for the graft production.
Statistics Statistical analysis was performed in two steps. First, we used dimensionality reduction algorithms in order to capture meaningful properties of immune senescence related markers in the cohort. PCAs were performed in R with the Factominer package  (16). The variables in PCAs were mean centered and scaled to unit. The number of PCs analyzed was based on the corresponding scree plot. We selected the PCs included in the steep part of the eigenvalues plot. UMAP was performed in R with the UMAP package  (15). In order to increase the validity of the method and ensure reproducible results we run the UMAP algorithm twice, slightly altering settings. The second step is a classic hypothesis testing to evaluate differences on markers between groups suggested by PCA biplots and UMAP projected ellipsoids.
Inferential statistical analysis was performed with SPSS. 25 IBM Corp, Armonk NY for Windows. Continues variables were checked for normality distribution by Shapiro-Wilk and/or Kolmogorov-Smirnov tests and expressed as Mean ± Standard Deviation or Median (interquartile range). As all variables, except for age, were non-parametric, differences between two variables were estimated by Mann-Whitney-U test. Kruskal-Wallis test followed by Bonferroni correction, was applied to compare data among more than two variables. Finally, chi-square test was used for categorical variables. P values < 0. 05 were considered statistically significant.
PCA Projections of Lymphocyte Subsets in ESRD Patients and Controls We initially performed a PCA, applied to both patients and controls, and included all the variables from the dataset of flow cytometric analysis. The PCA plot is depicted in Figure  1. PC1 and PC2 explain 46. 5% of total variance. The centroids and the corresponding ellipses of patients and control groups projected on this first two eigenvector delimited plane are far apart from each other suggesting that these two populations form two distinct clusters with limited admixture between the patients and controls in this space (Figure  1A ). Moreover, we observe a clustering of variables in two main beams, one of which, with direction to the upper right quadrant, points to the centroid of the control group, while the second one appears to be orthogonal to the first (Figure  1B ).
DISCUSSION Principal Component Analysis is an unsupervised machine learning technique, which allows dimensionality reduction and visualization of multidimensional datasets. The initial information of the original variables is transformed into a new set of variables called Principal Components (PCs). Each PC explains a certain percentage of variance of the original data. The first PC accounts for the largest variation in the data, followed by gradual decrease of variance explained in each subsequent PC  (17). In this study, we performed a Principal Component Analysis in order to examine whether this simple unsupervised method can unveil trends of the immune phenotype in ESRD patients. Our PCA was indeed able to identify the main lymphocyte phenotypes predominated in ESRD patients, and subsequently differentiate those patients from healthy individuals.
Background: The Region 1 Disaster Health Response System project is developing new telehealth capabilities to provide rapid, temporary access to clinical experts across US jurisdictions to support regional disaster health response. Objective: To guide future implementation, we identified hospital-level barriers, facilitators, and willingness to use a novel regional peer-to-peer disaster teleconsultation system for disaster health response. Methods: We used the National Emergency Department Inventory-USA database to identify all 189 hospital-based and freestanding emergency departments (EDs) in New England states. We digitally or telephonically surveyed emergency managers regarding notification systems used for large-scale no-notice emergency events, access to consultants in 6 disaster-relevant specialties, disaster credentialing requirements before system use, reliability and redundancy of internet or cellular service, and willingness to use a disaster teleconsultation system.
Study Design and Setting We conducted a cross-sectional survey study of all hospital-based and freestanding emergency departments (EDs; hospitals and EDs) open in March 2021 in the 6 New England states (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont). Inventory (NEDI)-USA database to identify all 189 New England hospitals and EDs open during the study period. Briefly, NEDI-USA contains information about all nonfederal, nonspecialty US EDs  [19]. Between March 1 and June 30, 2021, we called the main telephone number for each hospital or ED and requested to speak to the emergency manager for survey completion by structured interview. We selected emergency managers as key respondents, as these individuals are responsible for and most knowledgeable about the disaster preparedness and response capabilities of their facilities.
Three staff members with formal training and prior experience with contacting hospitals and EDs for other surveys about facility characteristics conducted the surveys. All used a standardized protocol and interview guide with scripted explanations in case respondents requested clarification of terms and definitions. Telephone survey responses were directly entered into a secure database by the interviewer, using Research Electronic Data Capture (REDCap; Vanderbilt University) tools hosted at Massachusetts General Hospital  [20]. REDCap is a secure, web-based software platform designed to support data capture for research studies. We also created a web-based version of the survey administered via REDCap as an alternate means of participation. The web-based survey link was active throughout the entire course of data collection.
To assess hospital-level willingness (primary outcome), we asked respondents how likely it would be for their hospital and ED to use a new disaster teleconsultation system to access specialists (very likely, somewhat likely, somewhat unlikely, or very unlikely). Among those who were "somewhat unlikely" or "very unlikely" to use this system, we asked about potential barriers to use (Multimedia Appendix 1). Additionally, using the NEDI-USA data set, we identified each ED's total annual visit volume (<10,000; 10,000-19,000; 20,000-39,000; ≥40,000), whether or not each ED was a designated critical access hospital (CAH)  [21]  or freestanding ED  [22] , whether each hospital and ED was in an urban or rural location based on location within or outside of a core based statistical area, and whether the ED received routine (nondisaster) telehealth services  [23].
Primary Data Analysis We used descriptive and bivariate statistics to describe and compare the disaster response characteristics of New England hospitals and EDs by state. Data are presented as frequencies with percentages. A chi-square test and the Fisher exact test were used to examine interstate variation in ED disaster response capability. Fisher exact test was used alternatively for low counts. P values of <. 05 were considered statistically significant. We also created maps to demonstrate the geographic differences in the hospital or ED disaster response infrastructure. Data analysis and mapping were performed using SAS (version 9. 4; SAS Institute).
If respondents did not distinguish between teleconsultation for advice and telehealth to provide direct patient care, they could perceive credentialing procedures as necessary. Confusion about the service type offered could overestimate variability in disaster credentialing procedures. However, respondents were provided ample opportunity to ask clarifying questions regarding terms and definitions during survey administration, and we did not perceive any confusion to this end. Second, the data were self-reported by 1 person per site, which may have introduced information bias. However, we mitigated this by contacting emergency managers as respondents, as they are responsible for and knowledgeable about disaster operations and plans for their facilities. Third, the attitudes of hospital emergency managers may not reflect those of frontline clinicians or other hospital administrators, so it is possible our estimates of willingness to use regional disaster telehealth services are inaccurate.
Acknowledgments The authors would like to thank  Lily Y Lu ,  Sabrina N Robichaud , and  Cordelia Zhong  for their assistance with data collection, and  Gerard Dubois  and the  Northeast Telehealth Resource Center  for expert guidance. This work is funded by  Administration for Strategic Preparedness and Response  (grant # HITEP180042 ). TB is funded by the  National Institutes of Health (NIH ; grant # 1K23HL145126 ) and the  Health Resources and Services Administration/Maternal Child and Health Bureau  (grant # U1I45814 ). The contents herein are solely the authors' responsibility and do not necessarily represent the official views of the  Administration for Strategic Preparedness and Response  or  NIH.
International efforts to overcome the gap of IMIDs Multiple research groups and consortia throughout the world are generating datasets combining clinical data with either genome data, transcriptome data, or gut microbiome data. By studying these datasets, researchers can address important individual questions. A systematic taxonomy for patients with autoimmune diseases at different omics levels has been supported by the innovative medicine initiative project PRECISESADS  (32). Analyzing whole blood samples by using transcriptomic, genomic, epigenomic, cytokine expression and flow cytometry approaches, and combining them with clinical parameters, new insights into a better stratification of patients with systemic sclerosis, syndrome Sjögren's, systemic lupus erythematosus, and rheumatoid arthritis have been achieved  (33) (34) (35) (36) (37).
To this end, the study will follow a registry-like design, with physician choice of targeted therapies and will recruit >500 UC and AD adult patients (male and female patients ≥ 18 years of age) with complete data including a clinical follow up over at least 12 months. Multi-omics data from different molecular profiling of tissue microenvironment and accessible matrices, such as blood and stool, using multiple technologies (single cell sequencing, multiomics approaches) will be integrated with clinical data. Signatures identified in blood or other matrices will be correlated with signatures in the tissue. The ultimate goal is therefore two-fold: (A) Integration of different omics layers on single tissue data to derive predictive and prognostic multi-omics signatures and (B) identification of signatures correlating between tissue and blood.
Multi-omics predictive models will undergo thorough crossvalidation to derive robust estimates for classification performance, which will be benchmarked against comparable single-omics performances and scrutinized by ImmUniverse's clinical team for their clinical utility. Reported will be not only the performance estimates, but also the predictive features (signatures) including a measure of their respective importance for classification. Tissue-derived signatures will be correlated with data derived from accessible matrices (blood and stool), using variations of canonical correlation analysis capable of dealing with the dimensionality of the data. Again, such circulating features shall be reported for the subsequent task of enriching the detected features with data from open sources. Tissue-specific molecular interaction networks and pathway maps will be identified and visualized for both diseases and compared for the tissue-specific determination of shared and disease-specific processes and pathways.
Hormonal and genetic factors contribute significantly to sex differences in immune function and disease pathogenesis. Along these lines ImmUniverse will systematically take into account biological sex differences as well as cultural/behavioral influences from phenotype/outcome definitions to tissue-based molecular analyses through to data models. Therefore, we will consider the potential confounding influence of gender at all stages of analysis and incorporate gender as a co-variate. Based on these features and enriched with data from the literature, a disease map will be developed in close collaboration with clinical partners and domain experts, which will describe the mechanisms of the pathophysiology behind the investigated diseases, highlighting their overlapping and specific areas in the context of specific tissues.
This approach will not only generate translatable signatures for disease prognosis and therapy response prediction in UC and AD, but it will also develop a first mechanistic hypothesis of the underlaying pathophysiologies (both shared and diseasespecific). This integrated analysis approach is depicted in Figure  1. Aggregated results will be shared with public via scientific publications and patients/patient organizations. Patients have right to request their data and we will securely share the corresponding data with requested study participant. Researchers will be able to search the aggregated metadata indexed in the data catalogue and submit a request to Data Access Committee (DAC) to access necessary datasets. Once the DAC approves the request, the requested data will be securely shared with the researcher.
The inclusive mechanistic models and algorithms for the integration and analysis of tissue, clinical, and omics data will be used as variables in the identification of classifiers and predictors of disease progression. Indeed, from the aggregation of multidimensional omics data, ImmUniverse will select biomarker candidates based on the following criteria: (A) 'translatability', i. e. can a test based on a marker (signature) be translated into the clinical routine (tissue accessibility, technical feasibility), (B) clinical utility based on the clinical need and the performance estimates of a candidate marker (signature) and (C) biological relevance based on the disease models & maps and interactivity networks for key regulatory genes controlling the downstream molecular alterations observed in the omics data using a causal reasoning analysis.