dataesr/scientific-paragraphs-categorization huggingface dataset:
3 m • s -1 ) are considered purely randomly oriented; the random values are refreshed every second. Quite interestingly, this simple reconstruction of head orientations yields mean infection rates C per hour that agree very well with the values computed with the bona fide orientations, with a relative difference generally lower than 15%, regardless of the transmission model. The correspondence between the individual C i values for each pedestrian is of course imperfect with this method, but overall the differences are not extremely large (Fig. S10 ). These observations are particularly relevant to bolster risk assessments based on observed or (reliably) simulated pedestrian datasets in which head orientations are missing, as they most often are.
Video acquisition and processing Videos of crowds were acquired from a top view in the non-confined settings described in Table  1 , in a discrete and passive way that allowed us to observe natural behavior as pedestrians were mostly unaware of being filmed. After correcting for lens deformation, the trajectories and head orientations were extracted by extensive manual tracking with the help of a dedicated Python script and a touch screen. The pixel coordinates (x , y ) are converted into realworld coordinates (X , Y ) using the following geometric relation, X = X c + γ (νx -X c ) Y = Y c + γ (νy -Y c ) where γ = 1 1-sin α• y D , α is the angle between the camera axis and the vertical direction (we systematically found α 0, reflecting the top view used in virtually all videos). Here, the (X, Y ) and (x, y) coordinates are defined in a frame that has been conventiently rotated in the horizontal plane so that one of its axes coincides with the horizontal projection of the camera axis; these coordinate systems are connected to (X , Y ) and (x , y ), respectively, by rotations in Euclidean space.
Direct comparison with the collated statistics of infections aboard Chinese trains It is generally difficult to test transmission models against empirical data related to Covid-19 cases in a statistically meaningful way because the detailed interactions between the index patient and his or her contacts are seldom known. However, Hu et al. 's study of transmission risks among train passengers in China  [4]  provides a virtually unique opportunity to attempt such a direct comparison. Indeed, Hu et al. were able to retrieve the trip record of confirmed Covid-19 patients who had traveled on a train in the 14 days before the onset of the illness, between December 2019 and late February 2020. Having access to the detailed seating plan on their train coaches, the researchers then tracked reported Covid-19 cases among co-travelers and computed the infection probability depending on the distance (expressed as a number of rows and columns) between the index patients and the contacts.
Acknowledgments We are grateful to  Isabelle Sabran  ( Ville de Lyon ),  Jessica Magraner  ( Cour d'appel de Lyon ), and  Frédéric Laurent  ( Hospices Civils de Lyon ) for facilitating our collection of data with our privacy-respective cameras and to the French  MODCOV19  initiative for supporting part of this work. We also thank  Cécile Appert-Rolland  for lending us some material and  Marina Nicolas  for proofreading. This work was funded by  Agence Nationale de la Recherche  ( ANR-20-COV1-0003 ) under project name SeparationsPietons. The setup of the CFD simulations discussed in Appendix D was designed collectively, with  P. Bénard ,  G. Lartigue ,  V. Moureau  ( CORIA Rouen, France ),  G. Balarac ,  P. Bégou  ( LEGI Grenoble, France ),  Y. Dubief  ( Univ. Vermont, USA)  and  R. Mercier  ( Safran Tech, France ).
One of these resources is habitual or dispositional optimism, a personality trait that describes the degree to which a person generally expects positive outcomes  [21, 22]. Dispositional optimism is associated with HRQoL  [23, 24]. Robust scientific data on the HRQoL of patients with breast cancer postactive treatment in Lebanon, especially those who experienced BCS, are still scarce. Only 2 single-center studies with cross-sectional designs were conducted in Lebanon  [25, 26]. Data of the first study collected from 89 participants between 2009 and 2010 showed that younger, single, and better-educated participants who were diagnosed for <30 months, who had no metastasis, and who paid <US $450 per month on medical expenses had better HRQoL. The second study conducted among 150 female patients with breast cancer diagnosed between January 2009 and March 2014 showed that participants who were Iraqi, had stage 4 disease, had a monthly household income <US $1000, or had received chemotherapy exhibited significantly lower HRQoL.
Analysis of the Primary Outcome Normally distributed continuous outcomes will be summarized as means and SDs. The rate of missing data at each HRQoL measurement time will be reported. To compare the main scores of the EORTC QLQ-C30, QLQ-BR23, EQ-5D-5L, MFI-20, and LOT across pre-and postsurgery data, a paired t test will be used. To compare possible differences between pre-and postsurgery outcomes, a one-way ANOVA will be used for quantitative variables. The factors associated with the change in HRQoL according to body image before BCS will be identified using a multiple regression model. To determine the factors potentially predicting the change in HRQoL according to body image, Pearson correlation coefficients between all the numerical variables collected at inclusion and the body image scores at baseline and after surgery will be calculated.
Analysis of the Secondary Outcomes Independent sample t tests and ANOVAs will be used to test differences in the mean scores of the main questionnaires used in this study and their subscales (EORTC QLQ-C30, QLQ-BR23, EQ-5D-5L, MFI-20, and LOT) and mean scores of demographic variables (age, residence, employment, and income) and clinical characteristics (comorbidities, BMI, and laboratory values). Pearson correlation between EORTC QLQ-C30 and EQ-5D-5L, MFI-20, and LOT scores and their subscales will be calculated to see which domain has the strongest relationship with QoL after surgery. All analyses will be calculated with their 95% CIs; statistical significance will be set at P<. 05. The collected data will be captured and analyzed using the SPSS software version 26 (IBM Corp).
Acknowledgments This work is supported by the  Agence Nationale de la Recherche  under the program Investissements d' Avenir  with reference  ANR-11-LABX-0021-LipSTIC  by the  Région Bourgogne Franche-Comté  (grant number  2019-0083 ). The authors would like to thank the  Rafik Hariri University Hospital and Sahel General Hospital  staff of the  Department of Medicine Surgery. The authors also thank  Fatima Haidar  ( Lebanese University ) for her editorial assistance.
In this setting, the COVI-DOSE trial (NCT04373707, INNOVTE F-CRIN research network), is a French multicenter randomized (1:1) open-label controlled trial that is randomizing +600 hospitalized adults with COVID-19 infection (both in the ICU and in the general ward) to weight-adjusted prophylactic dose vs. lower prophylactic dose of LMWH. Also, the multicentric Swiss COVID-HEP trial (NCT04345848) is randomizing 200 severe medical or critically-ill COVID-19 inpatients to low-dose TPX (intermediate-dose in the ICU) or therapeutic-dose TPX. The results of these trials are expected soon to help inform decision making for VTE prophylaxis of this important patient population. Interim analyses from three platform trials (ACTIV4, REMAP-CAP, and ATTACC) were recently released  [39] , and reported a lower risk of organ support and all-cause death with full-therapeutic heparin-based prophylaxis compared with standard-dose prophylaxis.
Background: The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs. Methods: Using 9280 knee magnetic resonance (MR) images (3268 patients) from the Osteoarthritis Initiative (OAI) database , we implemented a deep learning method to predict, from MR images and clinical variables including body mass index (BMI), further cartilage degradation measured by joint space narrowing at 12 months. Results: Using COR IW TSE images, our classification model achieved a ROC AUC score of 65%. On a similar task, trained radiologists obtained a ROC AUC score of 58. 7% highlighting the difficulty of the classification task. Additional analyses conducted in parallel to predict pain grade evaluated by the WOMAC pain index achieved a ROC AUC score of 72%.
A clustering method on OAI data during an 8-year follow-up concluded that only 29% of patients displayed a radiographic progression (as defined by JSN), with no further association between progression and pain worsening  [11]. In this context, the use of MRI emerges as a better quantitative endpoint recommended for assessing morphological changes in knee cartilage during OA  [12]. MRI allows the assessment of meniscal lesions such as root meniscal tears and extrusions known to be associated with OA progression  [13, 14]. It also detects other lesions predictive of pain, such as the presence of synovitis and synovial fluid effusion  [15]  or bone marrow lesions  [16]. We thus undertook the present study in support of the development of candidate DMOADs; in order to assess the feasibility of identifying future progressors of knee OA to assess whether knee MR images could predict further cartilage degradation 12 months ahead, we implemented a deep learning method using MR images to build up a predictive model for future progression of knee OA, measured by JSN 12 months after image acquisition.
Overview of OAI database The OAI database is a public multi-center longitudinal database assembled by a consortium led by the National Institutes of Health in the US to help better understand and prevent the progression of knee OA  [17]. At baseline, a total of 4796 patients had a bilateral standing knee radiograph (X-ray) and 3D knee MRI. Follow-up visits were done at 12, 24, 36, 48, 72, and 96 months (with 65% of patients enrolled at baseline having a 96 month follow-up visit). The knee MRI sequences include sagittal 3D DESS, coronal 2D IW TSE, and sagittal 2D IW TSE fat-suppressed. A detailed description of MRI sequences from the OAI database can be found in Peterfy et al. [18]. The database further contains clinical information (age, sex, body mass Index [BMI],. ), including as well results of pain assessment from WOMAC, a self-administered questionnaire encompassing for each visit up to 24 items divided into 3 subscales (i.
OAI data analysis The model was trained on N = 9280 knee MR images (2D MRI images of type "COR IW TSE"; detailed information regarding this type of MRI sequence can be found in Peterfy et al. [18]. We used data from 3268 patients (some patients had COR IW TSE knee MRI for both knees whereas others only had COR IW TSE images for a single knee). Those 9280 images were obtained in a sliding window fashion: baseline images were considered to predict month 12 JSN, month 12 images to predict month 24 JSN, and month 24 images to predict month 36 JSN. In light of some data losses (missing JSW measurements, corrupted image data), some images in the OAI database could not be used in the present study. In parallel to those computational analyses, two radiologists (one senior with more than 20 year experience in musculoskeletal imaging and one junior, resident in radiology) were given the task to assess a more restricted set of data corresponding to N = 300 baseline knee MRI images.
Image conversion and selection The OAI database exposes images in the DICOM format. In order to ease image reading and writing operations, each acquisition was converted to the NIFTI format (representing a full, three-dimensional image) by using the dcm2niix software  [23]. In order to select images containing most relevant anatomical information, a set of 8 consecutive slices around the middle slice was selected as it provides input slices which contain images of the knee while avoiding the ones with black pixels only.
Image orientation The OAI database contains images of both knees for each patient. Specifically, left knee images are RAS (right, anterior, superior)-oriented, while right knee images are LAS (left, anterior, superior)-oriented. In order to homogenize the dataset, orientations were normalized for all images. To this aim, images of right knees have been "mirrored" along the sagittal-axial plane in order to Fig. 1  Overview of the image preprocessing pipeline. The raw MR image is first re-oriented so that both left and right knees are similarly oriented. Noteworthy, only the left knee image is flipped, whereas the right is maintained as is, in order to obtain uniform orientations across the dataset. The N4 bias field correction is then applied, followed by a color normalization step look similar to images of left knees.
Architectures for 2D MRI sequences Throughout this manuscript, 2D MRI sequences refer to both COR IW TSE and SAG IW TSE images listed in the OAI database. Those images usually contain less slices than 3D sequences such as SAG 3D DESS. Taking this into account, the architecture of the deep learning model used with 2D MRI sequences slightly differs from the ones developed for 3D sequences, as detailed thereafter. Attention sub-model In light of previous studies related to multiple instance learning, we implemented a gated attention mechanism  [27]  to compute attention scores (which can be viewed as "importance" scores) for each slice of the input image. Such scores were further used in the second part of the model. Starting from a set of onedimensional feature vectors for each slice, a 1-dimensional convolution was applied (hence leading to one 2-dimensional matrix per image), followed by a gated recurrent unit (GRU) layer.
Discussion Predicting disease progression in knee OA is critical to identify patients more likely to benefit from DMOADs and further, to help selecting patients and defining treatment duration in clinical studies evaluating drug candidates  [31]. In the present study, we thus developed a weakly supervised deep learning method to build up predictive models for OA progression at 12 months from MR images. Further analyses were also conducted to predict pain grade evaluated by WOMAC from MR images and clinical data at the same visit. Using COR IW TSE images, our proposed classification model achieved a ROC AUC score of 63%, comparable to the performance of trained radiologists, obtaining a ROC AUC score of 59. 72%. To our knowledge, this is the first application of a weak supervised learning method to the prediction of knee osteoarthritis progression from MRI.
For example, whereas we used MRI as inputs for predicting an endpoint determined from knee X-ray imaging, further studies could rather use MRI criteria as endpoints of progression in clinical trials of knee osteoarthritis rather than joint space narrowing > 0. 5 mm, which is a criteria difficult to quantify reproducibly. In this regard, we investigated, in a post hoc analysis, the use of different criteria to characterize OA progression. Whereas our initial analyses have been based on "absolute" JSN (12 months) ≤ -0. 5 mm, reflecting that a knee is a "12-month OA progressor" when the minimum JSW is reduced by 0. 5 mm in the medial joint space of the knee, we reasoned that this "absolute" criteria may not be suited to knees with advanced OA. We thus considered as an alternative a "relative" criteria defined by: JSN (t + 12 months) ≤ -25% relative to JSW(t), with a threshold value chosen in order to ensure that the dataset has approximately the same class imbalance as with the "absolute" criteria.
Such studies have been carried out in the past with data from optical surveys such as the Massive Compact Halo Object project (MACHO;  Alcock et al. 2001; Zaniewski et al. 2005) , EROS-2  (Tisserand et al. 2004 (Tisserand et al. , 2008 (Tisserand et al. , 2009)) , the Catalina Survey (Lee 2015), the All Sky Automated Survey for Supernovae (ASAS-SN;  Tisserand et al. 2013; Otero et al. 2014; Shields et al. 2019) , and the Zwicky Transient Facility (ZTF;  Lee et al. 2020). In this paper, we use data from the Palomar Gattini IR (PGIR), an IR time domain facility to conduct the first NIR search for RCB stars. The PGIR  (De et al. 2020b; Moore & Kasliwal 2019 ) is a 25 deg 2 J-band camera on a 30 cm telescope located atop Mt. Palomar. The PGIR was commissioned in 2018 September and commenced survey operations in 2019 July.
The groups are listed in order of priority for spectroscopic follow-up. Figure  1  shows examples of light curves in each priority group. The priority A group is expected to be rich in RCB stars. Some objects in this group could be LPVs that show nonsinusoidal variations. The priority B, C, and D groups are also expected to contain RCB stars. Additional PGIR observations will help identify candidates with RCB-like declines or pulsations in these groups. Table  1  lists the light curve-based priorities and subpriorities for each of the 1209 candidates. All 1209 light curves are publicly available online at Zenodo:10. 5281/zenodo. 4480376.
No such campaign has been conducted in the past. As a pilot for such a campaign, we obtained NIR spectra for eight known RCB stars and 31 of our top light curve-based priority candidates. Of the 31 candidates, 26 have light-curve priority A, and five have light-curve priority B. Here we present the results from this pilot run. We used Triplespec  (Herter et al. 2008 ), a mediumresolution (R ≈ 2700) JHK-band spectrograph on the 200 inch Hale telescope at Mt. Palomar. We observed eight known RCB stars and 21 candidates on 2019 October 23 and three additional candidates on 2020 February 3. We further observed seven more candidates on 2020 June 28 with the Spex spectrograph (R ≈ 1500) on the NASA Infrared Telescope Facility (IRTF;  Rayner et al. 2003)  at Maunakea. The IRTF observations were acquired as part of program 2020A111 (PI: K.
For each RCB star, we also indicate the date when the spectrum was obtained. We note that photometry brighter than 8. 5 mag may not be reliable due to nonlinearity effects in the detector. All J-band magnitudes are in the Vega system. (T eff > 10,000 K) RCB stars. We identify C I emission at 1. 68 μm, C II emission at 1. 784 μm, Fe II emission at 1. 766 and 1. 778 μm, and, possibly, Ne I emission at 1. 7961 μm. Similar to other hot RCB stars, the emission lines are double-peaked, which could indicate the presence of an equatorial outflow around the star  (De Marco et al. 2002). Our pilot campaign has demonstrated the utility of NIR spectra in searches for RCB stars. We will continue this spectroscopic follow-up campaign in the future, aiming for completeness in the top priority classes. The NIR spectra of all RCB stars from our pilot campaign are publicly available online at Zenodo:10.
SED Modeling We searched archival data from optical, NIR, and mid-IR surveys for maximum-light photometric measurements of the RCB stars analyzed in this paper. For the optical wave bands, we used data from the AAVSO, ZTF, and ASAS-SN sky surveys. For the NIR wave bands, we compare the 2MASS J-band magnitudes to PGIR light curves to identify RCB stars for which the 2MASS measurements correspond to maximum light. For the mid-IR wave bands, we use the WISE magnitudes directly, as the mid-IR fluxes of RCB stars do not vary significantly  (Feast et al. 1997). We corrected the WISE 4. 6 μm magnitudes for saturation effects based on the prescription in  Tisserand (2012). As the distances to these stars are not known accurately (≈30% uncertainty from Gaia DR2; Bailer-Jones et al. 2018), we cannot estimate the line-of-sight extinction in their direction.
We restrict our analysis to only those stars that have integrated E B-V values smaller than 1. 5 mag. We assume 10% uncertainties on the photometric measurements to account for variations in the light curve and uncertainties in extinction. We construct maximum-light spectral energy distributions (SEDs) for six RCB stars analyzed in this paper. This includes five previously known RCB stars-AO Her, NSV 11154, ASAS-RCB-21, WISE J17+, and WISE J19+-and one new RCB, WISE ToI 1213. We model the SEDs with the radiative transfer code DUSTY  (Ivezic & Elitzur 1997; Ivezic et al. 1999; Elitzur & Ivezić 2001). In our models, DUSTY is embedded in a Markov Chain Monte Carlo wrapper using the python package emcee  (Foreman-Mackey et al. 2013). We modeled the star as a blackbody corrected for molecular absorption.
8 in steps of 0. 1. We fit the synthetic spectra to the continuum-normalized NIR spectra in the region 2. 26-2. 4 μm. For each star, we visually selected the spectra that best fit the data for each value of f dust. We used these fits to derive upper limits on the photospheric temperature and a range of O 16 /O 18 ratios,   3  irrespective of the dust contribution of the star. Figure  9  shows the best-fit synthetic spectra, together with the continuumnormalized NIR spectra. The five RCB stars WISE J17+, WISE J18+, WISE ToI 223, ASAS-RCB-21, and WISE ToI 268 show the strongest 12 C 18 O absorption features. The first three are consistent with having 16 O/ 18 O in the range 0. 5-10, while ASAS-RCB-21 and WISE ToI 268 are consistent with 16 O/ 18 O in 2-50 and 5-50, respectively (higher value for higher assumed dust contribution).
Summary and Way Forward The population of RCB stars in the Milky Way is largely unexplored; only about a third of Galactic RCB stars are currently known. A systematic census of RCB stars is necessary to shed light on the nature of these mysterious objects. Major advances in this direction were made by  Tisserand et al. (2020)  with a catalog of 2194 WISE sources expected to contain ≈85% of the RCB stars in the Milky Way. In this paper, we used data from the PGIR, a J-band time domain survey, to prioritize this catalog for spectroscopic follow-up. Of the candidates, 1209 have PGIR coverage, of which 922 are above the sensitivity of the PGIR. Based on their light curves, we assigned a "light curve-based priority" to each of the 922 candidates. The top priority comprises 149 In red, we plot all six previously available measurements of the isotope ratios in RCB stars derived from high-resolution spectra  (García-Hernández et al.
g. ,  OCO-3, Eldering et al. , 2019)  that provide more details of the CO 2 spatial distribution  (Kiel et al. 2021 ) and more frequent observations (e. g. CO2M)  (Kuhlmann et al. , 2019). Despite the pros and cons of current CO 2 satellite missions, this study focuses on the detection capability of long-term trends in urban CO 2ff emissions. To evaluate the current potential of space-based quantification techniques, we present the first attempt to monitor long-term changes in CO 2ff emissions based on OCO-2 satellite measurements over the fast-growing Asian metropolitan area of Lahore, Pakistan. After examining the OCO-2 data availability at global scale, we evaluated the data quality and its potential use for CO 2ff emission estimations over the largest cities. We then locally evaluated the OCO-2 data quality flags designed for global studies within a multi-model comparison of three top-down methods (two based on atmospheric numerical models, WRF-Chem and X-STILT, and the FCSI method) for the 25 available tracks near Lahore.
OCO-2 data and quality flags The OCO-2 satellite was launched on July 2, 2014 and started collecting data on September 4, 2014. It operates in a sun-synchronous polar orbit at an altitude of about 705 km, nominally crossing the equator at 13:36 local time. OCO-2 collects eight adjacent spatiallyresolved soundings every 0. 333 s (24 samples per second) along a narrow (0. 8 • ) swath, with a cross-track resolution of 0. 1-1. 3 km (Crisp R. Lei et al. et al. , 2010). This results in about 400 soundings per degree of latitude per orbit, or ~ 1 million soundings, collected each day over the sunlit hemisphere. However, a large fraction of the X CO2 retrievals is inadequate for inverting CO 2ff emissions, due to the data loss associated with cloud blockages, aerosol contaminations, or invalid geolocations.