dataesr/scientific-paragraphs-categorization huggingface dataset:
Patient Data We analyzed the Premier Healthcare Database (PHD), an all-payer, geographically diverse deidentified hospital administrative database with more than 1 billion patient encounters representing approximately 25% of inpatient discharges in the United States. 5 The hospitals included in the PHD are a subset of hospitals that are on the Premier Quality Advisor Platform and have agreed to make their data available for research. Compared with hospitals included in the American Hospital Association (AHA) Annual Survey, the PHD has a greater proportion of hospitals located in the South (43. 9% vs 37. 4%), rural hospitals (29. 8% vs 24. 1%), nonteaching hospitals (71. 7% vs 59. 2%), and hospitals greater than 400 beds (19. 6% vs 10. 4%) (eTable 1 in the Supplement). The PHD contains patient-level and visit-level data from hospital discharge files where a patient can be followed within a facility using a unique identifier.
The PHD contains information on patient demographics; diagnoses; billed laboratory and diagnostic services; and billed medications and procedures. 7] [8]  We identified 767 hospitals with continuous monthly data submissions from January 1, 2019, to December 31, 2020. This study was approved by the Institutional Review Board of the Harvard T. H. Chan School of Public Health. Because the PHD contains deidentified data, informed consent of study participants was not pursued. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
Limitations There are various limitations of this study. First, PHD is an administrative database and relies on hospital reporting for accurate identification of surgical procedures, comorbidities, and demographics. While the PHD is broadly representative of US acute care hospitals, the patterns of surgical use may not apply to individual health systems. Second, PHD race and ethnicity information is self-reported, and approximately 10% of race and ethnicity information in this sample was other or unknown, which could indicate underrepresentation of racial and ethnic minority groups in the sample. Third, we could not determine causality in this observational study. Future studies will be needed to explore potential mechanisms for racial and ethnic variations in demand and preference sensitivity for elective surgical care during pandemics.
delivery system. The COVID-19 pandemic led to unprecedented numbers of individuals seeking emergency, inpatient, and intensive care. Cancellation and postponement of elective surgical procedures emerged as a primary mechanism to generate hospital surge capacity. State-mandated Key Points Question To what extent did the COVID-19 pandemic reduce access to surgical care, and were racial and ethnic minority groups more likely to have reduced access to surgical care? In this cohort study of more than 13 million inpatient and outpatient surgical encounters in 767 US hospitals in a hospital administrative database, surgical use was 13% lower in 2020 compared with 2019, with the greatest decrease concentrated in elective surgical procedures. While Black and Hispanic patients experienced a reduction in surgical encounters, White patients experienced the greatest reduction in surgical encounters.
in Use of Surgical Care During the COVID-19 Pandemic 32. Wiznia DH, Schneble CA, O'Connor MI, Ibrahim SA. Musculoskeletal urgent care centers in Connecticut restrict patients with Medicaid insurance based on policy and location. Clin Orthop Relat Res. 2020;478(7):1443-1449. doi:10. 1097/CORR. 0000000000000957 SUPPLEMENT. eTable 1: Characteristics of Hospitals in the Premier Healthcare Database Compared to the American Hospital Association Annual Survey, 2018 eTable 2: Procedural codes of surgical cohorts.
Milton Fund of Harvard University , and Arnold Ventures outside the submitted work. Dr Rosenthal  reported financial support from  Premier Inc. No other disclosures were reported. Funding/Support: This project was supported by a research grant from  The Commonwealth Fund. Role of the Funder/Sponsor: The Commonwealth Fund had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The views expressed in the manuscript are those of the authors and do.
Findings delivery system. The COVID-19 pandemic led to unprecedented numbers of individuals seeking emergency, inpatient, and intensive care. Cancellation and postponement of elective surgical procedures emerged as a primary mechanism to generate hospital surge capacity. State-mandated Key Points Question To what extent did the COVID-19 pandemic reduce access to surgical care, and were racial and ethnic minority groups more likely to have reduced access to surgical care? In this cohort study of more than 13 million inpatient and outpatient surgical encounters in 767 US hospitals in a hospital administrative database, surgical use was 13% lower in 2020 compared with 2019, with the greatest decrease concentrated in elective surgical procedures. While Black and Hispanic patients experienced a reduction in surgical encounters, White patients experienced the greatest reduction in surgical encounters.
JAMA Health Forum | Original Investigation Variation in Use of Surgical Care During the COVID-19 Pandemic 32. Wiznia DH, Schneble CA, O'Connor MI, Ibrahim SA. Musculoskeletal urgent care centers in Connecticut restrict patients with Medicaid insurance based on policy and location. Clin Orthop Relat Res. 2020;478(7):1443-1449. doi:10. 1097/CORR. 0000000000000957 SUPPLEMENT. eTable 1: Characteristics of Hospitals in the Premier Healthcare Database Compared to the American Hospital Association Annual Survey, 2018 eTable 2: Procedural codes of surgical cohorts.
For large deltas, altimeter-based elevations offer a means of extrapolating gauge data over space and time. Papa et al. (2010 Papa et al. ( , 2012) )  applied such an approach to the Ganga-Brahmaputra river system for the period 1998-2011. This time series allows for assessment of interannual variability over time ranges not spanned by gauged efforts. Papa et al. (2012)  note a 12 500 m 3 s -1 standard deviation in interannual variability in the Ganga-Brahmaputra discharge. Importantly, such data sets are also easily accessible by the general public, facilitating progress and understanding by the scientific community. The bay's upper-ocean temperature and salinity structure is an integrated representation of the sources summarized above and sinks of heat and freshwater, combined with the physical processes that redistribute these quantities.
Andaman Sea exchange The Irrawady river drains into the Andaman Sea, a marginal sea at the eastern edge of the bay. Export from the Andaman Sea is then another source of freshwater for the bay, particularly at intermediate densities (22-25 kg m -3 ). A striking example of the interaction between strong surface forcing and an anticyclonic eddy can be found in the fortuitous crossing of an intra-thermocline eddy (ITE) in 2013 as reported by  Gordon et al. (2017). The water mass characteristics clearly identify ITE waters from the Andaman Sea, and analysis of ancillary Argo data suggest a similar water type often penetrates westward into the bay extending from the three passages connecting the two basins. While at the time of transit the observed ITE had a very weak surface expression, a week prior to encountering the ITE a clear sea surface high (> 10 cm) is evident in AVISO SSHA.
Acknowledgements. The authors acknowledge the sustained efforts of researchers and funding agencies in observing and modelling the oceanic and atmospheric processes that control climate variability in the Indian Ocean region. These contributions during the  International Indian Ocean Expeditions (I and II)  and in the intervening years through national and international programmes, such as CLIVAR and GOOS, are fundamental to improving our knowledge of these systems and increasing our skill at forecasting variability and extreme events. We thank the IIOE-2 leadership team (https: //iioe-2. incois. gov. in/, last access:  22 October 2021 ) for their unwavering efforts to share new discoveries and promote understanding of the importance of the Indian Ocean to the climate system and Earth's inhabitants.
Human and mice peripheral blood samples were washed three times with PBS (Thermo Fisher Scientific, Waltham, MA, USA). Samples were stained with thiazole orange in PBS with 0. 5% BSA (Sigma-Aldrich, St. Louis, MO, USA) for 30 min at room temperature (RT). Mice samples were processed using BD FACSMelody™ cell sorter (BD Biosciences, Franklin Lakes, NJ, USA) and Sony LE-SH800 cell sorter (Sony Corporation, Tokyo, Japan). Human samples were processed using BD FACSAriaII cell sorter (BD Biosciences, Franklin Lakes, NJ, USA) and Sony LE-SH800 cell sorter (Sony Corporation, Tokyo, Japan). The projected cell surface area was measured using forward scatter area (FSC-A) signal. At least 10,000 cells were collected. The data were analyzed using FCS Express 6 (De Novo Software, Pasadena, CA, USA).
5% BSA. Samples were acquired using ImageStream ®X Mk II Imaging Flow Cytometer (Merck Group, Darmstadt, Germany). At least 300,000 Ter119 + cells from the bone marrow and spleen and 100,000 cells from peripheral blood were collected. The data were analyzed using IDEAS 6. 2 software (Merck Group, Darmstadt, Germany) with at least 100 single cell images in each subpopulation. The Ter119, CD71, Hoechst and MitoFluor signals were analyzed using mean fluorescence intensity, if not stated otherwise. For nascent pyrenocyte and nascent reticulocyte analysis, we developed a gating strategy and masks based on Hoechst stain and brightfield (BF) images. First, elongated Ter119 + /Hoechst + cells were selected using the BF shape ratio range 0. 45-0. 70, and delta centroid Hoechst and BF range 0-4. From the elongated cell population, we selected enucleating cells using a cell area mask, where the nascent pyrenocyte area mask was defined as the area covered by Hoechst positive staining at a range of 25-50 µm 2 and the nascent reticulocyte area mask was defined as a BF area without the Hoechst area at a range of 20-30 µm 2.
Analyses of MS/MS Data The MS data were analyzed using MaxQuant software version 1. 6. 6. 0 (Max Planck Institute of Biochemistry, Martinsried, Germany)  [17]  using the Uniprot-Swissprot reviewed database (Uniprot-Swissprot, release 2019-10, European Bioinformatics Institute, Hinxton, UK; Swiss Institute of Bioinformatics, Lausanne, Switzerland; Protein Information Resource, Washington, DC, USA) and a protein false discovery rate of 0. 01. Carbamidomethylation of cysteines was set as a constant modification and acetylation of protein N termini and oxidation of methionines as variable modifications. Labelfree protein quantification (LFQ) was performed using both unique and razor peptides with at least two ratio counts. Data from all experiments were analyzed simultaneously using the match between runs (MBR) option.
Higher Ribosomal Content and Smaller Red Cell Size in JAK2 V617F PV and ET Patients To extend these data to human cells, we analyzed circulating reticulocytes and mature RBCs from patients with PV and ET carrying the JAK2 V617F mutation and healthy individuals (CT). Cells were analyzed for organelle remnants as well as for their size. Due to the very low percentages of circulating reticulocytes, we applied an enrichment protocol based on cellulose separation of blood samples followed by reticulocyte enrichment using a cell density gradient, as previously described  [7]  (Figure  7A ). Reticulocytes from PV patients showed a 1. 32-fold increase in ribosomal RNA levels compared with CT (Figure  7B, C ). Similar to the mouse results, human PV RBCs and reticulocytes were smaller compared with CT RBCs (Figure  7D, E ).
Acknowledgments: The authors thank  Thierry Peyrard ,  Dominique Gien ,  Sirandou Tounkara , and  Eliane Véra  at  Centre National de Référence pour les Groupes Sanguins  for the management of blood samples. The authors thank  Sandrine Genetet  and  Isabelle Mouro-Chanteloup  at the  Inserm   UMR_S1134  unit for their assistance in experiments. The authors also thank  Michaël Dussiot  at the  Institute Imagine  for his assistance in imaging flow cytometry. We thank  Johanna Bruce  and  Virginie Salnot  at  3P5 Proteomics Platform  for sample preparation and analysis, and  François Guillonneau  and  Patrick Mayeux  for their management and strategies.
Reticulocytes PV JAK2 vs. CT Subcellular Location Protein Gene Name Accession Number p-Value Fold Change Cytoplasm Plasma Membrane Mitochondria Nucleus Cytoskeleton Endoplasmic Reticulum Ribosomes Golgi Apparatus Endosome Transferrin receptor protein 1 TFRC P02786 0. 0439 6. 4 Pm E ATP synthase subunit alpha, mitochondrial ATP5A1 P25705 0. 0012 4. 3 M 60 kDa heat shock protein, mitochondrial HSPD1 P10809 0. 0212 3. 9 M Methionine-tRNA ligase, cytoplasmic MARS P56192 0. 0100 3. 8 C Prohibitin PHB P35232 0. 0253 3. 3 C Pm M N Myosin-9 MYH9 P35579 0. 0221 3. 1 C N S Stress-70 protein, mitochondrial HSPA9 P38646 0. 0151 2. 7 M Isocitrate dehydrogenase [NADP], mitochondrial IDH2 P48735 0. 0493 2. 4 M Protein-L-isoaspartate (D-aspartate) PCMT1 P22061 0. 0466 2. 4 C O-methyltransferase Vacuolar protein sorting-associated protein 35 VPS35 Q96QK1 0.
The state of the art Up to 1 st of June 2021 only 150 articles appeared in PubMed's database using the search queries "SARS-CoV-2 Neuropathology" and "COVID-19", an extremely low number compared with the 85,519 articles using a "SARS-CoV-19" entry and the 140,077 with the keyword "COVID-19". Furthermore, the cases studied were extremely limited  1. There are several reasons for this low number of publications, and the difficulty in performing brain biopsies or autopsy on subjects infected with SARS-CoV-2 or affected by COVID-19 plays an important role. To date, this explains how our knowledge of the neuropathology of this infection is limited and sometimes contradictory. The poor understanding of the neuropathology of this infection and of this disease is of considerable concern considering that neurological complications of COVID-19 2 have frequently been observed, both in the acute and in the long-term phases of the disease.
Stroke care and assisting patients with COVID-19 infection Despite the association of SARS-CoV-2 infection with an increased risk of ischemic stroke, in Spring 2020, numbers of inpatient stroke decreased. A decline in acute stroke code activations (Figure  8. 1), stroke hospitalizations, and mechanical thrombectomy volumes have been reported in a paper by Friedlich et al. 56  at local, regional, and national levels compared with most reports from comprehensive stroke centers (CSC) in high income countries (paper has been recently retracted because of lack of written consent from the American Heart Association to use the 'Get with the Guidelines' dataset). Primary stroke centers and centers with higher COVID-19 inpatient volumes experienced steeper declines. The reasons for this decrease are not completely understood but may relate to patients with stroke symptoms not seeking care due to fear of contracting SARS-CoV-2 in the emergency room, lack of recognition of stroke symptoms due to isolation from social distancing, misdiagnosis of stroke in the setting of SARS-CoV-2 encephalopathy, or other factors  56, 57.
Photoplethysmography with a smartphone camera has been applied for AF screening  112. Apps specifically designed to record a rhythm strip using smartwatches have been seen to be accurate in differentiating AF from sinus rhythm  113. More recently, an app for detecting AF has been applied in the Apple Heart Study 114. Depending on the availability of the users, the telerehabilitation sessions can be performed by phone, via videoconferencing software or through dedicated apps (i. e. , "REHABmyPatient," "myRehab," or "RehabPal")  91. This pandemic might be the key moment in which to introduce a more widespread use of new technologies to improve management of neurological disorders.
In the European database there are 122 reported cases of myocarditis in Pfizer (Comirnaty) recipients, 16 in the Moderna COVID-19 vaccine, 38 in Vaxzevria) and 0 for the Janssen COVID-19 vaccine  39. The reported cases of pericarditis were 126 (Comirnaty), 18 (Moderna COVID-19 vaccine), 47 (Vaxzevria) and 1 (Janssen COVID-19 vaccine). The exposure in the EEA for each vaccine was around 160 million doses for Comirnaty, 19 million doses for Moderna, 40 million for Vaxzevria, and 2 million for Janssen. Most of these cases were mild, resolving within a few days, mainly affecting males under 30 years of age, and after the second dose of vaccination  39. Six cases of capillary leak syndrome in people who had received Vaxzevria were reported mostly in women within 4 days of vaccination (one of the 3 with history of capillary leak syndrome died)  40.
During this phase, analytic memos were used to refine our theoretical categories. After reviewing a code category, researchers independently wrote memos on the key theoretical implications (eg, "socialization as a means of coping"), which were discussed in consensus meetings. To achieve consensus on the final model, coders met with team members who have expertise in IM delivery (JJM, KTL) and refined the constructs as needed. The qualitative software NVivo Pro 12. 0 (QSR International)  [32]  was used to facilitate the analysis and store the final codebook.
LPWAN have already highlighted their limits when the load (aka bit flux) increases  [4] , limiting the amount of traffic that an application can generate. In this paper, we investigate the scalability of LPWAN solutions, and more specifically LoRa, a leading technology for LPWAN, when considering a challenging smart mobility solution. We consider both mobile devices, in particular to monitor the actual speed through many road segments, and static devices used in smart parking. We also consider a ride hailing service co-existing in the same city. The contributions of this paper are as follows: 1) first, we define a global scenario for Intelligent Transport System (ITS): distinct applications share data to offer a globally efficient transportation system for smart cities; 2) then, we take advantage of two real and large cities datasets for our global system.
That way, we can generate packets that mimics a realistic situation, with all the heterogeneity (e. g. , downtown vs. suburbs) and temporality of large scale ITS. The traffic model we derive from these datasets is useful to assess the communication performances in real smart cities; 3) finally, to fully exploit our construct, we rely on a LoRa model inferring the Packet Error Rate (PER) of an arbitrary deployment of LoRa gateways; we thus estimate the scalability of such LPWAN solutions in realistic deployments. We highlighted the presence of hot spots, where the density of required LoRa gateways can be quite high to decently deal with some traffic peaks.
A. Service characterization Realistic service definitions are crucial in order to assess suitability of proposals. It is common to characterize a service by the amount of messages generated by device and/or geographical area  [5]. Fortunately, more and more open data projects have emerged  [6]  and allow us to design specific scenarios. Zebranet  [7]  represents an early example of such projects which has experimentally investigated the accuracy of Wireless Sensor Networks for wildlife monitoring. The Zebranet dataset has then been widely used, to assess the performance of protocols and algorithms. In Smart Cities, Kong et al. [8]  exploit a floating car dataset in New York City (NYC) to simulate a Vehicular Social Network. However and unfortunately, most of these deployments focus only on a single service and, as such, remain silo proofs of concepts.
Furthermore, we map relational to IoT data by having data sources, samples and attributes acting as tables, rows and columns, respectively. We then simply add the following keywords in our query language extending SQL to fully describe the IoT streams: COMPUTE indicates a list of computations to apply to the data in sequence. EVENT describes condition(s) that may activate/deactivate a stream (anything, from local sensors to remote messages). EVERY indicates when new data must be generated to the stream. Events or constant time intervals may apply. UNTIL indicates the condition to stop the stream. Analogous to EVERY, events or specific duration may apply. Without this keyword, the query is assumed to hold indefinitely.
A. Datasets Description We exploited the two following datasets (cf. Table  I ). Ride hailing: we use a dataset of rides performed by the companies Uber, Juno, Lyft and Via in NYC  2. Each sample describes the pick-up and drop-off information (i. e. , timestamp and geographical location) of a trip. To respect privacy concerns, the city employs pre-defined zones and zone identifiers instead of exact geographical coordinates. We model here mobile sensors (i. e. , vehicles); Parking usage: the city of Melbourne provides measurements of parking sensors  3. Each sample of this dataset describes individual occupations of parking spots in the city and includes timestamps of arrival and departures in addition to multiple identifiers of the parking spot. We have here static sensors with an event-based traffic.
B. Dataset Preparation Since the service usage described by these two datasets are deployed in different cities, we need to merge both to mimic a global service running in the same city. We map Melbourne's dataset to NYC since more information is openly available for the latter. We gather a list of parking lots in NYC with the aid of the TomTom API 4. Since we have more parking lots in Melbourne than in NYC, one-to-one mapping cannot be used. We first cluster the 4,876 parking lots in melbourne into 1,212 clusters (the number of parking lots we found in NYC) using the k-means algorithm according to geographical location. With this, each cluster represents a virtual parking lot with the data from its members. Now we can look for a parking bijection from Melbourne to NYC. Assuming that parking lots closer to Manhattan (commercial center of NYC) will have more demand, we map our virtual parking lots Melbourne with most traffic into the parking lots of NYC which are closer to Manhattan.
C. Database-based Network Traffic Model We infer the network traffic from the datasets (cf. Table  II  that summarizes the parameters used in our models): 1) Ride Hailing: we model both queries 1 and 2 by using the time and location information of taxi pick-ups and dropoffs in NYC. Each trip record emulates a query at time and location of pick-up and drop-off. We model the duration of the query with a normal distribution (see Table  II ). 2) Smart Parking: we model the park_begin and park_end events of queries 3 with the arrival and departure data observed in the Melbourne dataset. More specifically, for each record, at time of arrival and time of departure, one message is sent from the parking lot location. We model the inter-query time of query 4 by assuming that there was a search for available parking every time a parking spot is taken.
Since available parking can change constantly, one message is sent every minute until the car parks. For same reasons as before, we again rely on a normal distribution to model the time to find a parking spot (see Table  II ). 3) Smart Road Traffic Routing: in query 5, we consider that a subset of vehicles sends its speed information (e. g. , a subset of vehicles is equipped). More precisely, we consider that only 10% of the dataset of taxi trips is considered by this sub-service. Messages are sent periodically along the trajectory we generated for each trip.